Modeling Pairwise Test Generation from Cause-Effect Graphs as a Boolean Satisfiability Problem
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A cause-effect graph considers only the desired external behavior of a system by identifying input-output parameter relationships in the specification. When testing a software system with cause-effect graphs, it is important to derive a moderate number of tests while avoiding loss in fault detection ability. Pairwise testing is known to be effective in determining errors while considering only a small portion of the input space. In this paper, we present a new testing technique that generates pairwise tests from a cause-effect graph. We use a Boolean Satisbiability (SAT) solver to generate pairwise tests from a cause-effect graph. The Alloy language is used for encoding the cause-effect graphs and its SAT solver is applied to generate the pairwise tests. Using a SAT solver allows us to effectively manage constraints over the input parameters and facilitates the generation of pairwise tests, even in the situations where other techniques fail to satisfy full pairwise coverage.
Abstract
A cause-effect graph considers only the desired external behavior of a system by identifying input-output parameter relationships in the specification. When testing a software system with cause-effect graphs, it is important to derive a moderate number of tests while avoiding loss in fault detection ability. Pairwise testing is known to be effective in determining errors while considering only a small portion of the input space. In this paper, we present a new testing technique that generates pairwise tests from a cause-effect graph. We use a Boolean Satisbiability (SAT) solver to generate pairwise tests from a cause-effect graph. The Alloy language is used for encoding the cause-effect graphs and its SAT solver is applied to generate the pairwise tests. Using a SAT solver allows us to effectively manage constraints over the input parameters and facilitates the generation of pairwise tests, even in the situations where other techniques fail to satisfy full pairwise coverage.
참고문헌 (13)
- G. J. Meyers, "The Art of Software Testing" (1979), John Wiley & Sons
- C. Nie, "A survey of Combinational testing", ACM Computing Surveys, 43(2) (2011)
- J. Kim, "White box pairwise test case generation", Proc. 7th International Conference on Quality Software, pp.289~291 (2007)
- G. Perrouin, "Automated and scalable t-wise test case generation strategies for software product lines", Proc. ICST 2010, pp.459~468 (2010)
- D. Jackson, "Software Abstractions" (2006), The MIT Press
- K. C. Tai, "A test generation strategy for pairwise testing", IEEE Trans. on Software Engineering, 28(1), pp.109~111 (2002)
- D. M. Cohen, "The AETG system : an approach to testing based on combinatorial design", IEEE Trans. on Software Engineering, 23(7), pp.437~444 (1997)
- Pedro Flores, "PWiseGen: generating test Cases for pairwise testing", Proc. the 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE 2011), pp.747~752 (2011)
- C. Lott, "Modeling requirements for combinatorial software testing", Proc. the 1st International Workshop on Advances in Model-based Testing, pp.1~7 (2005)
- M. Patil, "Pair-wise testing using simulated annealing", Proc. 2nd International Conference on Computer, Communication, Control and Information Technology (C3IT-2012), pp.25~26 (2012)
- M. B. Cohen, "Constructing interaction test suites for highly-configurable systems in the presence of constraints: a greedy approach", IEEE Transactions on Software Engineering, 34(5), pp.633~650 (2008)
- Julien Brunel, "Formal Modelling and Safety Analysis of an Avionic Functional Architecture with Alloy", Proc. ERTS2(Embedded Real Time Software and Systems) (2014)
- J. Czerwonka, "PICT-Pairwise testing in the real world:practical extensions to test-case scenarios", Proc. the 24th Pacific Northwest Software Quality Conference, pp.419~430 (2006)