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Wednesday, January 16 2019

Setting up an analysis with the help of frama-c-script

Frama-C 18 (Argon) includes a new command, frama-c-script, which contains a set of helpful scripts for setting up new analyses. The available commands will likely expand in the future. We present here a quick overview of how to use them efficiently for a very fast setup.

Presentation of frama-c-script

frama-c-script is not a Frama-C plugin, but a command that is installed along with frama-c and frama-c-gui, starting from version 18.0 (Argon). Running frama-c-script on your terminal will output its usage messsage, which includes the list of available commands (which may change in the future):

usage: frama-c-script cmd [args]

  where cmd is:

  - make-template [dir]
      Interactively prepares a template for running analysis scripts,
      writing it to [dir/GNUmakefile]. [dir] is [.] if omitted.

  - make-path
      [for Frama-C developers and advanced users without Frama-C in the path]
      Creates a file in the current working directory.

  - list-files [path/to/compile_commands.json]
      Lists all sources in the given compile_commands.json
      (defaults to './compile_commands.json' if omitted).
      Also lists files defining a 'main' function
      (heuristics-based; neither correct nor complete).

  - flamegraph <flamegraph.txt> [dir]
      Generates flamegraph.svg and flamegraph.html in [dir]
      (or in the FRAMAC_SESSION directory by default).
      Also opens it in a browser, unless variable NOGUI is set.

These commands are often related to the analysis scripts described in previous blog posts, but independent; frama-c-script facilitates the usage of analysis-scripts, but it also has commands which do not require them.

Each command is actually a script in itself, and may thus require other interpreters (e.g. Python or Perl).

Standard usage of frama-c-script

The diagram below illustrates where frama-c-script participates in the workflow of an analysis: in the creation of a makefile template, and also after the analysis, for profiling. Both usages will be detailed in this post.

Integration of frama-c-script in the analysis workflow

Currently, frama-c-script is ideally used in conjunction with a JSON compilation database. As described in a previous blog post, CMake, Build EAR or other tools can be used to produce a compile_commands.json file, containing the list of compilation commands along with their arguments.

Once you have a compile_commands.json file, run:

frama-c-script list-files

It will parse the file and list all sources in it, in a Makefile-compatible syntax. It will also output the list of files containing definitions for a main function. This typically includes the main application itself, but also test cases or secondary applications, which can all be useful entry points for an analysis with Eva.

frama-c-script does not parse the source files to find function definitions; instead, it uses regular expressions to identify likely definitions, which can give both false positives and false negatives. This is a necessary approach since, during this stage, it is too early to have a full, parsable AST to begin with.

With the list of source files, we can now easily create a Makefile template for our application. The make-template command will interactively create a new GNUmakefile in the current directory, after asking for some data:

  • Name of the main make target;
  • List of source files.

The name of the main target is usually the application name. It is also the default Makefile target and the prefix of the directories where the results will be stored (both for parsing and for the analysis with Eva).

The list of source files can be as simple as *.c, or more sophisticated for projects with subdirectories and multiple main functions, in which case some source files must not be included in the list. The list-files command will produce the list of sources when a compile_commands.json file is present.

After these values are filled in, the script will create a GNUmakefile in the current directory. The values are simply written inside the makefile to help kickstart it, but they can be manually edited afterwards.

Ideally, this should be enough to try parsing the source files by running simply make. From then on, the analysis-scripts methodology takes over, and the usual commands (make parse, make, make gui) work as usual.

Easily visualizing flamegraphs

One of the options in frama-c-script is related to flamegraphs.

Flamegraphs are stack trace visualizations which, in Frama-C/Eva, are used to help understand in which functions the analysis spends most of its time. Option -eva-flamegraph has been added in Frama-C 14 (Silicon), and it activates the generation of a flamegraph trace file during the analysis. This file can then be given to the Perl script developed by Brendan Gregg to produce an image in SVG format.

The image below provides an example of a flamegraph for the PolarSSL code in Frama-C's open-source-case-studies repository. The stacks start from the top, with the main function, and then each called function is stacked underneath. The length of the bar is the amount of time spent in it. Hovering over a bar shows the function name and the percentage of time spent in the function. Clicking on a bar will zoom on it, increasing the size of its children for better readability. Note that bar colors do not have any special meaning.

Eva flamegraph

The flamegraph command available in frama-c-script is simply a shortcut to help generate the image file for the flamegraph (along with an HTML wrapper) and open it in a browser. The script itself is distributed in the Frama-C share directory, which simplifies its usage.

Since the analysis-scripts options already include the generation of flamegraphs by default (option -eva-flamegraph is set by FRAMAC_SHARE/analysis-scripts/, the user simply needs to run:

frama-c-script flamegraph <target>.eva/flamegraph.txt

And the user's default browser should display the flamegraph generated during the analysis.

Note that flamegraphs can be generated while the analysis is still running; this is useful, for instance, when the analysis is seemingly "frozen" in a given point, without emitting any messages. Running frama-c-script flamegraph will create a flamegraph of the current state of the analysis, while the trace file is still updated by the ongoing analysis.


The features currently available in frama-c-script offer extra comfort when setting up new analyses, by minimizing the amount of repetitive or hard-to-memorize tasks.

list-files requires a JSON compilation database, but it helps obtain the information for creating a GNUmakefile. make-template creates a GNUmakefile which can be manually edited later. flamegraph helps visualize the performance of the analysis, either while it is running, or afterwards.

One of the new commands expected for Frama-C 19 (Potassium) will help find which files in a directory declare and/or define a given function. This will help, for instance, when trying to identify which .c file to include to obtain the body of a function that is called from another source.

Friday, December 14 2018

New loop unroll annotation in Frama-C 18

One of the new features in Frama-C 18 (Argon) is the annotation @loop unroll, used by the Eva plug-in to replace and improve upon the usage of slevel for semantically unrolling loops. This new annotation is more stable, predictable, and overall more efficient. In this post we present the annotation with some examples where it outperforms previous mechanisms.

Loop unrolling mechanisms in Frama-C/Eva

Frama-C has two ways to unroll loops syntactically: either via option -ulevel N (which unrolls all loops in the program N times), or via the loop pragma UNROLL annotation, placed before a loop, which unrolls only that loop. Both have their uses, especially for plug-ins other than Eva; however, Eva has specific mechanisms which are almost always better suited to deal with loops.

When using the Eva plug-in, syntactic loop unrolling is is not recommended: it is more costly than semantic unrolling, and it makes the code harder to read in the GUI.

In Eva, the slevel mechanism is used to improve precision of the analysis inside loops: as long as there is enough slevel to be consumed, each loop iteration will keep consuming it before performing a widening and merging states, which might lead to loss of precision.

This works fine for simple loops, but as soon as there are branches or nested loops, slevel consumption is hard to control: each branch will also consume some slevel, and there is no way to ensure that only outer loop iterations will consume the remaining slevel.

To mitigate the effects of unwanted slevel consumption, options such as -eva-slevel-merge-after-loop force the merging of states at the end of each loop iteration, which minimizes the issue with loops containing branches. However, this option operates at the function level, so nested loops are still an issue.

Plug-ins such as Loop Analysis perform complex estimations to try and indicate values of slevel for each function, accounting for the fact that nested loops and branches require extra slevel. In realistic code bases, this often leads to functions having excessively large values of slevel (in the worst case, such overflows lead to 0, that is, giving up unrolling).

In the end, existing solutions had drawbacks which imposed an extra burden on the parameter refinement process. This motivated the development of the new @loop unroll annotation.

The new loop unroll mechanism

@loop unroll N is a code annotation, to be placed just before a loop in the program (as is the case with all @loop ACSL annotations). It will instruct Eva to avoid merging states after each loop iteration, until either:

  • there are no more states to unroll (i.e. the loop has been completely unrolled), or

  • N unrollings have been performed.

Just like with slevel, a value for N larger than the required to completely unroll the loop will not lead to wasted time.

The annotation currently accepts only constant expressions, that is, those involving literal constants or #define macros. This limitation may be removed in the future.

Nested loops and slevel usage

The main advantage of using @loop unroll is in the presence of nested loops. Consider the following example:

void main() {
  int a[10][10] = {0};
  for (int i = 0; i < 10; i++) {
    for (int j = 0; j < 10; j++) {
      a[i][j] += i + j;

In this program, one might naively think that using -eva-slevel 100 would be enough to unroll the loops. This will however result in:

[eva] example1.c:4: starting to merge loop iterations
[eva:alarm] example1.c:5: Warning:
  signed overflow. assert a[i][j] + (int)(i + j) ≤ 2147483647;
[eva:final-states] Values at end of function main:
   [8][9] ∈ {17}
   [9][0] ∈ {9}
   [9][1..9] ∈ [0..2147483647]

The first message indicates that there was not enough slevel to consume during the analysis, and thus a merge between different loop iterations happened, resulting in loss of precision. Widening causes the last cells of the matrix to contain over-approximated intervals, leading to an alarm.

The reason why 100 is not enough is due to the fact that an extra slevel is consumed for each iteration of the outer loop; for instance, you can try using -eva-slevel 10 and then -eva-slevel 11 and checking the displayed final states: in the first case, the result remains imprecise for the entire matrix:

[eva:final-states] Values at end of function main:
  a[0..9][0..9] ∈ [0..2147483647]

While in the second case (-eva-slevel 11), we have:

[eva:final-states] Values at end of function main:
  a[0][0] ∈ {0}
   [0][1] ∈ {1}
   [0][2] ∈ {2}
   [0][3] ∈ {3}
   [0][4] ∈ {4}
   [0][5] ∈ {5}
   [0][6] ∈ {6}
   [0][7] ∈ {7}
   [0][8] ∈ {8}
   [0][9] ∈ {9}
   [1..9][0..9] ∈ [0..2147483647]

Thus, the actual slevel required to completely unroll both loops is 109 (the last iteration does not require extra slevel). Computing this exact value is not straightforward, and it gets even more complicated when there are branches in the loop.

Using @loop unroll in nested loops

The @loop unroll annotation is local to a loop, and thus completely independent of whether the loop contains or is contained by other loops. Therefore, we can perform simple calculations to obtain the bounds: (max - min) / step in the general case, which here results in (10 - 0) / 1 = 10.

void main() {
  int a[10][10] = {0};
  //@ loop unroll 10;
  for (int i = 0; i < 10; i++) {
    //@ loop unroll 10;
    for (int j = 0; j < 10; j++) {
      a[i][j] += i + j;

With the above annotations, simply running Eva, without any slevel required, will result in no alarms at all. The bounds are much simpler to compute. In future Frama-C releases, we hope to obtain this information automatically whenever possible, so the user will not have to add them in simple cases.

There is currently (in Frama-C 18 - Argon) an option to set a minimum value for @loop unroll for all loops in the program: -eva-min-loop-unroll. This may be unwise for realistic case studies with several loops, but in many cases this saves time by not having to add annotations to each loop in the program. In our first example, adding -eva-min-loop-unroll 10 has the same effect as adding the two //@ loop unroll 10; annotations.

Loops with branches and slevel usage

The previous example was comprised of two very simple nested loops. In reality, many loops contain branches and conditional exits. In such cases, slevel usage becomes exponential. For instance, consider this variant of the previous example, with a non-deterministic increment:

void main(int nondet) {
  int a[10][10] = {0};
  for (int i = 0; i < 10; i++) {
    for (int j = 0; j < 10; j++) {
      a[i][j] += i + j;
      if (nondet) a[i][j]++;

In this example, instead of 109 as minimum slevel to avoid any warnings, Eva needs 5509. On my machine, the analysis goes from nearly instantaneous (about 0.3s) to visibly perceptible (about 1.7s). And all this for a very modestly-sized loop, with a single branching point.

Eva has an option to help mitigate the issue with slevel: -eva-slevel-merge-after-loop <functions>. This option ensures that, at the end of each loop iteration, all states from different branches in the current iteration will be merged. This avoids the exponential explosion, but still requires extra slevel, in a non-intuitive manner; for instance, the minimum slevel value required to avoid warnings in the above example is 208. Simply trying to understand precisely why this value is necessary and sufficient requires deep understanding of the evaluation mechanism inside Eva.

Again, if we add the same @loop unroll annotations as before, we do not need anything else: the analysis will not generate any warnings, it will run almost as fast as before (with a barely perceptible increase in time due to the branching), and it will be robust to changes: the introduction of extra branches or nested loops will not affect the existing annotations.

Warnings to help the user

The message starting to merge loop iterations is used to indicate insufficient slevel during the analysis of loops. A more specific warning is now available for loop unroll annotations. For instance, suppose we used 9 instead of 10 in our previous annotations. Eva would then report:

[eva:loop-unrolling] example2.c:6: loop not completely unrolled

This warning appears when there is an annotation that is insufficient to completely unroll the loop, which is often a sign that the annotation should contain a larger value. It can be disabled or even turned into an error, to make sure the user will not miss it.

Upgrading to the new mechanism

Existing case studies may benefit from the new annotation without requiring changes to existing parametrization; in most cases, keeping current slevel values (which are still necessary for dealing with branches) while adding such annotations should give results at least as precise, possibly improving the efficiency of the analysis. Then, slevel values can be lowered on a second stage.

The analysis scripts and sources in the Github open-source-case-studies repository have been updated to include several loop unroll annotations throughout the code, to serve as usage examples. During the process, we could confirm their improved reliability, predictability and ease of use. Their only downsides are that some oracles change due to different line numbers after they are introduced, and the fact that some (if not most) of these annotations could be automatically generated.


The slevel mechanism is very generic, but not appropriate in all situations, especially because it does not allow fine-grained control, and is context-dependent. Having a specific mechanism for handling loops, which is the majority of practical use cases, improves reliability and predictability of the analysis. It also prevents exponential blowup of states in more complex loops.

loop unroll annotations are very useful and likely to be present in every meaningful case study. Their usage is straightforward, and future Frama-C versions may end up incorporating more flexible annotations (e.g. using non-constant expressions). The slevel mechanism is still used in some cases, especially for branching, but its usage is likely to diminish over time, replaced with more specialized and expressive mechanisms.

Thursday, November 15 2018

Frama-C/Eva in SATE VI with Juliet 1.3

Frama-C chose to participate in the SATE VI Ockham Sound Analysis Criteria. With the upcoming Frama-C 18 (Argon) release, results can be reproduced using the open source version of the Eva plug-in. This post briefly presents SATE, the Juliet test suite, and the results obtained with Frama-C 18. We then describe the Frama-C SATE VI repository on Github, created to allow anyone to reproduce the results obtained with Frama-C/Eva. We also explain some patches that were applied to the test suites, and our choice of parametrization options for Eva.

SATE VI and Juliet 1.3

SATE (Static Analysis Tool Exposition), organized by NIST, is a comparative study of static analysis tools: toolmakers volunteer to participate by running their tools on the sets provided by NIST, and then by submitting their analysis scripts and results. The NIST SAMATE team then compiles a dense report, comparing and evaluating all participating tools.

For the analysis of C programs, there are two tracks: one in which all kinds of tools participate, called the Classic track, and another dedicated to sound analyzers, in the sense of the Ockham Sound Analysis Criteria.

For the Ockham track in SATE VI, we chose the Juliet 1.3 test suite for C/C++, which contains over 64,000 test cases. Some are written in C++, but most are written in C. Test suites are organized in directories related to CWEs (Common Weakness Enumerations): each directory has code samples containing violations of the CWE. For each test case, there are two variants: one with good code (which does not trigger the CWE), and another with bad code (which triggers the CWE in question). In our tests using Juliet, Frama-C is supposed to find all occurrences of bad code; it may, however, report some good code as bad.

CWEs handled by Frama-C/Eva

For the analysis with Frama-C/Eva, we chose the subset of CWEs in Juliet 1.3 for C/C++ which are handled by the analyzer. Click the arrow below to unfold the list.

CWEs in Juliet 1.3 handled by Frama-C/Eva (18 - Argon)
  • CWE121 - Stack Based Buffer Overflow
  • CWE122 - Heap Based Buffer Overflow
  • CWE123 - Write What Where Condition
  • CWE124 - Buffer Underwrite
  • CWE126 - Buffer Overread
  • CWE127 - Buffer Underread
  • CWE190 - Integer Overflow
  • CWE191 - Integer Underflow
  • CWE194 - Unexpected Sign Extension
  • CWE195 - Signed to Unsigned Conversion Error
  • CWE196 - Unsigned to Signed Conversion Error
  • CWE197 - Numeric Truncation Error
  • CWE369 - Divide by Zero
  • CWE415 - Double Free
  • CWE416 - Use After Free
  • CWE457 - Use of Uninitialized Variable
  • CWE464 - Addition of Data Structure Sentinel
  • CWE469 - Use of Pointer Subtraction to Determine Size
  • CWE475 - Undefined Behavior for Input to API
  • CWE562 - Return of Stack Variable Address
  • CWE587 - Assignment of Fixed Address to Pointer
  • CWE588 - Attempt to Access Child of Non Structure Pointer
  • CWE590 - Free Memory Not on Heap
  • CWE617 - Reachable Assertion
  • CWE665 - Improper Initialization
  • CWE690 - NULL Deref From Return
  • CWE758 - Undefined Behavior
  • CWE835 - Infinite Loop
  • CWE843 - Type Confusion

Note that some CWEs are very broad, such as CWE758 - Undefined Behavior. We do not claim Frama-C/Eva handles all cases which might be categorized under such CWEs; however, those considered in the Juliet 1.3 test suite have all their bad code samples correctly labeled.

Some examples of CWEs which are not considered by Frama-C/Eva include CWE426 - Untrusted Search Path, CWE90 - LDAP Injection and CWE780 - Use of RSA Algorithm Without OAEP. All three of them constitute functional-level weaknesses which do not cause runtime errors.

Some of the CWEs not currently considered by Frama-C/Eva could be handled via specific function annotations, but this would constitute a very ad hoc solution. Other plugins in the Frama-C platform might be better suited for these CWEs; e.g., using the WP plugin with function annotations, or the Secure Flow plugin with trusted and untrusted sinks and sources, might allow identifying the cases with API violations leading to weaknesses.


The following table summarizes the results obtained with Frama-C 18 (Argon). The script that produces this table is presented in a later section.

total imprecise      :    751
total non-terminating:      3
total ok             :  44668
total timeout        :      0
total unsound        :      0
precision (excluding non-terminating): 0.983465

Each test case is labeled as one of the following cases:

  • ok: correctly labeled: either a good test without alarms, or a bad test with alarms;
  • imprecise: a good test with a false alarm;
  • unsound: a bad test without an alarm;
  • non-terminating: the program does not terminate; this should never happen, except in the case of CWE835 - Infinite Loop;
  • timeout: a test which did not finish within the allotted time (by default, 30 seconds). This never happens in our benchmarking rig, but slower or overloaded machines may see this happen. If so, then a larger timeout should be given and the test should be re-run.

The precision (98.3%) displayed in the last line is the number of ok tests divided by the total number of tests (ok + imprecise + unsound).

The improvements between Frama-C 17 (Chlorine) and 18 (Argon) are noteworthy, especially the fact that the memcpy and memset builtins are now released in the open source version. Other improvements in precision also contributed to remove some spurious warnings and to simplify the analysis script.

The remaining imprecisions are due to different situations, but most of them occur several times, due to reuse of the same patterns among different test cases. One source of imprecision is the snprintf function, whose specification is not precise enough to ensure that the output string is null-terminated. This leads to false alarms when that string is used later in the code. Another case is related to CWE369 - Divide by Zero, in which some tests use socket-related functions whose specifications do not allow for precise results in every case.

The Frama-C SATE VI Repository

The Frama-C SATE VI repository on Github allows anyone to run Frama-C on the Juliet 1.3 test suite for C/C++, to reproduce the results or to try different approaches, e.g. parametrizing the analysis differently, or trying a new abstract domain.

Software requirements are: Frama-C 18.0 (Argon), GNU Make (>= 4.0), the GNU parallel tool, Bash (>= 4.0) and wget (to download the Juliet archive from NIST's website).

The repository is structured in the following way:

  • A GNUmakefile allows bootstrapping the repository, by downloading the Juliet 1.3 for C/C++ archive from NIST's website and then decompressing it (Juliet 1.3 is not distributed in our repository). It also applies some patches to Juliet's sources, described in a later section;
  • the fc directory contains Frama-C scripts used to run the analysis and automatically evaluate its results. The main scripts are:
    • sets the default analysis parameters; these differ from Frama-C's defaults in several ways, being stricter in some cases but less strict in others;
    • performs automatic evaluation of good and bad cases into the categories presented before;
  • the fc/patches directory contains some patches applied to Juliet 1.3 to fix known issues in a few tests. It also changes the way non-deterministic random numbers are used in some tests (details are presented later);
  • the C directory contains the extracted Juliet 1.3 archive, with its scripts and test cases;
  • finally, the oracles directory contains a versioned copy of the oracles produced by the analysis with Frama-C/Eva; this allows comparing different parametrizations, and following the evolution of the platform.

Reproducing the results

The overall process to use the repository is the following:

git clone
cd C/testcases

The last make step takes a very long time (2+ hours, depending on your CPU and number of cores).

Note that, if Frama-C is not installed in the PATH, you can use the fc/ script to specify the directory where Frama-C is installed. This is especially useful to compare different versions of Frama-C.

After running make inside the C/testcases directory, a small summary with the number of tests performed in each subdirectory will be presented in textual form:

CWE121_Stack_Based_Buffer_Overflow/s01: 876 tests
    876  ok
CWE121_Stack_Based_Buffer_Overflow/s02: 1020 tests
   1020  ok
CWE121_Stack_Based_Buffer_Overflow/s03: 1020 tests
     34  imprecise
    986  ok

Running ./ will also output this result. The script will produce an overall summary across all subdirectories, as presented in the Results section.

Re-running individual tests

Inside each directory in C/testcases/, it is possible to run a single test, by removing its extension and adding _good.res or _bad.res, e.g. if there is a file file.c inside directory CWE000/s01, then doing cd CWE000/s01 and make file_good.res will run the test and produce three files:

  • file_good: output of the analysis;
  • file_good.csv: CSV report of warnings produced during the analysis;
  • file_good.res: result of applied to the test case report;

Note that for tests consisting of multiple files (each ending with a, b, c, etc.), the test case name is their prefix without the letter, e.g. for the test comprised of files file_42a.c and file_42b.c, the command is make file_42_good.res (or bad).

Finally, by replacing .res with .gui in the make command, it is possible to open the Frama-C GUI on the test case, to inspect it.

Patches applied to Juliet 1.3

In the Frama-C SATE VI repository, there are two sets of patches applied to Juliet 1.3 for C/C++:


These are fixes in a small number of tests, due to 2 patterns which were found out to lead to undefined behavior.

The first fix consists in replacing an overflow check which accidentally introduces a signed overflow:

 /* FIX: Add a check to prevent an overflow from occurring */
-if (abs((long)data) <= (long)sqrt((double)CHAR_MAX))
+if (data <= LONG_MAX && labs(data) <= (long)sqrt((double)CHAR_MAX))

data has type unsigned int. In the original code, the call to abs((long)data) is equivalent to abs((int)(long)data), since abs takes an int as argument. If int and long have the same length (e.g. as in x86 32-bit), then data can be larger than LONG_MAX (and also INT_MAX, since they have the same length), leading to a signed overflow in the call, and thus an undefined behavior. The patched version first checks that data is small enough, and then uses labs to avoid downcasting data.

The second fix concerns a set of files which had a local variable declared in a scope smaller than intended:

 static void goodG2B()
     void * data;
+    int intBuffer = 8;
     /* Initialize data */
     data = NULL;
     /* FIX: Point data to an int */
-        int intBuffer = 8;
         data = &intBuffer;
     CWE843_Type_Confusion__short_68_goodG2BData = data;

In this set of test cases, intBuffer (and similar variables) was declared inside a block, and the value was later used outside of the scope of the variable, leading to undefined behavior. The fix consists in declaring the variable in the main scope of the function.


This concerns mainly the RAND32 macro, used in Juliet to produce non-deterministic numbers. This macro is used in some test cases to assign values to variables of different types. The result is downcast to char or short whenever necessary. However, this downcast leads to warnings due to usage of option -warn-signed-downcast. This option is enabled in the script since some CWEs are related to it.

The patches applied to the test cases consist in replacing calls to (char)RAND32() and (short)RAND32() with RAND8() and RAND16(), two new macros defined in a way that prevents overflows from happening. These macros are defined in a way very similar to the existing RAND32() and RAND64() macros, but avoiding overflows triggering downcast warnings. To preserve existing behavior, these macros are defined to RAND32() when not running Frama-C.

Finally, minor patches were applied to pretty-printing functions in io.c, such as:

void printHexCharLine (char charHex)
-    printf("%02x\n", charHex);
+    printf("%02hhx\n", charHex);

The printf modifier for a variable of type char is hh, although this is very rarely used in practice. Still, the Variadic plug-in warns about it, so fixing the test case support file prevents spurious warnings.

Note that all of these patches are intended to improve the precision of the analysis, but do not affect its correction: not applying these patches will lead Frama-C/Eva to produce several spurious warnings (and, in a few cases, such warnings will lead to non-termination, when the only possible behavior is undefined), but their absence will not make Frama-C's results unsound; bad cases will still be reported as such.

Rationale for analysis options

The script contains a series of non-default options used in the analysis. We present here the rationale for choosing these options during the analysis of Juliet 1.3. Similar options are grouped together for presentation purposes.

-no-autoload-plugins -load-module from,inout,report,scope,eva,variadic

These options are present in most of Frama-C's test suites. The -no-autoload-plugins option simply disables by default all plugins, which speeds up initialization of Frama-C. We then only load the plug-ins which are needed for Juliet: eva and its derived plugins (from, inout, scope), report for the CSV report, and variadic to handle variadic functions present in almost every test (e.g. printf). Overall, this is an optimization for running thousands of tests; when running only a few dozens of tests, the difference is negligible.

-kernel-warn-key parser:decimal-float=inactive \
-kernel-warn-key typing:no-proto=inactive \
-kernel-warn-key typing:implicit-conv-void-ptr=inactive \
-eva-warn-key locals-escaping=inactive

These options disable warnings which are not directly related to runtime errors, but are enabled by default since they often indicate code quality issues in real code bases. The first three warnings could be avoided by patching the code (e.g. adding prototypes, explicit casts, and avoiding inexact floating-point constants); the latter is related to addresses of variables remaining accessible out of their scopes (in Eva, their value becomes ESCAPINGADDR), and it is emitted since it is often easier to identify the moment where the variable becomes out of scope, rather than the later time at which it may be used. This is not an undefined behavior, unless the variables are actually used (which does not happen in the good test cases). In any case, when the values are accessed (as in the bad test cases), a different warning is emitted.

-eva-msg-key=-initial-state \
-eva-no-show-progress \
-eva-print-callstacks \

These are all options related to minimizing the default verbosity of the analysis, except for the last, which adds callstacks to the output: when there are warnings, it is better to know where they come from.

-slevel 300 \

Some amount of slevel is useful to improve precision of the analysis. The chosen value is arbitrary, and variable according to the characteristics of the code under analysis. In the case of Juliet 1.3, this value is a good compromise between precision and speed of the analysis.

-eva-builtin alloca:Frama_C_vla_alloc_by_stack \

The alloca function (which is not in the C standard) is used in several test cases. In all of them, it behaves just like the allocation of a VLA (variable length array), that is, the variables are not used outside of the scope in which alloca was called1 . It is therefore safe to associate the vla_alloc builtin to calls to the alloca function in Juliet 1.3.

1 The main difference between alloca and the allocation of a VLA is that the latter is deallocated at the end of the scope in which it was created, while alloca memory blocks are deallocated at the end of the function in which they are called.

-warn-special-float none \

By default, Frama-C considers non-finite floating-point values as forbidden, emitting warnings when they may arise, even though this does not constitute an undefined behavior. This option allows non-finite floating-point values, thus avoiding spurious warnings.

-warn-signed-downcast \
-warn-unsigned-overflow \

Signed downcasts (possible loss of information when converting a larger type to a smaller one) and unsigned overflows, by default, do not constitute undefined behavior; however, it is a good practice to avoid them, and some specific CWEs do require treating them as undesired. Another option that could have been activated is -eva-symbolic-locations-domain, but in Juliet 1.3 it does not make any difference, neither in the oracles nor in the overall analysis time.

-eva-warn-copy-indeterminate=-@all \

By default, copying a possibly indeterminate value (e.g. uninitialized) in Eva triggers a warning, even if the value is copied in a standards-compliant way (e.g. via unsigned char pointers). This helps detect problems earlier in the code, rather than when used. However, the usage of such values already triggers warnings anyway, so we can afford to be less strict, avoiding spurious warnings in good test cases where the copied values are not used later.

-eva-equality-domain \
-eva-sign-domain \

These two abstract domains are efficient enough to be used in most analyses, and improve the precision of the results at a minimal cost. The equality domain in particular is useful in some tests which require relational information, while the sign domain is well suited for testing divide-by-zero errors. The equality domain eliminates false alarms in 184 tests, and the sign domain removes 308 false alarms.


This is a technicality, but useful for scripts and versioning: by default, Frama-C uses relative paths when they are "sufficiently close" to the current working directory, otherwise absolute paths are used (e.g. when there is a .. in the path). To avoid versioning such paths (and also for creating test oracles), Frama-C has a mechanism to replace specific paths with user-defined strings. The most common use case is Frama-C's share directory, written as FRAMAC_SHARE in reports and in the GUI. In this case, the actual path to the C/testcasesupport directory is replaced with string TESTCASESUPPORT_DIR. This ensures every user should have the same oracles for the CSV files mentioned below.

-report-no-proven -report-csv <file>.csv

These options are not present in, but in, due to availability of the output filename. These options invoke the Report plug-in to generate a CSV file containing the list of properties in the program, along with their statuses. Option -report-no-proven filters only the properties which are left Unknown or Invalid, which are the ones we are interested in when looking for undefined behaviors.


The NIST SAMATE Juliet 1.3 test suite for C/C++ provides an excellent set of well-defined programs, as well as programs with isolated bugs, which are very useful for automated testing and comparative evaluation.

Frama-C/Eva can handle several of the CWEs present in the test suite, and recent improvements in the analyzer allow it to obtain a very high precision. The tests cases are small, which does not make them representative of large case studies, but they allowed the identification of limitations in the analyzer and offered opportunities for improvement.

The analysis with Frama-C/Eva allowed the identification of very few issues with the code, which given the size of the test suites is an indicator of its high quality.

Overall, participation in the Ockham track enabled Frama-C/Eva to improve its analysis, and offered a very extensive set of test cases for regression testing and quantitative experimentation with different parametrizations. For instance, the sign domain, developed originally as part of a tutorial for Eva, proved itself very useful to improve the precision of several tests related to CWE369 - Divide by Zero, with a minimal runtime cost. The large-scale testing afforded by Juliet enables such kinds of evaluation.

We would like to thank NIST for preparing Juliet and organizing SATE, and for their commitment to the exposition of innovative tools for program analysis and verification.

Tuesday, June 19 2018

Analyzing Chrony with Frama-C/Eva

Chrony is an implementation of NTP which is C99-compatible, with portable code, and thus a good candidate for an analysis with tools such as Frama-C.

As part of an effort sponsored by Orolia, researchers from the List, CEA Tech laboratory applied Frama-C/Eva on the Chrony source code, in an attempt to verify the absence of run-time errors. This post summarizes some of the findings and links to the full report, in PDF format.

Scope of the analysis

The analysis was performed on Chrony 3.2.

Some parts of the code were disabled via the configure scripts, namely IPV6, timestamping and readline. The idea is to minimize the amount of non-POSIX code, in hopes of improving the likelihood that external functions will have a specification in Frama-C's stdlib. Reenabling those features requires only writing additional stubs/specifications.

The entrypoint used for the analysis was the main function in test/unit/ntp_core.c, with a generalized state for argc and argv, to include possible behaviors from arbitrary command line arguments.

The Eva plug-in was iteratively parametrized to improve coverage and minimize the number of alarms, while maintaining a short execution time. Reported alarms include possible buffer overflows, uninitialized reads, and other undefined behaviors, as listed in the Eva plug-in user manual.

The analysis identified a few issues, but the overall impression was that code quality was high w.r.t. the C standard and the presence of some defensive programming patterns. However, there are still several potential alarms that need to be further investigated to ensure the absence of run-time errors.

The full report is available here:

Report: Frama-C/Eva applied to the Chrony source code: a first analysis (PDF)

Do not hesitate to contact us if you have suggestions, remarks, patches, etc. You can use the Frama-C mailing list or Github's issues page on open-source-case-studies.

Thursday, January 25 2018

Analysis scripts: helping automate case studies, part 1

(kindly reviewed by T. Antignac, D. Bühler and F. Kirchner)

Among Frama-C 16 Sulfur's new features, one of them is dedicated to help setting up and iterating case studies. In this series of posts, we will explain how to use such scripts to save time when starting a new case study with EVA.

This series supposes some familiarity with EVA, as in, its basic usage is not detailed. Reading A simple EVA tutorial or the tutorial in the EVA user manual should be sufficient.

In this first part, we will explore the basics of the analysis-scripts framework and its use on an open source project. We will see how to get the preprocessor flags needed to prepare an analysis template. This template is in fact a Makefile which will be used to set up the analysis. It will enable us to define the sources to parse by editing its targets.

In the next post, we will see that, once the set-up is done, the analysis steps are similar to the ones presented in previous EVA tutorials: run a simple analysis, then refine, improve the precision, iterate. Once an analysis has been run, we will see how to track red alarms to improve the coverage. This will lead to some syntactic fixes that will allow the analysis to go further.

Analysis scripts

The release 16 (Sulfur) of Frama-C includes a small set of files, called analysis scripts, whose purpose is to help starting new analyses with Frama-C. It is currently focused on analyses with EVA, but other plug-ins such as WP and E-ACSL may incorporate it as well.

The new files are available in the analysis-scripts of Frama-C's share directory. Use frama-c -print-share-path to obtain its exact location. This directory currently contains a README, two small Bash scripts and a Makefile ( which contains the bulk of it.

Note: the mentions fcscripts, the old name of this set of files (it was already being used by the open-source-case-studies Git repository, under that name, but now it is part of the Frama-C release). The upcoming Frama-C release will update that.

analysis-scripts relies on some GNU-specific, Make 4.0+ features. This version of Make (or a more recent one) is already available on most Linux distributions and on Homebrew for MacOS, but in the worst case, downloading and compiling GNU Make from source should be an option.

Basically, what these files provide is a set of semi-generated Makefile targets and rules that automate most of the steps typically used by Frama-C/EVA developers to set up a new case study. The scripts themselves are extensively used in the open-source-case-studies repository, which serves as use example.

Using analysis scripts

To illustrate the use of analysis-scripts, we picked one project from ffaraz's awesome-cpp repository: GHamrouni's Recommender, a library from the Machine Learning section.

We start by cloning the repository1:

git clone

1 Note: at the time of this posting, the latest commit is 649fbfc. The results presented later in this tutorial may differ in the future. You can select this commit via git checkout 649fbfc in case this happens.

Then, inside the newly-created Recommender directory, we look for its build instructions. Many C open-source libraries and tools are based on autoconf/configure/make, which may require running some commands before all headers are available (e.g., ./configure often produces a config.h file from a template). Frama-C does not require compiling the sources, so in most cases you can stop before running make. However, since Frama-C does require preprocessor flags, you can use existing Makefiles to cheaply obtain that information.

Obtaining preprocessor flags from a compile_commands.json

In order to find out which preprocessor flags are needed for a given program, you can use tools such as CMake or Build EAR to produce a JSON compilation database, commonly called compile_commands.json. This is a JSON file which contains the list of commands (including all of their arguments) to be given to the compiler. It contains many options which are irrelevant to Frama-C (such as warning and optimization flags, typically -W and -O), but it also contains preprocessor flags, mostly -I and -D, which we are interested in.

The compile_commands.json file can be produced as follows:

  1. If the project is based on CMake, add -DCMAKE_EXPORT_COMPILE_COMMANDS=ON to the cmake command-line, e.g. instead of cmake <source dir>, use cmake <source dir> -DCMAKE_EXPORT_COMPILE_COMMANDS=ON.

  2. If the project is based on Makefile, use Build EAR. After Build EAR is installed, you just have to prefix make with bear: typing bear make is usually enough to obtain the JSON database.

Note: Frama-C 17 (Chlorine) includes option -json-compilation-database, which allows using compile_commands.json directly, rendering the next step unnecessary.

Once you have the file, simply grep it for -I and -D flags, e.g:

grep '\-I\|\-D' compile_commands.json

Those flags should be added to the preprocessor flags in Frama-C's Makefile (described in the next section), CPPFLAGS.

Note that these recommendations may not work in complex setups. Manual inspection of the commands used during compilation might be necessary to obtain all necessary flags.

Preparing an analysis template

We will create a Makefile for Frama-C, to manage dependencies and help re-run analyses. In order to avoid having to type make -f <name> each time, we will name it GNUmakefile, for the following reasons:

  1. GNU Make gives preference to GNUmakefile over Makefile if both exist, so the default file used when typing make will be ours, even if the project already has its own Makefile;

  2. This avoids having to rename/overwrite existing makefiles (or, worse, having Frama-C's Makefile erased when re-running ./configure);

  3. The analysis-scripts Makefile already relies on some features specific to GNU Make, so there is no compatibility downside here.

If you want to name your Makefile otherwise, just remember to always add -f <your makefile name> to the make commands presented in this tutorial.

Our GNUmakefile will be created with content based on the template available on Frama-C's Github repository.

In this tutorial, we consider that Frama-C is installed and in the PATH to keep the template concise.

include $(shell frama-c-config -print-share-path)/analysis-scripts/

# Global parameters
FCFLAGS     +=


# Default targets
all: main.eva

# Input files
main.parse: <TO BE COMPLETED>

The essential element to complete this template is the list of files to be parsed. Other arguments, such as flags for the C preprocessor (CPPFLAGS), for the Frama-C kernel (FCFLAGS) and for the EVA plug-in (EVAFLAGS) are also to be filled by the user, when necessary.

Finally, note that the target name (main) is completely arbitrary. You can have multiple targets if your code contains multiple main functions. The important part is the use of the suffixes .parse and .eva, which are hard-coded in the analysis-scripts' Makefile to generate targets with the appropriate dependencies and rules.

The .parse suffix is used by analysis-scripts to set the list of source files to be parsed. It is associated to an .eva target which runs the EVA analysis. This target is generated by analysis-scripts itself; we just need to tell the Makefile that it should be run when we run make all, or simply make, since all is the first rule in our Makefile.

Setting sources and testing parsing

The list of source files to be given to Frama-C can be obtained from the compile_commands.json file. However, it is often the case that the software under analysis contains several binaries, each requiring a different set of sources. The JSON compilation database does not map the sources used to produce each binary, so it is not always possible to entirely automate the process. You may have to manually adjust the sets of files to be given to Frama-C. For a whole-program analysis with EVA, in particular, you might want to ensure that there is exactly one file defining a main function.

In the case of Recommender, since it is a library, the src directory does not contain a main function. However, the test directory contains two files, each of them defining a main function. In this tutorial, we will use the test.c file as the main entry point of the analysis. We could also have used the -lib-entry option on one or more functions in the library. More advanced users of Frama-C may prefer this option though we will keep it simple and use the main function in test.c as unique entry point in this tutorial.

Therefore, the list of sources to be filled in the main.parse target is the following:

main.parse: src/*.c test/test.c

We can test that Frama-C is able to parse the sources:

make main.parse

If you are strictly following this tutorial, you should have the following error:

[kernel] Parsing test/test.c (with preprocessing)
test/test.c:1:25: fatal error: recommender.h: No such file or directory
 #include "recommender.h"
compilation terminated.

This is because we never included the actual -I lines that we found in the compile_commands.json file. Note that they include flag -I../../src/, which is relative to one of the subdirectories in tools/*. Since our Makefile (and thus Frama-C) will run relative to the base directory in Recommender, the actual include directive needs to be -Isrc, which we add to CPPFLAGS:


Running make main.parse now should succeed. Run it again. You will notice that nothing is done: thanks to the dependencies managed by analysis-scripts, unless you modify one of the source files, or the flags given to Frama-C (CPPFLAGS or FCFLAGS), Frama-C will not waste time reparsing the results: they have already been saved inside the main.parse directory.

Contents of the .parse directory

For each .parse target, analysis-scripts will create a corresponding directory with several files:

  • command: the command-line arguments used by Frama-C;

  • framac.ast: the pretty-printed normalized AST produced by Frama-C;

  • stats.txt: performance information (user time and memory consumption);

  • warnings.log: warnings emitted during parsing;

  • framac.sav: a Frama-C save file (a binary) with the result of parsing;

  • parse.log: the entire log of the parsing (includes the warnings in warnings.log).

All of these files are used internally by the analysis scripts or some other tools (notably for regression testing and profiling purposes), however only the last 2 files are occasionally relevant for the end user:

  • framac.sav can be used with frama-c -load, for instance when trying different plug-ins;

  • parse.log contains a copy of all messages emitted during parsing.

If we want to load the parsed files in the Frama-C GUI, we can use either frama-c -load framac.sav, or more conveniently, make main.parse.gui. The advantage of the latter is that it will generate the .parse directory if it has not been done already.

This concludes the first part of this tutorial. In the next post, we will run EVA using our Makefile, then iterate to improve the analysis.

Tuesday, June 13 2017

Frama-C 15 (Phosphorus) released, and open source case studies

Frama-C 15 (Phosphorus) has been released, and the OPAM package is already available! A MinGW-based OPAM package, distributed by fdopen's MinGW OPAM repository, is also available.

In this post, we briefly highlight two new features in this release. We also announce the release of a new Github repository, open-source-case-studies, which contains some snapshots of code bases ready to be analyzed with Frama-C/EVA.

Highlighted new features

E-ACSL in the default release

One notable change in this release is the direct integration of E-ACSL: instead of having to install OPAM packages frama-c and frama-c-e-acsl, you only need to install frama-c.

E-ACSL enables runtime verification in Frama-C, serving as an efficient tool for detecting undefined behavior and for debugging ACSL specifications. It can be used in a "stand-alone" mode (e.g. with assertions generated by the RTEgen plug-in), or in combination with EVA, in which case its instrumentation is more efficient: EVA only generates ACSL assertions for the properties that it cannot prove, thus greatly reducing E-ACSL's instrumentation.

Note that, due to the usage of jemalloc and some technical details, E-ACSL is disabled by default in Mac and Windows.

Better pretty-printing of #include directives

One of the drawbacks of the -print option of Frama-C was the fact that the code was entirely preprocessed, expanding a Hello world example to several hundreds of lines, due to the expansion of #include <stdio.h> and derived files.

There are now two options, -print-libc and -no-print-libc (the latter is enabled by default) which control the folding/unfolding of #include directives in pretty-printed code. More specifically, if your original code is:

#include <stdio.h>

int main() {
    printf("hello world!\n");
    return 0;

Then the result of -print will be:

/* Generated by Frama-C */
#include "errno.h"
#include "stdarg.h"
#include "stddef.h"
#include "stdio.h"
/*@ requires valid_read_string(format);
    assigns \result, __fc_stdout->__fc_FILE_data;
    assigns \result
      \from (indirect: __fc_stdout->__fc_FILE_id),
            __fc_stdout->__fc_FILE_data, (indirect: *(format + (0 ..)));
    assigns __fc_stdout->__fc_FILE_data
      \from (indirect: __fc_stdout->__fc_FILE_id),
            __fc_stdout->__fc_FILE_data, (indirect: *(format + (0 ..)));
int printf_va_1(char const *format);

int main(void)
  int __retres;
  printf_va_1("hello world!\n");
  __retres = 0;
  return __retres;

There are two interesting things to notice here:

  1. Some #include directives are present at the beginning of the file. These directives correspond to all files from the Frama-C standard library whose identifiers were present in the (expanded) original code. For instance, errno.h is present because Frama-C's stdio.h includes it. As you can see, the mechanism does not guarantee a minimal number of includes, but it is much cleaner than having all files expanded;

  2. The specification of printf_va_1 is visible. This is due to the fact that the Variadic plug-in (which is enabled by default on Frama-C 15 (Phosphorus)) generated this specification - it is not part of the standard Frama-C library. In fact, printf_va_1 is a specific instantiation of the variadic printf function. You can disable the automatic variadic translation with -variadic-no-translation, in which case -print will result in:

/* Generated by Frama-C */
#include "errno.h"
#include "stdarg.h"
#include "stddef.h"
#include "stdio.h"
int main(void)
  int __retres;
  printf("hello world!\n");
  __retres = 0;
  return __retres;

The Phosphorus release also includes, as usual, a series of bug fixes and minor improvements. Consult the Changelog for more details.

Open source case studies

A new Github repository on the Frama-C organization, open-source-case-studies, has been created to help users quickly run Frama-C (and EVA in particular) in more realistic code bases, which includes different sorts of open-source code; some of them are very small (a single file) while others contain significantly larger bases. Their usage is very simple: once you have installed Frama-C and put it in the PATH, enter one of the case study directories and run:

  • make to parse and run EVA;

  • make <target>.eva.gui to open the Frama-C GUI and view the results.

The target names vary on each case study, and can be obtained via make help. Note that this will show only the base target name, from which other targets are derived (e.g. <target>.parse, <target>.eva, <target>.eva.gui).

All case studies include a Makefile, which uses the files in fcscripts to generate targets and Makefile rules to allow running EVA quickly. Among the facilities provided by these scripts, we highlight:

  • templates for Frama-C parametrization (i.e. variables CPPFLAGS, FCFLAGS and EVAFLAGS to delineate which options are related to preprocessing, parsing and running EVA), including helpful default parameters;

  • automatic target dependencies on command line arguments: Frama-C reparses files only when they are modified, and re-runs EVA only when command line arguments change;

  • saving of intermediate results in directories (for easy comparison via Meld), to run other plug-ins without having to re-run EVA (e.g. frama-c -load <target>.eva/framac.sav ...).

Note, however, that there are some caveats concerning this repository:

  1. It is not representative of the scale of programs that Frama-C/EVA can handle; indeed, all large code bases where Frama-C/EVA is applied consist in industrial code that cannot be shared;

  2. One of the main purposes of the repository (internally) is to serve for non-regression testing, which means that some analyses are not fully parametrized;

  3. Some case studies include code that is not ideally dealt with by EVA, but may be useful for other plug-ins.

Those caveats aside, we hope this repository will give practical examples and help you to parametrize your own analyses. If you also have some interesting open source code bases on which to run EVA, you can submit them to us as a Github pull requests. This will make it easier to compare the behavior of future versions of Frama-C on such code, and to benefit from improvements in the analyzer.

Tuesday, April 11 2017

A simple Eva tutorial, part 3

On the previous post we've seen how to run Eva, but at the end we had a NON TERMINATING FUNCTION for a function that is supposed to always terminate, a likely indication that a definitive undefined behavior has been found in the analysis. In this post, we will see how to diagnose such cases, using derived plug-ins and the GUI.

We will reuse the save file produced at the end of the analysis, value2.sav.

When the GUI is not enough

Usually, after running the value analysis in the command-line, we launch the GUI to visualize the results:

frama-c-gui -load value2.sav

In this case, because of the NON TERMINATING FUNCTION message, we know that at some point in the program we will have red statements in the GUI, which indicate unreachable code.

By scrolling down from the main function, we reach the non-terminating statement, which is a call to test_x25519:

Unreachable code in the Frama-C GUI

Note the red semicolon at the end of the statement, and the fact that the following statements are also red. If we click on the statement, the Information panel says that This call never terminates.

You can right-click on the function and Go to the definition of test_x25519, and you will find the same thing inside, this time a call to crypto_x25519_public_key, and so on, until you reach fe_tobytes, which is slightly different: it contains a for loop (defined via a macro FOR), after which all statements are red, but the loop itself is not an infinite loop: it simply iterates i from 0 to 5. How can this be non-terminating?

The answer, albeit non-intuitive, is simple: there is one statement inside the loop which is non-terminating, but not during the first iteration of the loop. Because the GUI colors are related to the consolidated result of all callstacks (i.e., if there is at least one path which reaches a statement, it is marked as reachable), it cannot show precisely which callstack led to the non-termination. To better understand what happens here, we will use the Nonterm plug-in.

Nonterm to the rescue

Nonterm is a small plug-in that uses the result of the value analysis to display callstack-wise information about non-terminating statements, by emitting warnings when such statements are found. It requires Eva to have been executed previously, but it runs very quickly itself, so you do not need to save its results. Close the GUI and re-run it again, this time with Nonterm:

frama-c-gui -load value2.sav -then -nonterm -nonterm-ignore exit

The -nonterm-ignore exit argument serves to minimize the number of warnings related to calls to the libc exit() function, which is always non-terminating.

The warnings generated by Nonterm are displayed in the Messages panel, after those emitted by Eva.

Examples of Nonterm warnings

The warnings display the non-terminating callstacks. The order of the warnings themselves is not relevant. However, some kinds of warnings are more useful than others. Here is a rough indication of their relevance, from most to least precise:

  1. Non-terminating statements;
  2. Non-terminating loops;
  3. Non-terminating function calls;
  4. Unreachable returns.

In our analysis, the first (and only) warning about a non-terminating statement is the following:

Non-terminating statement

Note a few important details about the Frama-C GUI:

  • When you click on the warning in the Messages panel, the GUI focuses on the related statement.
  • When a statement has associated annotations (here, two warnings), the focus is placed on the first annotation, instead of the statement itself. This does not imply that the annotation itself is related to this specific warning.
  • The property status indicators (colored circles, or bullets on the left of each property) display the consolidated status of all callstacks; in particular, if the property is definitively valid in one callstack, but possibly/definitively invalid in another, the GUI displays a yellow bullet.

Nonterm restores some of the information lost due to callstack consolidation. The highlighted warning in particular gives us the following information:

  1. There exists a stack trace in which statement h5 -= c5 << 25 does not terminate;
  2. There is exactly one stack trace in which the statement never terminates; all other stack traces (which are not shown in the warning) terminate.

Currently, it is not possible to select a stack trace from the Messages panel, but we can do so using the Values panel. If we switch to it (keeping the statement highlighted in the source code), we can see that there are 40 different stack traces reaching this point.

Values Panel

The Values panel is arguably the most powerful inspection tool for the Eva plug-in in the Frama-C GUI. Some of its features were presented in earlier posts, but for the sake of completeness, here are some commented screenshots:

Values panel

The values displayed in this panel are related to the green highlighted zone in the Cil source.

The Ctrl+E shortcut is equivalent to highlighting a statement, then right-clicking Evaluate ACSL term. The Ctrl+Shift+E shortcut is slightly more powerful: it also evaluates terms, such as \valid(p). This command is not available from any menus.

The Multiple selections checkbox allows adding several expressions to be compared side-by-side. When checked, highlighting an expression in the same statement adds a column with its value. Note that highlighting a different statement results in resetting all columns.

The three checkboxes to the right are seldom used: Expand rows simply expands all callstacks (but generates visual clutter); Consolidated value displays the row all (union of all callstacks); and Per callstack displays a row for each separate callstack.

The callstacks display has several contextual menus that can be accessed via right-clicks.

Callstacks display

Let us start from the bottom: right-clicking on a callstack shows a popup menu that allows you to focus on a given callstack. This focus modifies the display in the Cil code viewer: reachability will only be displayed for the focused callstack(s). We will come back to that later.

Right-clicking on a cell containing a value allows filtering on all callstacks for which the expression has the same value. This is often used, for instance, to focus on all callstacks in which a predicate evaluates to invalid or unknown.

Finally, clicking on the column headers allows filtering columns.

Note that the Callstacks column header displays a pipette icon when a filter is being applied, to remind you that other callstacks exist.

Filtering non-terminating callstacks

In our code, despite the existence of 40 callstacks, only one of them is non-terminating. If you highlight the 0 ≤ c5 expression before statement h5 -= c5 << 25, you will see that only a single callstack displays invalid in the column 0 ≤ c5. Focus on this callstack using the popup menu, then highlight expression c5 in the Cil code. You will obtain the following:

Focused on a non-terminating callstack

As you can see, the GUI now displays the statements following h5 -= c5 << 25 in red, indicating thay they are unreachable in the currently focused callstacks. The exact value that caused this is shown in column c5: -1. The C standard considers the left-shift of a negative number as undefined behavior. Because -1 is the only possible value in this callstack, the reduction caused by the alarm leads to a post-state that is <BOTTOM>.

Proceeding with the analysis

To allow Eva to continue the analysis of the code, we need to modify it in some way. Since we are not experts in cryptography, we are unable to provide a definitive explanation why the code was written this way. In any case, it is not specific to Monocypher, but also present in TweetNaCl and ref10, two cryptographic libraries.

It is likely that replacing the signed carry variables in function fe_mul with unsigned ones would get rid of the undefined behavior, without changing the expected behavior of the code. However, without a more formal analysis performed by a cryptographer, this is just guesswork. Still, we need to do something to be able to continue the analysis (and possibly spot more undefined behaviors), such as changing the declarations of variables c0 to c9 to u64 instead of i64. Then, re-parse the sources, re-run the analysis, and keep iterating.

Ideas for complex situations

In the beta version of this post, we were using version 0.1 of Monocypher, which had a different version of functions related to fe_mul. In particular, some of the functions were taken from TweetNaCl, and the code was not unrolled the same way as in Monocypher 0.3. One of the consequences was that Nonterm was unable to show as clear a picture as in this case; it was necessary to perform syntactic loop unrolling (e.g., using loop pragma UNROLL) just to be able to clearly see in the GUI which statement was non-terminating.

Future developments in Frama-C and in the Eva plug-in will help identifying and visualizing such situations more easily.


We would like to thank loup-vaillant (Monocypher's author) for the discussions concerning Monocypher and Eva's analysis. New versions of Monocypher have been released since the analysis performed for this series of posts, which do not present the undefined behavior described in this post.

Friday, March 17 2017

A simple Eva tutorial, part 2

On the previous post we've seen some recommendations about using Frama-C/Eva, and some tips about parsing. In this post, we will see how to run Eva, and how to quickly setup it for a more precise result.

We will reuse the save file produced at the end of the parsing, parsed.sav.

First run of Eva

The default parameters of Eva are intended for a fast analysis. In Frama-C 14 (Silicon), option -val-builtins-auto is recommended to enable the usage of built-in functions that improve the precision and sometimes the speed of the analysis1.

1 This option will be enabled by default in Frama-C 15 (Phosphorus).

The following command-line should result in a relatively quick first analysis:

frama-c -load parsed.sav -val-builtins-auto -val -save value.sav

Note that we save the result in another file. It can be reused as input for another analysis, or visualization in the GUI.

The analysis will likely output many alarms, some due to loss of precision, others due to an incorrect setup. Here are a few important alarms concerning an incorrect setup:

  1. Missing code or specification

    file.c:42:[kernel] warning: Neither code nor specification for function foo, generating default assigns from the prototype

    There are two major causes for this warning: (1) the file containing the function definition was not given to Frama-C during parsing; or (2) the function has no source code and no ACSL specification was given.

    In the first case, the solution is to include the missing source file. Parsing will succeed even if only a declaration (function prototype) is present, but Eva requires more than that. It may be necessary to return to the parsing stage when this arrives.

    In the second case, you must supply an ACSL specification for the function, otherwise Eva will assume it has no effect, which may be unsound. To do it with minimal modifications to the original code, you can do the following:

    1. create a file, say stubs.h;
    2. copy the function prototype to be stubbed in stubs.h (adding the necessary #includes for the types used in the prototype);
    3. add an ACSL specification to this prototype;
    4. include stubs.h in the original source, either by adding #include "stubs.h", or using GCC's -include option (e.g. -cpp-extra-args="-includestubs.h", without spaces between -include and the file name).
  2. Missing assigns clause, or missing \from

    When analyzing functions without source code, Eva imposes some constraints on the ACSL specification: they must contain assigns clauses, and these clauses must have \from dependencies. Otherwise, warnings such as the following may be generated:

    foo.c:1:[value] warning: no 'assigns \result \from ...' clause specified for function foo
    foo.c:3:[value] warning: no \from part for clause 'assigns *out;' of function foo
    foo.c:6:[kernel] warning: No code nor implicit assigns clause for function foo, generating default assigns from the prototype

    The following is an example of an incomplete specification:

    /*@ assigns *out; */
    void foo(int in, int *out);

    Even if it contains an \assigns clause for pointer out, it does not say where the result comes from. Adding \from in, for instance, makes the specification complete from the point of view of Eva.

    Note: Eva cannot verify the correctness of the specification in the absence of code, especially that of ensures clauses. If you provide an incorrect specification, the result may be unsound. For that reason, it is often useful to write a simplified (or abstract) implementation of the function and then run the analysis. If Eva has both the code and the specification, it is able to check ensures clauses and detect some kinds of errors.

Running Eva on monocypher

Running Eva on parsed.sav will start the value analysis on the main function defined in test.c. Due to the large number of small functions in Monocypher, Eva will output a huge amount of lines, whenever a new function is entered. Adding option -no-val-show-progress will omit messages emitted whenever entering a new function.

Also, the fact that this code contains lots of small functions with few or no side-effects is a very strong indicator that -memexec-all will be very helpful in the analysis.

Memexec, which is part of Eva, acts as a cache that allows reusing the result of function calls when their memory footprint is unchanged. It dramatically improves performance.

Combining both options, we obtain the following command-line:

frama-c -load parsed.sav -val -val-builtins-auto \
        -no-val-show-progress -memexec-all -save value.sav

The analysis will then start and emit several warnings (mainly due to imprecisions). It should finish in a few seconds, depending on your processor speed.

Improving Eva results

After running the value analysis, it is a good time to check what the result looks like, using the GUI:

frama-c-gui -load value.sav

In the screenshot below, we indicate some parts of the GUI that are useful when inspecting Eva results (besides the source view). We also indicate some parts that are never (or rarely) used with Eva.

Frama-C GUI for Eva

Note that the Properties tab (between Console and Values) is not updated automatically: you need to click on the Refresh button before it outputs anything, and after changing filters.

Several tips concerning this panel were presented in a previous post about the GUI. If you follow them, you will be able to make the Properties panel show the total of Unknown (unproven) properties for the entire program, and only those. This number is often similar to the number of messages in the Messages panel.

In Monocypher, after setting the filters to show every Unknown property in the entire program, and clicking Refresh, we obtain over 900 unproven properties. Since the analysis was not tuned at all for precision (other than with -val-builtins-auto), this number is not particularly surprising.

A quick way to improve on results is to use the Loop analysis plug-in.

The Loop analysis plug-in performs a mostly syntactic analysis to estimate loop bounds in the program (using heuristics, without any soundness guarantees) and outputs a list of options to be added to the value analysis. Running Eva again with these options should improve the precision, although it may increase analysis time. Loop analysis' main objective is to speed up the repetitive task of finding loop bounds and providing them as semantic unrolling (-slevel) counters. The analysis may miss some loops, and the estimated bounds may be larger or smaller, but overall it minimizes the amount of manual work required.

Loop analysis does not depend on Eva, but if it has been run, the results may be more precise. In Monocypher, both commands below give an equivalent result (the difference is not significative in this context):

frama-c -load parsed.sav -loop
frama-c -load value.sav -loop

In both cases, Loop analysis' effect is simply to produce a text output that should be fed into Eva for a new analysis:

[loop] Add this to your command line:
       -val-slevel-merge-after-loop crypto_argon2i \
       -val-slevel-merge-after-loop crypto_blake2b_final \

You should, by now, use a shell script or a Makefile to run the Frama-C command line, adding all the -val-slevel-merge-after-loop and -slevel-function lines to your command.

Let us consider that the environment variable LOOPFLAGS contains the result of Loop analysis, and EVAFLAGS contains the flags mentioned previously (-no-val-show-progress, -val-builtins-auto and -memexec-all). Then the following command will re-run Eva with a more detailed (and, hopefully, precise) set of parameters:

frama-c -load parsed.sav $LOOPFLAGS -val $EVAFLAGS -save value2.sav

Opening this file on the GUI will indicate approximately 500 warnings, which is still substantial, but much better than before. Improvements to Loop analysis in the next release of Frama-C will allow this number to be reduced slightly.

The basic tutorial is over, but there are several paths to choose

From here on, there are several possibilities to reduce the imprecisions in the analysis:

  • Inspect alarms and see if their functions contain loops that were not inferred by Loop analysis; if so, adding their bounds to -slevel-function can improve the precision of the analysis;

  • Increase the precision using other parameters, such as -plevel;

  • Stub libc functions to emulate/constrain inputs when relevant;

  • Use Eva's abstract domains (e.g. -eva-equality-domain) to improve precision;

  • Stop at the first few alarms (-val-stop-at-nth-alarm), to track more closely the sources of imprecision. However, when there are hundreds of alarms, this is more useful as a learning experience than as a practical solution.

Each solution is more appropriate in a specific situation. Here are a few tips for an intermediate-level user of Eva:

  1. Functions that perform array initialization are often simple (a loop with a few assignments per iteration), so unrolling them completely should not slow down the analysis excessively. The Loop analysis plug-in usually works with them, but some pattern variations may throw it off. You may want to check the proposed values in such loops. Because initialization happens early in program execution, checking such loops may yield good results.

  2. The plevel parameter is often used in response to messages such as:

    monocypher.c:491:[kernel] more than 200(255) elements to enumerate. Approximating.

    where the first number is the current plevel (by default, 200), and the second number is the amount that would be required to avoid the approximation. In this case, -plevel 255 would be reasonable, but if you had more than 200(67108864) elements, for instance, it would not be helpful to set the plevel to such a high value.

  3. Stubbing is a good approach when dealing with functions that are closely system-dependent, specifically input functions that read from files, sockets, or from the command-line. Check the Frama-C builtins in __fc_builtin.h, they provide some useful primitives for abstracting away code with non-deterministic functions.

  4. Eva's domains have specific trade-offs between precision and efficiency, and some have broader applicability than others. Future posts in this blog will describe some of these domains, but as a rule of thumb, two domains that are fairly low-cost and generally useful are -eva-equality-domain (for syntactic equalities) and -eva-gauges-domain (for some kinds of loops).

  5. The Messages panel in the GUI is chronologically sorted, so it can help the user follow what the analysis did, to try and identify sources of imprecision. However, even in this case, there is still an advantage to using -val-stop-at-nth-alarm: because the execution stops abruptly, there are possibly less callstacks displayed in the GUI, and therefore it may be easier to see at a glance which parts of the code were actually executed, and the dependencies between values that lead to the alarm.

Non-terminating function?

The "beginner" tutorial ends here, but one thing that you may have noticed after running Eva, is the dreaded "non terminating function" message at the end of the analysis:

[value:final-states] Values at end of function main:

This indicates that, somewhere during the analysis, a completely invalid state was found, and Eva could not proceed. This usually indicates one of the following:

  1. Eva's setup is incorrect: most likely, some function has missing or incorrect specifications, or some case that cannot be currently handled by Eva (e.g. recursive calls) was encountered.
  2. A definitively undefined behavior is present in the code, which may or may not lead to an actual bug during execution. In either case, it should be taken care of.

We will see how to handle such situations in the next post, using the GUI and the Nonterm plug-in (-nonterm), in a tutorial destined for beginners and experienced users alike.

Tuesday, March 7 2017

A simple Eva tutorial, part 1

(with the collaboration of T. Antignac, Q. Bouillaguet, F. Kirchner and B. Yakobowski)

This is the first of a series of posts on a new Eva tutorial primarily aimed at beginners (some of the later posts contain more advanced content).

Reminder: Eva is the new name of the Value analysis plug-in.

There is a Value tutorial on Skein-256 that is part of the Value Analysis user manual. The present tutorial is complementary and presents some new techniques available in Frama-C. If you intend to use Eva, we recommend you read the Skein-256 tutorial as well because it details several things that will not be repeated here. (However, it is not required to have read the Skein-256 before this one.)

The source code used in this tutorial is the version 0.3 of Monocypher, a C99-conformant cryptographic library that also includes a nice test suite.

Note: newer versions of Monocypher are available! For this tutorial, please ensure you download version 0.3, otherwise you will not obtain the same behavior as described in this tutorial.

Future posts will include more advanced details, useful for non-beginners as well, so stay tuned!

Starting with Frama-C/Eva

This tutorial will use Monocypher's code, but it should help you to figure out how to analyze your code as well. First and foremost, it should help you answer these questions:

  1. Is my code suitable for a first analysis with Frama-C/Eva?
  2. How should I proceed?

(Un)Suitable code

There are lots of C code in the wild; for instance, searching Github for language:C results in more than 250k projects. However, many of them are not suitable candidates for a beginner, for reasons that will be detailed in the following.

Note that you can analyze several kinds of codes with Frama-C/Eva. However, without previous experience, some of them will raise many issues at the same time, which can be frustrating and inefficient.

Here is a list of kinds of code that a Frama-C/Eva beginner should avoid:

  1. Libraries without tests

    Eva is based on a whole-program analysis. It considers executions starting from a given entry point. Libraries without tests contain a multitude of entry points and no initial context for the analysis. Therefore, before analyzing them, you will have to write your own contexts1 or test cases.

  2. Command-line tools that rely heavily on the command-line (e.g. using getopt), without test cases.

    Similar to the previous situation: a program that receives all of its input from the command-line behaves like a library. Command-line parsers and tools with complex string manipulation are not the best use cases for the Eva's current implementation. A fuzzer might be a better tool in this case (though a fuzzer will only find bugs, not ensure their absence). Again, you will have to provide contexts1 or test cases.

  3. Code with lots of non-C99 code (e.g. assembly, compiler extensions)

    Frama-C is based on the C standard, and while it includes numerous extensions to handle GCC and MSVC-specific code, it is a primarily semantic-based tool. Inline assembly is supported syntactically, but its semantics needs to be given via ACSL annotations. Exotic compiler extensions are not always supported. For instance, trying to handle the Linux kernel without previous Frama-C experience is a daunting task.

  4. Code relying heavily on libraries (including the C standard library)

    Frama-C ships with an annotated standard library, which has ACSL specifications for many commonly-used functions (e.g. string.h and stdlib.h). This library is however incomplete and in some cases imprecise2. You will end up having to specify and refine several functions.

1 A context, here, is similar to a test case, but more general. It can contain, for instance, generalized variables (e.g. by using Frama_C_interval or ACSL specifications).

2 A balance is needed between conciseness (high-level view), expressivity, and precision (implementation-level details). The standard library shipped with Frama-C tries to be as generic as possible, but for specific case studies, specialized specifications can provide better results.

Each new version of Frama-C brings improvements concerning these aspects, but we currently recommend you try a more suitable code base at first. If your objective is to tackle such a challenging code base, contact us! Together we can handle such challenges more efficiently.

This explains why Monocypher is a good choice: it has test cases, it is self-contained (little usage of libc functions), and it is C99-conforming.

The 3-step method

In a nutshell, the tutorial consists in performing three steps:

  1. Parsing the code (adding stubs if necessary);
  2. Running Eva with mostly default parameters (for a first, approximated result);
  3. Tuning Eva and running it again.

The initial parsing is explained in this post, while the other steps will be detailed in future posts.

General recommendations

Before starting the use of Frama-C, we have some important general recommendations concerning the Eva plug-in:

  1. DO NOT start with the GUI. Use the command-line. You should consider Frama-C/Eva as a command-line tool with a viewer (the GUI). The Frama-C GUI is not an IDE (e.g. you cannot edit code with it), and Eva does not use the GUI for anything else other than rendering its results.

  2. Use scripts. Even a simple shell script, just to save the command-line options, is already enough for a start. For larger code bases, you will want Makefiles or other build tools to save time.

  3. Use frama-c -kernel-help (roughly equivalent to the Frama-C manpage) and frama-c -value-help to obtain information about the command-line options. Each option contains a brief description of what it does, so grepping the output for keywords (frama-c -kernel-help | grep debug for instance) is often useful. Otherwise, consider Stack Overflow - there is a growing base of questions and answers available there.

  4. Advance one step at a time. As you will see, the very first step is to parse the code, and nothing else. One does not simply run Eva, unless he or she is very lucky (or the program is very simple). Such precautions may seem excessive at first, but being methodical will save you time in the long run.

Parsing the code

Often overlooked, this step is erroneously considered as "too simple" ("just give all files to the command-line!"). In a few cases, it is indeed possible to run frama-c *.c -val and succeed in parsing everything and running Eva.

When this does not work, however, it is useful to isolate the steps to identify the error. Here are some general recommendations:

  1. Start with few files, and include more when needed

    Note that parsing may succeed even if some functions are only declared, but not defined. This will of course prevent Eva from analyzing them. If so, you may have to return to this step later, adding more files to be parsed.

  2. Ensure that preprocessor definitions and inclusions are correct

    Several code bases require the use of preprocessor definitions (-D in GCC) or directory inclusions (-I in GCC) in order for the code to be correctly preprocessed. Such information is often available in Makefiles, and can be given to Frama-C using e.g. -cpp-extra-args="-DFOO=bar -Iheaders".

    -cpp-extra-args is the most useful option concerning this step. It is used in almost every case study, and often the only required option for parsing. Note: old releases of Frama-C did not have this option, and -cpp-command was recommended instead. Nowadays, -cpp-command is rarely needed and should be avoided, because it is slightly more complex to use.

  3. Make stubs for missing standard library definitions

    Frama-C's standard library is incomplete, especially for system-dependent definitions that are not in C99 or in POSIX. Missing constants, for instance, may require the inclusion of stub files (e.g. stubs.h) with the definitions and/or the ACSL specifications. A common way to include such files is to use GCC's -include option, documented here.

  4. Save the result

    Use Frama-C's -save/-load options to avoid having to reparse the files each time. There is no default extension associated with Frama-C save files, although .sav is a common choice. For instance, running:

    frama-c <parse options> -save parsed.sav

    will try to parse the program and, if it succeeds, will save the Frama-C session to parsed.sav. You can then open it in the GUI (frama-c-gui -load parse.sav), to see what the normalized source code looks like, or use it as an input for the next step.

Reminder: for the Eva plug-in, the GUI is not recommended for parametrizing/tuning an analysis. It is best used as a viewer for the results.

The default output of Eva is rather verbose but very useful for studying small programs. For realistic case studies, however, you may want to consider the following options:

  • -no-val-show-progress: does not print when entering a new function. This will be the default in Frama-C 15 (Phosphorus);

  • -value-msg-key=-initial-state: does not print the initial state;

  • -value-msg-key=-final-states: does not print the final states of the analysis.

Note the minus symbols (-) before initial-state and final-states. They indicate we want to hide the messages conditioned by these categories.

Parsing monocypher

As indicated above, the naive approach (frama-c *.c) does not work with monocypher:

$ frama-c *.c

[kernel] Parsing FRAMAC_SHARE/libc/__fc_builtin_for_normalization.i (no preprocessing)
[kernel] Parsing monocypher.c (with preprocessing)
[kernel] Parsing more_speed.c (with preprocessing)
[kernel] syntax error at more_speed.c:15:
         14    // Specialised squaring function, faster than general multiplication.
         15    sv fe_sq(fe h, const fe f)
         16    {
         17        i32 f0 = f[0]; i32 f1 = f[1]; i32 f2 = f[2]; i32 f3 = f[3]; i32 f4 = f[4];

The first line is always printed when Frama-C parses a source file, and can be ignored.

The second line indicates that monocypher.c is being parsed.

The third line indicates that more_speed.c is now being parsed, implying that the parsing of monocypher.c ended without issues.

Finally, we have a parsing error in more_speed.c, line 15. That line, plus the lines above and below it, are printed in the console.

Indeed, the file more_speed.c is not a valid C source (sv is not a type defined in that file, and it does not include any other files). But this is not an actual issue, since more_speed.c is not part of the library itself, simply an extra file (this can be confirmed by looking into the makefile). Thus we are going to restrict the set of files Frama-C is asked to analyze.

Note: Frama-C requires the entire program to be parsed at once. It may be necessary to adapt compilation scripts to take that into account.

We also see that the rule for building monocypher.o includes a preprocessor definition, -DED25519_SHA512. We will add that to our parsing command, which will then become:

frama-c test.c sha512.c monocypher.c -cpp-extra-args="-DED25519_SHA512" -save parsed.sav

The lack of error messages is, in itself, a resounding success.

The first part of this tutorial ends here. See you next week!

For now, you can start reading the Skein-256 tutorial available at the beginning of the Eva manual. Otherwise, if you already know Eva (and still decided to read this), you may try to find some undefined behavior (UB) in Monocypher 0.3!

Hint: There is indeed some UB, although it does not impact the code in any meaningful way. At least not with today's compilers, maybe in the future... and anyway, it has been fixed in the newer releases of Monocypher.

Tuesday, December 13 2016

Frama-C Silicon has been released!

Frama-C 14 (Silicon) has just been released. In this post, we present a few additions that should help everyday usage of Value EVA.

Value is now EVA

The Value analysis plug-in has been renamed EVA, for Evolved Value Analysis. It has a new architecture that allows plugging abstract domains, among other features. It is a truly remarkable evolution which this post is too small to contain1, however, so it will presented in more detail later.

1 Facetious reference to Fermat's Last Theorem

Automatic built-in matching for libc functions

One of the new user-friendly features is the -val-builtins-auto option, which avoids having to memorize which built-in to use for each libc function that has one, namely malloc, free, and some floating-point and string-related functions (e.g. pow and strlen).

For instance, consider the following toy program, which simply allocates a buffer, copies a string into it, then allocates a buffer of the right size for the string, and stores it there.

// file.c
#include <stdio.h>
#include <stdlib.h>
#include "string.c" // include Frama-C's implementation of string.h

int main() {
  char *buf = malloc(256); // allocate a large buffer
  if (!buf) exit(1);
  char *msg = "silicon";
  strcpy(buf, msg);
  size_t n = strlen(buf);
  char *str = malloc(n + 1); // allocate a buffer with the exact size (plus '\0')
  if (!str) exit(1);
  strncpy(str, buf, n);
  str[n] = 0;
  size_t n2 = strlen(str);
  //@ assert n == n2;
  return 0;

This program uses dynamic allocation and calls functions from string.h.

The following command-line is enough to obtain an interesting result:

frama-c file.c -val -val-builtins-auto -slevel 7

Without -val-builtins-auto one would need to use this overly long argument:

-val-builtin malloc:Frama_C_alloc_by_stack,free:Frama_C_free,strlen:Frama_C_strlen

For more details about Frama_C_alloc_by_stack, check the EVA manual, section 8.1.

The builtins for free and strlen were automatically chosen by EVA. Note however that strcpy and strncpy do not have builtins. In this case, we include "string.c" (which is actually in share/libc/string.c) to use the implementations available with Frama-C.

Analyzing a program using the implementations in share/libc/string.c is less efficient than using a built-in, but more precise than using only a specification. These implementations are designed to minimize the number of alarms when their inputs are imprecise. Also, because they are not optimized for execution, they are conceptually simpler than the actual libc implementations.

Using these functions. -slevel 7 ensures that their loops are fully unrolled in the example. Can you guess why 7 is the right value here?

Inspecting values in the GUI

Another improvement to usability comes in the form of a popup menu in the GUI. To see it, run the following command using the same file as previously:

frama-c-gui file.c -val -val-builtins-auto -slevel 7

On the Frama-C GUI, click on the str expression in the statement str = (char *)malloc(n + (size_t)1); (corresponding to line 11 in the original source code). Then open the Values tab, and you will see something similar to this:

Show pointed values in the GUI

In the Values tab on the bottom, right-clicking on the cell containing the NULL; &__malloc_main_l11[0] value will show a popup menu "Display values for ...". Clicking on it will add a new column displaying its contents.

Before Silicon, this information was already available, but as the result of a long and painful process. The new popup menu shows one entry per pointed variable in the chosen cell, so if there are several different values, there will be several popup menu entries.

malloc may fail

In the previous example, the values of str are those returned by the malloc builtin: NULL and a newly allocated base (__malloc_main_l11). This models the fact that there may not be enough memory, and malloc may fail. The code should definitely handle this case! But for the hurried evaluator, the use of option -no-val-malloc-returns-null can help focus on the other potential run-time errors (before coming back to the malloc-related ones).

Still ways to go

In this example, there are still some warnings, due to the specification of functions strcpy and strncpy, which use logical constructs not yet evaluated by EVA (but very useful for WP). They are not an issue in this example, since we used the actual implementation of these functions, and therefore do not need their specifications, but future work on EVA will help deal with these details and provide a more streamlined experience.