An FIS for Early Detection of Defect Prone Modules

Abstract

Early prediction of defect prone modules helps in better resource planning, test planning and reducing the cost of defect correction in later stages of software lifecycle. Early prediction models based on design and code metrics are difficult to develop because precise values of the model inputs are not available. Conventional prediction techniques require exact inputs, therefore such models cannot always be used for early predictions. Innovative prediction methods that use imprecise inputs, however, can be applied to overcome the requirement of exact inputs. This paper presents a fuzzy inference system (FIS) that predicts defect proneness in software using vague inputs defined as fuzzy linguistic variables. The paper outlines the methodology for developing the FIS and applies the model to a real dataset. Performance analysis in terms of recall, accuracy, misclassification rate and a few other measures has been conducted resulting in useful insight to the FIS application. The FIS model predictions at an early stage have been compared with conventional prediction methods (i.e. classification trees, linear regression and neural networks) based on exact values. In case of the FIS model, the maximum and the minimum performance shortfalls were noticed for true negative rate (TN Rate) and F measure respectively. Whereas for Recall, the FIS model performed better than the other models even with the imprecise inputs.