Designing a Requirement Mining System

TLDR

Accurate requirements are critical for IS success, yet most are captured in informal natural‑language documents, making them difficult to process because of ambiguity, inconsistency, and incompleteness, which challenges even experienced engineers. This study proposes a design theory for requirement mining systems that combines semi‑automatic mining and the use of imported and retrieved knowledge. We implemented the REMINER prototype, which applies the theory to automatically identify and classify natural‑language requirements, enabling evaluation of its viability and conceptual soundness. Evaluation shows that the RMS improves recall significantly while preserving precision.

Abstract

The success of information systems (IS) development strongly depends on the accuracy of the requirements gathered from users and other stakeholders. When developing a new IS, about 80 percent of these requirements are recorded in informal requirements documents (e.g., interview transcripts or discussion forums) using natural language. However, processing the resultant natural language requirements resources is inherently complex and often error prone due to ambiguity, inconsistency, and incompleteness. Thus, even highly qualified requirements engineers often struggle to process large amounts of natural language requirements resources efficiently and effectively. In this paper, we propose a design theory for requirement mining systems (RMSs) based on two design principles: (1) semi-automatic requirement mining and (2) usage of imported and retrieved knowledge. As part of an extensive design project, which led to these principles, we also implemented a prototype based on this design theory (REMINER). It supports requirements engineers in identifying and classifying requirements documented in natural language and allows us to evaluate the artifact’s viability and the conceptual soundness of our design. The results of our evaluation suggest that an RMS based on our proposed design principles can significantly improve recall while maintaining precision levels.