Predicting Fault Proneness of Classes Trough a Multiobjective Particle Swarm Optimization Algorithm

Authors:
Andre B. de Carvalho;Aurora Pozo;Silvia Vergilio;Alexandre Lenz
Affiliations:
-;-;-;-
Venue:
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Year:
2008

Citing 0
Cited 4

A symbolic fault-prediction model based on multiobjective particle swarm optimization

Journal of Systems and Software
Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Advances in Engineering Software
Comparing the performance of fault prediction models which report multiple performance measures: recomputing the confusion matrix

Proceedings of the 8th International Conference on Predictive Models in Software Engineering
DConfusion: a technique to allow cross study performance evaluation of fault prediction studies

Automated Software Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Software testing is a fundamental software engineering activity for quality assurance that is also traditionally very expensive. To reduce efforts of testing strategies, some design metrics have been used to predict the fault-proneness of a software class or module. Recent works have explored the use of Machine Learning (ML) techniques for fault prediction. However most used ML techniques can not deal with unbalanced data and their results usually have a difficult interpretation. Because of this, this paper introduces a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for fault prediction. It allows the creation of classifiers composed by rules with specific properties by exploring Pareto dominance concepts. These rules are more intuitive and easier to understand because they can be interpreted independently one of each other. Furthermore, an experiment using the approach is presented and the results are compared to the other techniques explored in the area.