Identifying Hidden Dependencies in Software Systems

Abstract

The maintenance and evolution of software systems are highly impacted by activities such as bug fixing, adding new features or functionalities and updating existing ones. Impact analysis contributes to improving the maintenance activities by determining those parts from a software system which can be affected by changes to the system. There exist hidden dependencies in the software projects which cannot be found using common coupling measures and are due to the so called indirect coupling. In this paper we aim to provide a comprehensive review of existing methods for hidden dependencies identification, as well as to highlight the limitations of the existing state-of-the-art approaches. We also propose an unsupervised learning based computational model for the problem of hidden dependencies identification and give some incipient experimental results. The study performed in this paper supports our broader goal of developing machine learning methods for automatically detecting hidden dependencies.