A Feature Selection Based Model for Software Defect Prediction

Software is a complex entity composed in various modules with varied range of defect occurrence possibility. Efficient and timely prediction of defect occurrence in software allows software project managers to effectively utilize people, cost, time for better quality assurance. The presence of defects in a software leads to a poor quality software and also responsible for the failure of a software project. Sometime it is not possible to identify the defects and fixing them at the time of development and it is required to handle such defects any time whenever they are noticed by the team members. So it is important to predict defect-prone software modules prior to deployment of software project in order to plan better maintenance strategy. Early knowledge of defect prone software module can also help to make efficient process improvement plan within justified period of time and cost. This can further lead to better software release as well as high customer satisfaction subsequently. Accurate measurement and prediction of defect is a crucial issue in any software because it is an indirect measurement and is based on several metrics. Therefore, instead of considering all the metrics, it would be more appropriate to find out a suitable set of metrics which are relevant and significant for prediction of defects in any software modules. This paper proposes a feature selection based Linear Twin Support Vector Machine (LSTSVM) model to predict defect prone software modules. F-score, a feature selection technique, is used to determine the significant metrics set which are prominently affecting the defect prediction in a software modules. The efficiency of predictive model could be enhanced with reduced metrics set obtained after feature selection and further used to identify defective modules in a given set of inputs. This paper evaluates the performance of proposed model and compares it against other existing machine learning models. The experiment has been performed on four PROMISE software engineering repository datasets. The experimental results indicate the effectiveness of the proposed feature selection based LSTSVM predictive model on the basis standard performance evaluation parameters.