A Comparative Study Of Attribute Weighting Techniques For Software Defect Prediction Using Case-Based Reasoning

Software defect prediction is an acknowledged approach used to achieve better product quality and to better utilize resources needed for that. One known method for predicting the number of defects is applying case-based reasoning (CBR). In this paper, different attribute weighting techniques for CBR-based defect prediction are analyzed. Two weighting techniques are compared with the case of applying uniform weights. The first one called SANN is based on sensitivity analysis of the impact of attributes as part of neural network analysis. The second one is multi-linear regression called MLR.Evaluation of the accuracy of the overall method for applying the three different weighting techniques is done over five data sets comprising in total about 5000 modules from NASA. Two quality measures are applied: Average absolute error AAE and average relative error ARE. In addition to the variation of weighting techniques, the impact of varying the number of nearest neighbours is studied.The three main results of the empirical analysis are: (i) In the majority of cases, SANN achieves the most accurate results, (ii) uniform weighting performs better than the MLR-based weighting heuristic, and (iii) there is no significant preference pattern for the different number of similar objects applied in CBR.