The DOI matches its Crossref record; 193 of the 193 references it cites were found in a registry or an index.
Mutation Testing Advances: An Analysis and Survey
Open accessAbstract
Mutation testing realizes the idea of using artificial defects to support testing activities. Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the generation of test cases, and to support experimentation. Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a “mini-handbook”-style roadmap for the application of mutation testing as experimental methodology.
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KLEE: unassisted and automatic generation of high-coverage tests for complex systems programs
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An Analysis and Survey of the Development of Mutation Testing
Jia, Y., & Harman, M. (2011) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2010.62 · Cited by 1,745
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Large language models for software engineering: survey and open problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650
Self-citationLarge Language Models for Software Engineering: Survey and Open Problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · arXiv · doi:10.48550/arxiv.2310.03533 · Cited by 523
Self-citationDeep learning library testing via effective model generation
Wang, Z., Yan, M., Chen, J., Liu, S., & Zhang, D. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3409761 · Cited by 167
Effective test generation using pre-trained Large Language Models and mutation testing
Dakhel, A. M., Nikanjam, A., Majdinasab, V., Khomh, F., & Desmarais, M. C. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107468 · Cited by 157
An Autocorrelation-based LSTM-Autoencoder for Anomaly Detection on Time-Series Data
Homayouni, H., Ghosh, S., Ray, I., Gondalia, S., Duggan, J., & Kahn, M. G. (2020) · 2020 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata50022.2020.9378192 · Cited by 66
Metadata
- Venue
- Advances in Computers
- Publisher
- Elsevier
- Published
- 2019
- Pages
- 275-378
- ISSN
- 0065-2458 (ISSN-L)
- OpenAlex
- W2725449579
- Cited by
- 425
- References
- 193
- FWCI
- 230.77
- Type
- book chapter
- Language
- en
- Open access
- green
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