Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2020
Abstract
In Search-based Software Testing (SBST), test generation is guided by fitness functions that estimate how close a test case is to reach an uncovered test goal (e.g., branch). A popular fitness function estimates how close conditional statements are to evaluating to true or false, i.e., the branch distance. However, when conditions read Boolean variables (e.g., if(x && y)), the branch distance provides no gradient for the search, since a Boolean can either be true or false. This flag problem can be addressed by transforming individual procedures such that Boolean flags are replaced with numeric comparisons that provide better guidance for the search. Unfortunately, defining a semantics-preserving transformation that is applicable in an interprocedural case, where Boolean flags are passed around as parameters and return values, is a daunting task. Thus, it is not yet supported by modern test generators. This work is based on the insight that fitness gradients can be recovered by using runtime information: Given an uncovered interprocedural flag branch, our approach (1) calculates context-sensitive branch distance for all control flows potentially returning the required flag in the called method, and (2) recursively aggregates these distances into a continuous value. We implemented our approach on top of the EvoSuite framework for Java, and empirically compared it with state-of-the-art testability transformations on 807 non-trivial methods suffering from interprocedural flag problems, sampled from 150 open source Java projects. Our experiment demonstrates that our approach achieves higher coverage on the subject methods with statistical significance and acceptable runtime overheads.
Keywords
Program analysis, search-based, testability, testing
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ISSTA '20: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual, July 18-22
First Page
440
Last Page
451
ISBN
9781450380089
Identifier
10.1145/3395363.3397358
Publisher
ACM
City or Country
New York
Embargo Period
5-24-2021
Citation
LIN, Yun; SUN, Jun; FRASER, Gordon; XIU, Ziheng; LIU, Ting; and DONG, Jin Song.
Recovering fitness gradients for interprocedural Boolean flags in search-based testing. (2020). ISSTA '20: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual, July 18-22. 440-451.
Available at: https://ink.library.smu.edu.sg/sis_research/5960
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3395363.3397358