Can We Predict Dependencies Using Domain information?

Abstract

Software dependencies play a vital role in program comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis, however, the source code is sometimes inaccessible, and not all stakeholders have adequate knowledge to perform such analysis. For example, non-technical domain experts and consultants raise most maintenance requests, however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predict software dependencies by exploiting coupling present in domain-level information. Our approach is independent of the software implementation, hence, it can be used to evaluate architectural dependencies without access to the source code or the database. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 68\% of the source code dependencies and 77\% of the database dependencies are predicted solely based on domain information.