The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Feedback-Directed Random Test Generation
Abstract
We present a technique that improves random test generation by incorporating feedback obtained from executing test inputs as they are created. Our technique builds inputs incrementally by randomly selecting a method call to apply and finding arguments from among previously-constructed inputs. As soon as an input is built, it is executed and checked against a set of contracts and filters. The result of the execution determines whether the input is redundant, illegal, contract-violating, or useful for generating more inputs. The technique outputs a test suite consisting of unit tests for the classes under test. Passing tests can be used to ensure that code contracts are preserved across program changes; failing tests (that violate one or more contract) point to potential errors that should be corrected. Our experimental results indicate that feedback-directed random test generation can outperform systematic and undirected random test generation, in terms of coverage and error detection. On four small but nontrivial data structures (used previously in the literature), our technique achieves higher or equal block and predicate coverage than model checking (with and without abstraction) and undirected random generation. On 14 large, widely-used libraries (comprising 780KLOC), feedback-directed random test generation finds many previously-unknown errors, not found by either model checking or undirected random generation.
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Citations
Cites
JCrasher: an automatic robustness tester for Java
Csallner, C., & Smaragdakis, Y. (2004) · Software: Practice and Experience · doi:10.1002/spe.602 · Cited by 426
Cited by
(2021) · doi:10.1109/tse.2019.2946563 · Cited by 574
Software Testing With Large Language Models: Survey, Landscape, and Vision
Wang, J., Huang, Y., Chen, C., Liu, Z., Wang, S., & Wang, Q. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3368208 · Cited by 573
Fairness testing: testing software for discrimination
Galhotra, S., Brun, Y., & Meliou, A. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106277 · Cited by 405
“Automated testing research has produced tools to generate tests, including random testing, such as Randoop [63, 64], NightHawk [4], JCrasher [20], CarFast [65], and T3 [69]; search-based testing, such as EvoSuite [29], TestFul [9], and eToc [78]; dynamic symbolic execution tools, such as DSC [37], Symbolic PathFinder…”
MethodCodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models
Lemieux, C., Inala, J. P., Lahiri, S. K., & Sen, S. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00085 · Cited by 306
Self-citationExploring and Unleashing the Power of Large Language Models in Automated Code Translation
Yang, Z., Liu, F., Yu, Z., Keung, J. W., Li, J., Liu, S., Hong, Y., Ma, X., Jin, Z., & Li, G. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3660778 · Cited by 104
The future of AI-driven software engineering
Terragni, V., Vella, A., Roop, P., & Blincoe, K. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715003 · Cited by 62
Bhatia, S., Gandhi, T., Kumar, D., & Jalote, P. (2024) · Proceedings of the 1st International Workshop on Large Language Models for Code · doi:10.1145/3643795.3648396 · Cited by 55
Measuring code behavioral similarity for programming and software engineering education
Li, S., Xiao, X., Bassett, B., Xie, T., & Tillmann, N. (2016) · Proceedings of the 38th International Conference on Software Engineering Companion · doi:10.1145/2889160.2889204 · Cited by 30
Cyber-Physical Inconsistency Vulnerability Identification for Safety Checks in Robotic Vehicles
Choi, H., Kate, S., Aafer, Y., Zhang, X., & Xu, D. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3372297.3417249 · Cited by 27
Generating Unit Tests for Documentation
Nassif, M., Hernandez, A., Sridharan, A., & Robillard, M. P. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3087087 · Cited by 22
Automated infrastructure as code program testing
Sokolowski, D., Spielmann, D., & Salvaneschi, G. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3393070 · Cited by 20
Metadata
- Authors
- Pacheco, C., Lahiri, S. K., Ernst, M. D., & Ball, T.
- Venue
- 29th International Conference on Software Engineering (ICSE'07)
- Publisher
- IEEE
- Published
- 2007-05
- Pages
- 75-84
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2107709519
- Cited by
- 863
- References
- 1
- FWCI
- 22.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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