Feature location is the activity of identifying an initial location in the source code that implements functionality in a software system. Many feature location techniques have been introduced that automate some or all of this process, and a comprehensive overview of this large body of work would be beneficial to researchers and practitioners. This paper presents a systematic literature survey of feature location techniques. Eighty-nine articles from 25 venues have been reviewed and classified within the taxonomy in order to organize and structure existing work in the field of feature location. The paper also discusses open issues and defines future directions in the field of feature location.

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.

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 or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g., object-oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. 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 predicting software dependencies by exploiting the coupling present in domain-level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependencies without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 65 % of the source code dependencies and 77 % of the database dependencies are predicted solely based on domain