Handling missing attributes using matrix factorization

Predictive models that use machine learning techniques has been useful tools to guide software project managers in making decisions under uncertainty. However in practice collecting metrics or defect data has been a troublesome job and researchers often have to deal with incomplete datasets in their studies. As a result both researchers and practitioners shy away from implementing such models. Missing data is a common problem in other domains to build recommender systems. We believe that the techniques used to overcome missing data problem in other domains can also be employed in software engineering. In this paper we propose Matrix Factorization algorithm to tackle with missing data problem in building predictive models in software development domain.