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{{Short description|Application of metaheuristic search techniques to software engineering}}
{{Use dmy dates|date=December 2020}}
'''Search-based software engineering''' ('''SBSE''') applies [[metaheuristic]] search techniques such as [[genetic algorithms]], [[simulated annealing]] and [[tabu search]] to [[software engineering]] problems. Many activities in [[software engineering]] can be stated as [[Optimization (mathematics)|optimization]] problems. [[Optimization (mathematics)|Optimization]] techniques of [[operations research]] such as [[linear programming]] or [[dynamic programming]] are often impractical for large scale [[software engineering]] problems because of their [[Computational complexity theory|computational complexity]] or their assumptions on the problem structure. Researchers and practitioners use [[metaheuristic]] search techniques, which impose little assumptions on the problem structure, to find near-optimal or "good-enough" solutions.<ref>{{Cite journal |last1=Mohan |first1=M. |last2=Greer |first2=D. |date=2019-08-01 |title=Using a many-objective approach to investigate automated refactoring |url=https://www.sciencedirect.com/science/article/pii/S0950584919300916 |journal=Information and Software Technology |volume=112 |pages=83–101 |doi=10.1016/j.infsof.2019.04.009 |issn=0950-5849}}</ref>▼
▲'''Search-based software engineering''' ('''SBSE''') applies [[metaheuristic]] search techniques such as [[genetic algorithms]], [[simulated annealing]] and [[tabu search]] to [[software engineering]] problems. Many activities in [[software engineering]] can be stated as [[Optimization (mathematics)|optimization]] problems. [[Optimization (mathematics)|Optimization]] techniques of [[operations research]] such as [[linear programming]] or [[dynamic programming]] are often impractical for large scale [[software engineering]] problems because of their [[Computational complexity theory|computational complexity]] or their assumptions on the problem structure. Researchers and practitioners use [[metaheuristic]] search techniques, which impose little assumptions on the problem structure, to find near-optimal or "good-enough" solutions.
SBSE problems can be divided into two types:
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==Definition==
SBSE converts a software engineering problem into a computational search problem that can be tackled with a [[metaheuristic]]. This involves defining a search space, or the set of possible solutions. This space is typically too large to be explored exhaustively, suggesting a [[metaheuristic]] approach. A metric
{{Cite conference
| doi = 10.1109/METRIC.2004.1357891
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| journal = [[IEE Proceedings - Software]]
| year = 2003
| doi-broken-date = 12 July 2025
| citeseerx = 10.1.1.144.3059
}}</ref>
The term "[[search-based application]]", in contrast, refers to using [[search
==Brief history==
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| year = 2004
| citeseerx = 10.1.1.122.33
| s2cid = 17408871
}}</ref> Search techniques have been applied to other [[software engineering]] activities, for instance, [[requirements analysis]],<ref>
{{Cite journal
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| date = 2004-03-15
| citeseerx = 10.1.1.195.321
| s2cid = 710923
}}</ref><ref>{{Cite conference
| doi = 10.1109/SBES.2009.23
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Y. Zhang and M. Harman and S. L. Lim, "[http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/images/Research_Student_Information/RN_11_12.pdf Search Based Optimization of Requirements Interaction Management]," Department of Computer Science, University College London, Research Note RN/11/12, 2011.
</ref>
<ref>{{cite book|last1=Li|first1=Lingbo|last2=Harman|first2=Mark|last3=Letier|first3=Emmanuel|last4=Zhang|first4=Yuanyuan|title
===Debugging and maintenance===
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| pages = 3–13
| last1 = Le Goues
| first1 = Claire | author1-link = Claire Le Goues
| last2 = Dewey-Vogt
| first2 = Michael
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| book-title = IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)
| year = 2008
| citeseerx = 10.1.1.159.7991
}}</ref>
===Testing===
Search-based software engineering has been applied to software testing, including the automatic generation of test cases (test data), test case minimization and test case prioritization.<ref>{{Cite
===Optimizing software===
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| url = http://www0.cs.ucl.ac.uk/staff/w.langdon/ftp/papers/Langdon_2013_ieeeTEC.pdf
}}</ref>
A recent work by Basios et al. shows that by optimising the data structure, Google Guava found a 9% improvement
===Project management===
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==Tools==
Tools available for SBSE include OpenPAT
{{cite
|last1 = Mayo
|first1 = M.
|last2 = Spacey
|first2 = S.
|title = Search Based Software Engineering
|
|series = Lecture Notes in Computer Science
|volume = 8084
|pages = 158–171
|year = 2013
|doi = 10.1007/978-3-642-39742-4_13
|chapter-url= https://researchcommons.waikato.ac.nz/bitstream/10289/7763/1/SBSE13.pdf
|hdl= 10289/7763
|isbn = 978-3-642-39741-7
|hdl-access= free
}}</ref>
</ref>
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| pages = 421–443
| last1 = Le Goues
| first1 = Claire | author1-link = Claire Le Goues
| last2 = Forrest
| first2 = Stephanie
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{{Cite conference
| publisher = IEEE Press
| conference = First International Workshop on Combining Modelling with Search-Based Software Engineering, First International Workshop on Combining Modelling with Search-Based Software Engineering
| pages = 49–50
| last = Simons
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*[https://scholar.google.co.uk/citations?view_op=search_authors&hl=en&mauthors=label:sbse Google Scholar page on Search-based software engineering]
[[Category:Computer-related introductions in 2001]]
▲[[Category:Software engineering]]
[[Category:Software testing]]
[[Category:Search algorithms]]
[[Category:Optimization algorithms and methods]]
[[Category:
[[Category:Software quality]]
[[Category:Program analysis]]
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