Basic block coverage for search-based unit testing and crash reproduction

Basic block coverage for search-based unit testing and crash reproduction

Derakhshanfar, P. (TU Delft Software Engineering)
Devroey, Xavier (University of Namur)
Zaidman, A.E. (TU Delft Software Engineering)

2022

Search-based techniques have been widely used for white-box test generation. Many of these approaches rely on the approach level and branch distance heuristics to guide the search process and generate test cases with high line and branch coverage. Despite the positive results achieved by these two heuristics, they only use the information related to the coverage of explicit branches (e.g., indicated by conditional and loop statements), but ignore potential implicit branchings within basic blocks of code. If such implicit branching happens at runtime (e.g., if an exception is thrown in a branchless-method), the existing fitness functions cannot guide the search process. To address this issue, we introduce a new secondary objective, called Basic Block Coverage (BBC), which takes into account the coverage level of relevant basic blocks in the control flow graph. We evaluated the impact of BBC on search-based unit test generation (using the DynaMOSA algorithm) and search-based crash reproduction (using the STDistance and WeightedSum fitness functions). Our results show that for unit test generation, BBC improves the branch coverage of the generated tests. Although small (∼ 1.5%), this improvement in the branch coverage is systematic and leads to an increase of the output domain coverage and implicit runtime exception coverage, and of the diversity of runtime states. In terms of crash reproduction, in the combination of STDistance and WeightedSum, BBC helps in reproducing 3 new crashes for each fitness function. BBC significantly decreases the time required to reproduce 43.5% and 45.1% of the crashes using STDistance and WeightedSum, respectively. For these crashes, BBC reduces the consumed time by 71.7% (for STDistance) and 68.7% (for WeightedSum) on average.

Automated crash reproduction
Evolutionary algorithm
Search-based software testing
Secondary objective

http://resolver.tudelft.nl/uuid:5cb03dcf-de45-4271-8bc2-b4afde6ca74d

https://doi.org/10.1007/s10664-022-10155-0

1382-3256

Empirical Software Engineering, 27 (7)

Institutional Repository

journal article

© 2022 P. Derakhshanfar, Xavier Devroey, A.E. Zaidman