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. I.

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. I.