A systematic mapping of goal-oriented requirements engineering: reflections, impact, and the way forward

Goal-oriented Requirements Engineering describes the body of work which focuses on goals as the driving force motivating and capturing requirements, often using graphical goal models. A proliferation of work in this area inspired the study: “Goal-oriented requirements engineering: an extended systematic mapping study” first available in 2017, covering 246 publications in GORE up to 2015. In the current article, we have the pleasure of reflecting on this original mapping study. Using bibliometrics, we examine the study’s visibility, although such a quantitative view gives us only an approximation of real impact. The bibliometric view shows us that most citations are relatively recent: 85% of the citations have occurred since 2020. Unsurprisingly, the work has primarily been cited in requirements engineering venues, but also related venues such as the Conceptual Modeling conference (ER) and the Information and Software Technology Journal (IST). The work has been cited by a wide variety of authors. We estimate that roughly 40% of the publications citing the original SLR actually focus on goal models (have some variant of “goal” in the title), but that only roughly 53% of citing papers which are not themselves systematic reviews emphasize some form of empirical evaluation, 10% lower than the presence of empirical studies found in the original mapping study. Turning to trends and future directions, we reflect on the limitations of GORE uptake in industry, along with recommendations for continued use in education. We highlight new developments in using Large Language Models (LLMs) to generate goal models, including limitations, and reflect on what opportunities may be lost via full automation of goal model generation. We reflect on the potential role of GORE in the rising field of intent-driven engineering with AI, and the disconnect between much of this work and requirements-focused goal-oriented foundations. Finally, we consider the rising popularity of agentic AI, describing conceptual challenges and opportunities for GORE in terms of monitoring, capturing, and driving agentic system design. Overall, our reflection emphasizes that in the rise of LLMs and Agentic AI, the expressive power of GORE has great potential, and we invite the research community to explore and harness goal modeling for this purpose.