Combining Symbolic Execution and Model Checking for Data Flow Testing

Su, Ting; Fu, Zhoulai; Pu, Geguang; He, Jifeng; Su, Zhendong

doi:10.1109/icse.2015.81

Cited by 30 publications

(27 citation statements)

References 54 publications

(83 reference statements)

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“…Perhaps the closest works to ours are the ones by Beckman et al [12], Baluda et al [4][5][6] and Bardin et al [8] Other techniques attempt to combine infeasible test objectives detection techniques as a means to speed-up test generation and refine the coverage metric. Su et al [61] combines symbolic execution with model checking to generate data flow test inputs. Baluda et al [6] combines backward (using weakest precondition) and forward symbolic analysis to support branch testing and Bardin et al [8,9] combines weakest precondition with dynamic symbolic execution to support the coverage of structural test objectives.…”

Section: Related Work 81 Infeasible Structural Objectivesmentioning

confidence: 99%

Time to clean your test objectives

Marcozzi

Bardin²,

Kosmatov³

et al. 2018

Proceedings of the 40th International Conference on Software Engineering

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Testing is the primary approach for detecting software defects. A major challenge faced by testers lies in crafting efficient test suites, able to detect a maximum number of bugs with manageable effort. To do so, they rely on coverage criteria, which define some precise test objectives to be covered. However, many common criteria specify a significant number of objectives that occur to be infeasible or redundant in practice, like covering dead code or semantically equal mutants. Such objectives are well-known to be harmful to the design of test suites, impacting both the efficiency and precision of the tester's effort. This work introduces a sound and scalable technique to prune out a significant part of the infeasible and redundant objectives produced by a panel of white-box criteria. In a nutshell, we reduce this task to proving the validity of logical assertions in the code under test. The technique is implemented in a tool that relies on weakest-precondition calculus and SMT solving for proving the assertions. The tool is built on top of the Frama-C verification platform, which we carefully tune for our specific scalability needs. The experiments reveal that the pruning capabilities of the tool can reduce the number of targeted test objectives in a program by up to 27% and scale to real programs of 200K lines, making it possible to automate a painstaking part of their current testing process. CCS CONCEPTS • Software and its engineering → Software testing and debugging; • Theory of computation → Program analysis;

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Section: Related Work 81 Infeasible Structural Objectivesmentioning

confidence: 99%

Time to clean your test objectives

Marcozzi

Bardin²,

Kosmatov³

et al. 2018

Proceedings of the 40th International Conference on Software Engineering

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show abstract

“…Compared with them, MPI-SV extracts path-level models for verification. Furthermore, there exists work of combining symbolic execution and model checking [20,65,79]. YOGI [65] and Abstraction-driven concolic testing [20] combine dynamic symbolic execution [27,72] with counterexample-guided abstraction refinement (CEGAR) [16].…”

Section: Related Workmentioning

confidence: 99%

Combining symbolic execution and model checking to verify MPI programs

2018

Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

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Message passing is the standard paradigm of programming in highperformance computing. However, verifying Message Passing Interface (MPI) programs is challenging, due to the complex program features (such as non-determinism and non-blocking operations). In this work, we present MPI symbolic verifier (MPI-SV), the first symbolic execution based tool for automatically verifying MPI programs with non-blocking operations. MPI-SV combines symbolic execution and model checking in a synergistic way to tackle the challenges in MPI program verification. The synergy improves the scalability and enlarges the scope of verifiable properties. We have implemented MPI-SV 1 and evaluated it with 111 real-world MPI verification tasks. The pure symbolic execution-based technique successfully verifies 61 out of the 111 tasks (55%) within one hour, while in comparison, MPI-SV verifies 100 tasks (90%). On average, compared with pure symbolic execution, MPI-SV achieves 19x speedups on verifying the satisfaction of the critical property and 5x speedups on finding violations. CCS CONCEPTS• Software and its engineering → Software verification and validation;

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“…Symbolic execution [1,2] is a powerful method for software verification and validation, with applications in the processes of automatic mutation generation, data flow testing, and patch generation, among others [3][4][5]. A symbolic execution engine performs a dynamic pathbased analysis of a program, allowing it to explore a large percentage of that program's bounded behavior space.…”

Section: Introductionmentioning

confidence: 99%