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Title:
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APPLICATION AND BENCHMARKING OF ARTIFICIAL IMMUNE SYSTEMS TO CLASSIFY FAULT-PRONE MODULES FOR SOFTWARE DEVELOPMENT PROJECTS |
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Author(s):
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Cagatay Catal , Banu Diri |
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ISBN:
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978-972-8924-30-0 |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Fault-prone module classification, artificial immune systems, AIRS, Immunos, clonal selection, metrics. |
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Type:
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Full Paper |
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First Page:
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347 |
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Last Page:
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354 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Software quality assurance is a crucial activity to enhance and ensure the quality of software development projects. Necessary budget, personnel and resource should be allocated for quality assurance activities to minimize the operational level faults by identifying faulty modules. In this study, Artificial Immune Systems (AIS) have been investigated to classify fault-prone modules. AIS algorithms such as AIRS1, AIRS2, AIRS2 Parallel, Immunos1, Immunos2, Immunos99, CSCA, and CLONALG have been applied and investigated by varying user-defined parameters to reach the best classification accuracy for KC2 dataset which is a part of NASA Metric Data Program. Furthermore, ROC (Receiver Operating Characteristic) analysis has been used in this study. |
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