Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction

Abstract

Software fault prediction (SFP) is a complex problem that meets developers in the software development life cycle. Collecting data from real software projects, either while the development life cycle or after lunch the product, is not a simple task, and the collected data may suffer from imbalance data distribution problem. In this research, we proposed an Enhanced Binary Moth Flame Optimization (EBMFO) with Adaptive synthetic sampling (ADASYN) to predict software faults. BMFO is employed as a wrapper feature selection, while ADASYN enhances the input dataset and address the imbalanced dataset. Converting MFO algorithm from a continues version to the binary version using transfer functions (TFs) from two different groups (S-shape and V-shape) is investigated in this work and proposed an EBFMFO version. Fifteen real projects data obtained from PROMISE repository are employed in this work. Three different classifiers are used: the k-nearest neighbors (k-NN), Decision Trees (DT), and Linear discriminant analysis (LDA). The reported results demonstrate that the proposed EBMFO enhances the overall performance of classifiers and outperforms the results in the literature and show the importance of TF for feature selection algorithms.