AN INVESTIGATION ON THE USE OF MACHINE LEARNED MODELS FOR ESTIMATING SOFTWARE CORRECTABILITY

DE ALMEIDA, MAURICIO A.; LOUNIS, HAKIM et MELO, WALCELIO L. (1999). « AN INVESTIGATION ON THE USE OF MACHINE LEARNED MODELS FOR ESTIMATING SOFTWARE CORRECTABILITY ». International Journal of Software Engineering and Knowledge Engineering, 09(05), pp. 565-593.

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Résumé

In this paper we present the results of an empirical study in which we have investigated Machine Learning (ML) algorithms with regard to their capabilities to accurately assess the correctability of faulty software components. Three different families algorithms have been analyzed: divide and conquer (top down induction decision tree), covering, and inductive logic programming (ILP). We have used (1) fault data collected on corrective maintenance activities for the Generalized Support Software reuse asset library located at the Flight Dynamics Division of NASA's GSFC and (2) product measures extracted directly from the faulty components of this library. In our data set, the software quality models generated by both C4.5-rules (a divide and conquer algorithm) and FOIL (an inductive logic programming one) presented the best results from the point of view of model accuracy.

Type:	Article de revue scientifique
Mots-clés ou Sujets:	Software correctability; machine learning algorithms; predictive software quality model building
Unité d'appartenance:	Faculté des sciences > Département d'informatique
Déposé par:	Hakim Lounis
Date de dépôt:	21 avr. 2016 12:56
Dernière modification:	27 avr. 2016 18:36
Adresse URL :	http://archipel.uqam.ca/id/eprint/8213

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