The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
The Current State and Future of Search Based Software Engineering
Abstract
This paper describes work on the application of optimization techniques in software engineering. These optimization techniques come from the operations research and metaheuristic computation research communities. The paper briefly reviews widely used optimization techniques and the key ingredients required for their successful application to software engineering, providing an overview of existing results in eight software engineering application domains. The paper also describes the benefits that are likely to accrue from the growing body of work in this area and provides a set of open problems, challenges and areas for future work.
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Cites
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Ant colony optimization theory: A survey
Dorigo, M., & Blum, C. (2005) · Theoretical Computer Science · doi:10.1016/j.tcs.2005.05.020 · Cited by 2,295
Search‐based software test data generation: a survey
McMinn, P. (2004) · Software Testing, Verification and Reliability · doi:10.1002/stvr.294 · Cited by 1,368
Prioritizing test cases for regression testing
Rothermel, G., Untch, R. H., Chu, C., & Harrold, M. J. (2001) · IEEE Transactions on Software Engineering · doi:10.1109/32.962562 · Cited by 1,338
From recombination of genes to the estimation of distributions I. Binary parameters
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Estimating software project effort using analogies
Shepperd, M., & Schofield, C. (1997) · IEEE Transactions on Software Engineering · doi:10.1109/32.637387 · Cited by 954
Search-based software engineering
Harman, M., & Jones, B. F. (2001) · Information and Software Technology · doi:10.1016/s0950-5849(01)00189-6 · Cited by 914
Self-citation
Cited by
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
Self-citationGenProg: A Generic Method for Automatic Software Repair
Le Goues, C., Nguyen, T., Forrest, S., & Weimer, W. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.104 · Cited by 1,085
FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage
Lemieux, C., & Sen, K. (2018) · Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering · doi:10.1145/3238147.3238176 · Cited by 477
The ManyBugs and IntroClass benchmarks for automated repair of C programs
Le Goues, C., Holtschulte, N., Smith, E. K., Brun, Y., Devanbu, P., Forrest, S., & Weimer, W. (2015) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2015.2454513 · Cited by 326
Is the cure worse than the disease? overfitting in automated program repair
Smith, E. K., Barr, E. T., Le Goues, C., & Brun, Y. (2015) · Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering · doi:10.1145/2786805.2786825 · Cited by 323
Metadata
- Authors
- Harman, M.
- Venue
- Future of Software Engineering (FOSE '07)
- Publisher
- IEEE
- Published
- 2007-05
- Pages
- 342-357
- OpenAlex
- W2146136779
- Cited by
- 631
- References
- 48
- FWCI
- 38.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
