Decision support for global software development with pattern discovery

Background: Software development process nowadays is becoming more globalized than ever before. Global Software Development (GSD) implies that the software development process is spread across countries and geographic boundaries. GSD brings challenges to software project leaders / managers because of the increase in management difficulty. As a result, utilizing data mining and machine learning techniques to provide quantitative, objective and predictive solution for project management is essential. Aim: To facilitate software project management to make decisions by mining embedded knowledge from data and providing meaningful results. Method: In this paper we propose to adopt a pattern discovery technique which has been successfully applied in the field of computational Biology. The technique discovers association patterns inherited in the data which can provide insightful information for domain experts (e.g., project leaders), therefore increasing their confidence in making decisions. We apply the technique in the software defect datasets from the NASA MDP repository to predict whether a software project is defective or not and find out important factors in the data that signaled the prediction. Results: For the tested datasets, statistically significant patterns are produced with good classification performance. The experiment results also reveal the effect of different discretization techniques on performance. Conclusions: To the best of our knowledge, this is the first study to employ the specific pattern mining technique in Software Engineering for defective software detection and the results showed the potential of such a technique in which it can provide not only good classification results but also meaningful information for project leaders to make decisions.