Adopting Use Case Descriptions for Requirements Specification: an Industrial Case Study

2025-06-16

AI Summary

Prior research lacks empirical insights into the actual adoption, quality effectiveness, and influencing factors of use case (UC) descriptions in industrial practice. Method: Drawing on large-scale industrial data from multinational enterprises (2020–2024), this study employs a mixed-methods approach—combining large-scale textual statistics, expert-based quality assessment, and regression/correlation analyses—to systematically examine UC practices. Contribution/Results: It provides the first large-scale empirical evidence revealing systematic deviations between real-world UC usage and textbook norms. Findings indicate that only a few features—such as solution orientation—significantly enhance development efficiency; UC template adoption exhibits a nonlinear relationship with quality; and UC quality exerts limited, highly context-dependent influence on downstream development—challenging conventional assumptions about UC quality efficacy. These results offer novel empirical evidence and theoretical directions for requirements engineering research and practice.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: User Experience and Usability

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Context: Use case (UC) descriptions are a prominent format for specifying functional requirements. Existing literature abounds with recommendations on how to write high-quality UC descriptions but lacks insights into (1) their real-world adoption, (2) whether these recommendations correspond to actual quality, and (3) which factors influence the quality of UCs. Objectives: We aim to contribute empirical evidence about the adoption of UC descriptions in a large, globally distributed case company. Methods: We surveyed 1188 business requirements of a case company that were elicited from 2020-01-01 until 2024-12-31 and contained 1192 UCs in various forms. Among these, we manually evaluated the 273 template-style UC descriptions against established quality guidelines. We generated descriptive statistics of the format's adoption over the surveyed time frame. Furthermore, we used inferential statistics to determine (a) how properties of the requirements engineering process affected the UC quality and (b) how UC quality affects subsequent software development activities. Results and Conclusions: Our descriptive results show how the adoption of UC descriptions in practice deviates from textbook recommendations. However, our inferential results suggest that only a few phenomena like solution-orientation show an actual impact in practice. These results can steer UC quality research into a more relevant direction.

Problem

• Assess real-world adoption of use case descriptions
• Evaluate quality impact of requirements engineering process
• Analyze influence of use case quality on development

Innovation

• Surveyed 1188 business requirements for UC adoption
• Manually evaluated 273 template-style UC descriptions
• Used inferential statistics to assess UC quality impact