Search-based Prediction of Fault-slip-through in Large Software Projects
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Afzal:2010:SSBSE,
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author = "Wasif Afzal and Richard Torkar and Robert Feldt and
Greger Wikstrand",
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title = "Search-based Prediction of Fault-slip-through in Large
Software Projects",
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booktitle = "Second International Symposium on Search Based
Software Engineering (SSBSE 2010)",
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year = "2010",
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month = "7-9 " # sep,
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pages = "79--88",
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address = "Benevento, Italy",
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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",
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DOI = "doi:10.1109/SSBSE.2010.19",
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isbn13 = "978-0-7695-4195-2",
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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.",
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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
Citations