Search-based prediction of fault-slip-through in large software projects

Document type: Conference Papers
Peer reviewed: Yes
Author(s): Wasif Afzal, Richard Torkar, Robert Feldt, Greger Wikstrand
Title: Search-based prediction of fault-slip-through in large software projects
Conference name: 2nd International Symposium on Search Based Software Engineering
Year: 2010
Publisher: IEEE
City: Benevento
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/com
Authors e-mail: wasif.afzal@bth.se, richard.torkar@bth.se, robert.fedlt@bth.se
Language: English
Abstract: A large percentage of the cost of rework can be avoided by finding 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 five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial 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 finding 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 fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, 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.
Subject: Software Engineering\General
Computer Science\Artificial Intelligence
Keywords: Faults-slip-through, Prediction, Search-based
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