Mining defect reports for predicting software maintenance effort

Software Maintenance is the crucial phase of software development lifecycle, which begins once the software has been deployed at the customer´s site. It is a very broad activity and includes almost everything that is done to change the software if required, to keep it operational after its delivery at the customer´s end. A lot of maintenance effort is required to change the software after it is in operation. Therefore, predicting the effort and cost associated with the maintenance activities such as correcting and fixing the defects has become one of the key issues that need to be analyzed for effective resource allocation and decision-making. In view of this issue, we have developed a model based on text mining techniques using the statistical method namely, Multi-nominal Multivariate Logistic Regression (MMLR). We apply text mining techniques to identify the relevant attributes from defect reports and relate these relevant attributes to software maintenance effort prediction. The proposed model is validated using `Camera´ application package of Android Operating System. Receiver Operating Characteristics (ROC) analysis is done to interpret the results obtained from model prediction by using the value of Area Under the Curve (AUC), sensitivity and a suitable threshold criterion known as the cut-off point. It is evident from the results that the performance of the model is dependent on the number of words considered for classification and therefore shows the best results with respect to top-100 words. The performance is irrespective of the type of effort category.