ACO based comprehensive model for software fault prediction

The comprehensive models can be used for software quality modelling which involves prediction of low-quality modules using interpretable rules. Such comprehensive model can guide the design and testing team to focus on the poor quality modules, thereby, limited resources allocated for software quality inspection can be targeted only towards modules that are likely to be defective. Ant Colony Optimization (ACO) based learner is one potential way to obtain rules that can classify the software modules faulty and not faulty. This paper investigates ACO based mining approach with ROC based rule quality updation to constructs a rule-based software fault prediction model with useful metrics. We have also investigated the effect of feature selection on ACO based and other benchmark algorithms. We tested the proposed method on several publicly available software fault data sets. We compared the performance of ACO based learning with the results of three benchmark classifiers on the basis of area under the receiver operating characteristic curve. The evaluation of performance measure proves that the ACO based learner outperforms other benchmark techniques.