onr:"swepub:oai:research.chalmers.se:5d0c9d8e-1b85-4bd5-90bd-c6540332b0f5"
Search: onr:"swepub:oai:research.chalmers.se:5d0c9d8e-1b85-4bd5-90bd-c6540332b0f5" > Learning How to Sea...
- 1 of 1
- Previous record
- Next record
- To hitlist
- Almulla, HusseinUniversity of South Carolina (author)
Learning How to Search: Generating Exception-Triggering Tests Through Adaptive Fitness Function Selection
- Article/chapterEnglish2020
Publisher, publication year, extent ...
- Porto, Portugal :IEEE,2020
- electronicrdacarrier
Numbers
- LIBRIS-ID:oai:research.chalmers.se:5d0c9d8e-1b85-4bd5-90bd-c6540332b0f5
- https://research.chalmers.se/publication/519497URI
- https://research.chalmers.se/publication/518608URI
- https://doi.org/10.1109/ICST46399.2020.00017DOI
- https://gup.ub.gu.se/publication/295408URI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Notes
- Search-based test generation is guided by feedback from one or more fitness functions—scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals—such as forcing the class-under-test to throw exceptions— do not have a known fitness function formulation. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current population of test suites. To test this hypothesis, we have implemented two reinforcement learning algorithms in the EvoSuite framework, and used these algorithms to dynamically set the fitness functions used during generation.We have evaluated our framework, EvoSuiteFIT, on a set of 386 real faults. EvoSuiteFIT discovers and retains more exception-triggering input and produces suites that detect a variety of faults missed by the other techniques. The ability to adjust fitness functions allows EvoSuiteFIT to make strategic choices that efficiently produce more effective test suites.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Programvaruteknik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Software Engineering hsv//eng
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- Reinforcement Learning
- Search-Based Software Engineering
- Automated Test Generation
- Automated Test Generation
- Search-Based Software Engineering
- Reinforcement Learning
Added entries (persons, corporate bodies, meetings, titles ...)
- Gay, Gregory,1987Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU)(Swepub:gu)xgagre (author)
- University of South CarolinaInstitutionen för data- och informationsteknik (GU) (creator_code:org_t)
Related titles
- In:Proceedings - 2020 IEEE 13th International Conference on Software Testing, Verification and Validation, ICST 2020Porto, Portugal : IEEE, s. 63-73
Internet link
- https://research.chalmers.se/publication/519497/file/519497_Fulltext.pdfFULLTEXT
- https://gup.ub.gu.se/publication/295408FULLTEXT
- https://research.chalmers.se/publication/519497
- https://research.chalmers.se/publication/518608
- https://doi.org/10.1109/ICST46399.2020.00017
- https://gup.ub.gu.se/publication/295408
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Almulla, Hussein
- Gay, Gregory, 19 ...
- About the subject
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Computer and Inf ...
- and Software Enginee ...
-
- NATURAL SCIENCES
- NATURAL SCIENCES
- and Computer and Inf ...
- and Computer Science ...
- Articles in the publication
- Proceedings - 20 ...
- By the university
- Chalmers University of Technology
- University of Gothenburg
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
