Symbolic Pathfinder for SV-COMP
- 4k Downloads
This paper describes the benchmark entry for Symbolic Pathfinder, a symbolic execution tool for Java bytecode. We give a brief description of the tool and we describe the particular run configuration that was used in the SV-COMP competition. Furthermore, we comment on the competition results and we outline some directions for future work.
1 Verification Approach
Symbolic Pathfinder (SPF) is a program analysis tool for Java bytecode; the tool is based on symbolic execution. In this approach, programs are executed on symbolic inputs representing multiple concrete inputs. Values of variables are represented as numeric constraints, generated from the analysis of the code structure, i.e. conditionals and other statements in the program. These constraints are then solved using different constraint solvers (both off-the-shelf and built-in-house) to generate test inputs that are guaranteed to reach those parts of the code.
SPF can also be used for probabilistic analysis by leveraging model counting over symbolic constraints [2, 4], and for automated program repair [6, 7]. Most recent work explores combinations of SPF with AFL-style fuzzing  and further differential analysis for regression problems.
2 Software Architecture
Symbolic execution is implemented by a “non-standard” interpretation of bytecodes. The symbolic information is propagated via attributes associated with program variables, operands, etc. The analysis can start from any point in the program and it can perform mixed concrete/symbolic execution. SPF relies on jpf-core’s search engine to explore different paths through the code. The default search strategy is depth-first search. State matching (as implemented in jpf-core) is usually turned off during symbolic execution.
SPF uses several constraint solvers and decision procedures, most notably Z3 and Z3bitvector, which are available from https://github.com/Z3Prover/z3. SPF implements both incremental and non-incremental constraint solving.
3 Discussion of Strengths and Weaknesses of the Approach
The competition results are provided on the SV-COMP website. The results indicate that SPF outperforms the other tools in terms of correct answers (337), cpu time (1300 s) and energy (13000 J). However, SPF also reported 6 incorrect results, which penalized the overall final score. While the incorrect true results are due to the bounded nature of the analysis, the incorrect false results are due mainly to the string analysis, with the exception of one result which was due to an error in jpf-core which has since been corrected. The string solver was incorrectly specified and tested (i.e. the path to the string solver is hard coded in the current implementation but we provided no string solver for the competition).
In the future we plan to test SPF on the competition string examples using either ABC or Z3str and to robustify the implementation. We also plan to contribute to the competition by adding more interesting benchmarks, particularly related to input data structures.
4 Tool Setup and Configuration
For this competition we used the version with the timestamp Mon Nov 19 09:51:16 CET 2018, which refers to the date when we pulled the artifacts from the GitHub repository and generated the jpf-symbc jar archive.
The user then creates a *.jpf configuration file (described in detail below). For the competition we modified the SPF tool to handle the non-deterministic constructs required by the competition.
4.1 Example Configuration
We give here an example configuration that can be used to run the SPF tool; this is the default configuration, that we used in the competition. The explanation for the different options is given in parenthesis.
Open image in new window (specify the target application)
Open image in new window (path to your class example)
Open image in new window (path to the source of your example)
Open image in new window (specify the decision procedure)
Open image in new window (specify the bitvector length)
Open image in new window (specify various min max values)
Open image in new window (print debug information)
Open image in new window (specify search limit)
Open image in new window (handling symbolic arrays)
Open image in new window (specify string analysis)
Open image in new window(specify string solver).
SPF also has the option of running the constraint solving incrementally. Note however that we did not use the string solving and the incremental solving options in the competition as we did not have enough time to prepare and test those features, as we were entered late in the competition.
5 Software Project and Contributors
Information about the project and contributors can be found at the project webpage: https://github.com/SymbolicPathFinder/jpf-symbc. For more information please contact the authors of this paper.
- 1.Bang, L., Aydin, A., Phan, Q., Pasareanu, C.S., Bultan, T.: String analysis for side channels with segmented oracles. In: FSE 2016, Seattle, WA, USA, 13–18 November 2016, pp. 193–204 (2016)Google Scholar
- 2.Filieri, A., Pasareanu, C.S., Visser, W.: Reliability analysis in symbolic PathFinder. In: ICSE 2013, San Francisco, CA, USA, 18–26 May 2013, pp. 622–631 (2013)Google Scholar
- 4.Geldenhuys, J., Dwyer, M.B., Visser, W.: Probabilistic symbolic execution. In: ISSTA 2012, Minneapolis, MN, USA, 15–20 July 2012, pp. 166–176 (2012)Google Scholar
- 6.Le, X.-B.D., Chu, D.-H., Lo, D., Le Goues, C., Visser, W.: JFIX: semantics-based repair of Java programs via symbolic PathFinder. In: ISSTA 2017, pp. 376–379 (2017)Google Scholar
- 7.Le, X.-B.D., Chu, D.-H., Lo, D., Le Goues, C., Visser, W.: S3: syntax- and semantic-guided repair synthesis via programming by examples. In: ESEC/FSE 2017, pp. 593–604. ACM, New York (2017)Google Scholar
- 8.Noller, Y., Kersten, R., Pasareanu, C.S.: Badger: complexity analysis with fuzzing and symbolic execution. In: ISSTA 2018, Amsterdam, The Netherlands, 16–21 July 2018, pp. 322–332 (2018)Google Scholar
- 9.Pasareanu, C.S., Rungta, N., Visser, W.: Symbolic execution with mixed concrete-symbolic solving. In: ISSTA 2011, Toronto, ON, Canada, 17–21 July 2011, pp. 34–44 (2011)Google Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.