Machine Learning Techniques to Predict Software Defect

Machine learning techniques have been dominating in the last two decades. The recently published comprehensive state-of-the-art review (Mohanty et al., 2010) justifies this issue. The ability of software quality models to accurately identify critical faulty components allows for the application of focused verification activities ranging from manual inspection to automated formal analysis methods. Therefore, software quality models to ensure the reliability of the delivered products. Accurate prediction of fault prone modules enables the verification and validation activities that includes quality models: Musa, 1998, logistic regression (Basili et al., 1996), discriminant analysis (Khoshgoftaar, 1996), the discriminant power techniques (Schneidewind, 1992), artificial neural network (Khoshgoftaar, 1995), genetic algorithm (Azar et al., 2002), and classification trees (Gokhale et al., 1997; Khoshgoftar et al., 2002; Selby et al., 1988; Fenton et al., 1999). A wide range of modeling techniques has been proposed and applied for software quality predictions. These include: proposed the Bayesian belief network as the most effective model to predict software quality. Classification is a popular approach to predict software defects and involves categorizing modules, which is represented by a set of metrics or code attributes into fault prone (fp) non fault prone (nfp) by means of a classification model derived from data (Lessman et al., 2008), statistical methods (Basili et al., 1996; Khoshgoftar & Allen, 1999), tree based methods, (Guo et al., 2004; Khoshgoftar et al., 2000; Menzies et al., 2004; Porter et al., 1990; Selby et al., 1988), neural networks (Khoshgoftar et al., 1995, 1997) and analogy based approaches (El-Emam et al., 2001; Ganeshan et al., 2000; Khoshgoftar et al., 2003), Decision tree (Selby et al., 1988). The discriminative power techniques correctly classified 75 out of 81 fault free modules, and 21 out of 31 faulty modules (Porter et al., 1992). Lessmann et al., (2008) used 10 software development datasets from NASA MDP repository to predict software defects. Most recently, Pendharkar (2010) used the same dataset to test the efficacy of their hybrid exhaustive search and probabilistic neural network (PNN), and simulated annealing (SA) method. In this chapter, we present a software defect prediction methodology based on GP, BPNN, GMDH, PNN, GRNN, TreeNet, CART, Random Forest Naïve Baye’s and J48 on the DATATRIEVE, PC1, PC3, PC4, MC1, KC1, KC2, KC3, CM1 and JM1 datasets. The rest of the chapter is organized in the following manner. A brief discussion about the overview of machine learning techniques is presented in section 2. Section 3 describes the experimental methodology. Section 4 presents a detailed discussion of the results and discussions. Finally, section 5 concludes the chapter. Ramakanta Mohanty Keshav Memorial Institute of Technology, India