Skip to content
Switch branches/tags
Go to file

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time


Thanks for downloading and trying CAUT.

You can get news and announcements at CAUT's homepage:
--What it does? 
	CAUT is a DSE(dynamic symbolic execution)-based engine to automatically generate test data for C program at unit/program testing level.
	It currently supports the coverage-driven testing on branch and MC/DC criteria.
	And it's easy to be extended to support other logical coverage criteria, like condition, condition/decision or multiple coverage.
	Up to now, serveral path exploration strategies have been implemented on CAUT. 

--What does it need before building CAUT?

	Install Ocaml 3.11 --> sudo apt-get install ocaml
	gcc and/or g++.
	We use lpsolve as the underlying constraint solver.
	Now we default use the Linux-32bit version of lpsolve library.
	Please change to Linux-64bit version if your platform is 64bit version.
		lpsolve library:
		or see tools/lpsolve for linux64

	Up to now, CAUT is only tested on Linux-Ubuntu 32 bit.
	But we think it is easy to port it to other unix supported platforms with a little modification.

-- How to build the caut library ?

Note: If encountering some building errors, you can contact

	1. build c-ocaml-sqlite-lib, which is used to create an sqlite interface in Ocaml. 
	   step1: cd ./tools/c-ocaml-sqlite-lib
           step2: make clean --> make --> sudo make install
	   default lib directory : /usr/lib/ocaml, refer to ./tools/c-ocaml-sqlite-lib for details.

        2. CAUT includes a modified version of CIL executable. 

	  CIL source code is available at: 
	  It is distributed under the revised BSD license. See cil/LICENSE for details.

	3. It may need to export lpsolve library path in $(HOME)/.bashrc
		 like this:
			export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/work/caut-lib/lpsolve
	   You should also export caut include path:
	   	  like this:
			export C_INCLUDE_PATH=$C_INCLUDE_PATH:/work/caut-lib

-- How to run CAUT on a unit/program under test ?
   	A simple example: refer to benchmarks/bubble.c
	A simple tutorial : refer to "tutorial"
		xx.c --> the file name of the program under test
	Generated file including:
		xx.cil.c --> the instrumented c file
		xx.orig.cil.c --> the original c file 
		xx.i --> the preprocessed c file
		xx.c.db --> cfg related info. db file

-- CAUT's source code directory

	README --> this file
	tutorial --> a simple tutorial
	caut.h  --> caut header file
	benchmarks/  --> some benchmarks from GNU Coreutils
		./ --> invoke cilly.byte.exe
		./  --> build the UUT with the caut library
		./Makefile --> makefile
		./ --> run branch testing
	lib/ --> caut library
	cil/ --> CIL
	tools/  --> some utility tools
	lpsolve --> lpsolve library

--Which search strategies on CAUT?

	1. CREST cfg-guided search strategy

	   For details:

	input --> p= p0,p1,p2,..pi,..,pn
        for all pk, typeof(pk)== _branch_, cal. UncoveredDistance(!pk)=min dist(!pk,b') (b' is uncovered branch)
	init. tries(!pk)=0;
        choose !pk with minimal UncoveredDistance(!pk)+tries(!pk) to force a new path prefix
	If the new path p', has not covered any uncovered branches, then tries(!pk)++, restart the search on p.
	If the new path p', has covered some uncovered branches, then p <- p' , recal. UncoveredDistance(!pk') and reinit. tries(!pk')=0

	2. KLEE rp-md2u search strategy
	   For details:
	Interleave the Random Path strategy with Min-Dist-to-Uncovered heuristic.
	The Random Path strategy is actually a probabilistic version of breadth-first search, which weights a path candidate of length l by 2^-l and randomly chooses candidates with the same length. The Min-Dist-to-Uncovered heuristic prefers the path candidate with minimal distance to uncovered goals in the CFG.
	3. Predictive Path Search strategy based on the Coverage Structure

	   For details: refer to our SERE'14 paper: automated coverage-driven test data generation via dynamic symbolic execution.

	4. other search strategies: DFS (depth-first search)
				    BFS (breath-first search)
			            LSF (local shortest first)
				    LLF (local longest first)
			            RANDOM (uniform random path search)

	5. fitness guided search strategy(experimental stage, not stable)

	   For details: Fitness-Guided Path Exploration in Dynamic Symbolic Execution. Xie et al.


a CIL-based dynamic symbolic execution (DSE) engine for C language




No releases published


No packages published