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Search-based prediction of fault-slip-through in large software projects
Mälardalens högskola, Akademin för innovation, design och teknik. Blekinge Institute of Technology. (IS (Embedded Systems))ORCID-id: 0000-0003-0611-2655
Blekinge Institute of Technology.
Blekinge Institute of Technology.
KnowIT YAHM Sweden AB.
2010 (engelsk)Inngår i: Proceedings - 2nd International Symposium on Search Based Software Engineering, SSBSE 2010, 2010, s. 79-88Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

A large percentage of the cost of rework can be avoided by ?nding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of ?ve different techniques, namely particle swarm optimization based arti?cial neural networks (PSO-ANN), arti?cial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards ?nding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of ?t and absolute relative error measures. They indicate that the four search-based techniques (PSOANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.

sted, utgiver, år, opplag, sider
2010. s. 79-88
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-22276DOI: 10.1109/SSBSE.2010.19Scopus ID: 2-s2.0-79952053759ISBN: 9780769541952 (tryckt)OAI: oai:DiVA.org:mdh-22276DiVA, id: diva2:661359
Konferanse
2nd International Symposium on Search Based Software Engineering, SSBSE 2010; Benevento; Italy; 7 October 2010 through 9 October 2010
Prosjekter
Project_ExternalTilgjengelig fra: 2013-11-03 Laget: 2013-10-31 Sist oppdatert: 2013-12-03bibliografisk kontrollert

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