Abstract Requirements traceability (RT) is the ability to link requirements to other software development artifacts. In pre-requirements (pre-RS) traceability, requirements are linked to their origin, such as interviews with stakeholders, meeting protocols, or legacy systems. Compared with post-RS traceability, which links requirements to source code and other later artifacts, pre-RS traceability has seen much less research. This article presents a systematic literature review of pre-RS traceability based on 77 articles published between 1992 and 2022, aiming to provide a comprehensive overview of its use cases, benefits, problems, and solutions. Through the analysis of existing literature, this review identifies gaps for future research and establishes a foundation for future investigations in the field of pre-RS traceability.

Requirements traceability is the ability to trace requirements to other software engineering artifacts. Traceability can be classified as either pre- or post-requirements specifications (RS) traceability. Pre-RS traceability is the ability to trace between requirements and their origin. However, the benefits of pre-RS traceability are often not clear. In this article, we systematically lay out the benefits of pre-RS traceability. We present results from both a literature review and a qualitative survey of practitioners involved with documenting and utilizing such trace links. We find that the benefits strongly depend on the practitioners, their tasks, and the project environment. Awareness of these relationships supports a clearer understanding of the benefits of pre-RS traceability and thus motivates successful implementation of the required practices. The results of our research motivates the adoption of pre-RS traceability and present problem areas for future research.

Abstract Using qualitative data analysis (QDA) to perform domain analysis and modeling has shown great promise. Yet, the evaluation of such approaches has been limited to single-case case studies. While these exploratory cases are valuable for an initial assessment, the evaluation of the efficacy of QDA to solve the suggested problems is restricted by the common single-case case study research design. Using our own method, called QDAcity-RE, as the example, we present an in-depth empirical evaluation of employing qualitative data analysis for domain modeling using a controlled experiment design. Our controlled experiment shows that the QDA-based method leads to a deeper and richer set of domain concepts discovered from the data, while also being more time efficient than the control group using a comparable non-QDA-based method with the same level of traceability.