Search-based Prediction of Fault-slip-through in Large Software Projects

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Afzal:2010:SSBSE,

• author = "Wasif Afzal and Richard Torkar and Robert Feldt and Greger Wikstrand",
• title = "Search-based Prediction of Fault-slip-through in Large Software Projects",
• booktitle = "Second International Symposium on Search Based Software Engineering (SSBSE 2010)",
• year = "2010",
• month = "7-9 " # sep,
• pages = "79--88",
• address = "Benevento, Italy",
• keywords = "genetic algorithms, genetic programming, gene expression programming, sbse, AIRS, GEP, GP, MR, PSO-ANN, artificial immune recognition system, artificial neural network, fault-slip-through, multiple regression, particle swarm optimisation, search-based prediction, software project, software testing process, artificial immune systems, fault tolerant computing, neural nets, particle swarm optimisation, program testing, regression analysis",
• DOI = "doi:10.1109/SSBSE.2010.19",
• isbn13 = "978-0-7695-4195-2",
• 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.",
• notes = "IEEE Computer Society Order Number P4195 BMS Part Number: CFP1099G-PRT Library of Congress Number 2010933544 http://ssbse.info/2010/program.php Also known as \cite{5635180}",

}

Genetic Programming entries for Wasif Afzal Richard Torkar Robert Feldt Greger Wikstrand

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