Comprehensible software fault and effort prediction: a data mining approach

Autor: Bart Baesens, David Martens, Enric Junqué de Fortuny, Julie Moeyersoms, Karel Dejaeger
Jazyk: angličtina
Rok vydání: 2015
Předmět:
Zdroj: Journal of systems and software
ISSN: 0164-1212
Popis: Software fault and effort prediction are important tasks to minimize costs of a software project. In software effort prediction the aim is to forecast the effort needed to complete a software project, whereas software fault prediction tries to identify fault-prone modules. In this research both tasks are considered, thereby using different data mining techniques. The predictive models not only need to be accurate but also comprehensible, demanding that the user can understand the motivation behind the model's prediction. Unfortunately, to obtain predictive performance, comprehensibility is often sacrificed and vice versa. To overcome this problem, we extract trees from well performing Random Forests (RFs) and Support Vector Machines for regression (SVRs) making use of a rule extraction algorithm ALPA. This method builds trees (using C4.5 and REPTree) that mimic the black-box model (RF, SVR) as closely as possible. The proposed methodology is applied to publicly available datasets, complemented with new datasets that we have put together based on the Android repository. Surprisingly, the trees extracted from the blackbox models by ALPA are not only comprehensible and explain how the blackbox model makes (most of) its predictions, but are also more accurate than the trees obtained by working directly on the data. publisher: Elsevier articletitle: Comprehensible software fault and effort prediction: A data mining approach journaltitle: Journal of Systems and Software articlelink: http://dx.doi.org/10.1016/j.jss.2014.10.032 content_type: article copyright: Copyright © 2014 Elsevier Inc. All rights reserved. ispartof: The Journal of Systems and Software vol:100 pages:80-90 status: published
Databáze: OpenAIRE
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