Search-based software engineering: Difference between revisions

Revision as of 15:19, 23 December 2020

Revision as of 13:56, 13 January 2021

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| first = Mark

| title = Why Source Code Analysis and Manipulation Will Always be Important

| booktitlebook-title = 10th IEEE Working Conference on Source Code Analysis and Manipulation (SCAM 2010)

| year = 2010

}}</ref>

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|author2=John A. Clark

| title = Metrics are fitness functions too

| booktitlebook-title = Proceedings of the 10th International Symposium on Software Metrics, 2004

| year = 2004

}}</ref> (also called a fitness function, cost function, objective function or quality measure) is then used to measure the quality of potential solutions. Many software engineering problems can be reformulated as a computational search problem.<ref>{{Cite journal

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| first5 = Geraldo R.

| title = A New Approach to the Software Release Planning

| booktitlebook-title = XXIII Brazilian Symposium on Software Engineering, 2009. SBES '09

| year = 2009

}}</ref> [[software design|design]],<ref>

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| first3 = Mark

| title = Search-based techniques applied to optimization of project planning for a massive maintenance project

| booktitlebook-title = Proceedings of the 21st IEEE International Conference on Software Maintenance, 2005. ICSM'05

| year = 2005

| citeseerx = 10.1.1.63.8069

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| first4 = Westley

| title = A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each

| booktitlebook-title = 2012 34th International Conference on Software Engineering (ICSE)

| year = 2012

}}</ref>

Line 239:

| first2 = Xin

| title = A novel co-evolutionary approach to automatic software bug fixing

| booktitlebook-title = IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)

| year = 2008

}}</ref>

Line 273:

| first3 = Xin

| title = Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design

| booktitlebook-title = Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference

| ___location = New York, NY, USA

| series = GECCO '12

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Tools available for SBSE include OpenPAT.<ref>

{{cite conference

|ref = harv

|last1 = Mayo

|first1 = M.

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|doi = 10.1007/978-3-642-39742-4_13

|url= https://researchcommons.waikato.ac.nz/bitstream/10289/7763/1/SBSE13.pdf

}}</ref> and [[EvoSuite]] <ref>(http://www.evosuite.org/)</ref> and [https://coverage.readthedocs.io/ Coverage], a code coverage measurement tool for Python<ref>{{Citation|last=others|first=Ned Batchelder and 100|title=coverage: Code coverage measurement for Python|url=https://bitbucket.org/ned/coveragepy|accessdateaccess-date=2018-03-14}}

</ref>

Line 313 ⟶ 312:

|date = 30 December 2018

|website = VentureBeat

|accessdateaccess-date = 29 September 2020

}}</ref>

Line 324 ⟶ 323:

|date = 18 October 2013

|website = The Shape of Code

|accessdateaccess-date = 31 October 2013

}}

</ref> In the context of SBSE use in fixing or improving programs, developers need to be confident that any automatically produced modification does not generate unexpected behavior outside the scope of a system's requirements and testing environment. Considering that fully automated programming has yet to be achieved, a desirable property of such modifications would be that they need to be easily understood by humans to support maintenance activities.<ref>

Line 356 ⟶ 355:

| title = Whither (away) software engineers in SBSE?

| ___location = San Francisco, USA

| accessdateaccess-date = 2013-10-31

| date = May 2013

| url = http://eprints.uwe.ac.uk/19938/