Rapid quality assurance with Requirements Smells
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Abstract (arXiv)
Bad requirements quality can cause expensive consequences during the software development lifecycle, especially if iterations are long and feedback comes late. %-- the faster a problem is found, the cheaper it is to fix. This makes explicit the need of a lightweight detection mechanism of requirements quality violations. We aim at a light-weight static requirements analysis approach that allows for rapid checks immediately when requirements are written down. We transfer the concept of code smells to Requirements Engineering as Requirements Smells. To evaluate the benefits and limitations, we define Requirements Smells, realize our concepts for a smell detection in a prototype called Smella and apply Smella in a series of cases provided by three industrial and a university context. The automatic detection yields an average precision of 59% at an average recall of 82% with high variation. The evaluation in practical environments indicates benefits such as an increase of the awareness of quality defects. Yet, some smells were not clearly distinguishable. Lightweight smell detection can uncover many practically relevant requirements defects in a reasonably precise way. Although some smells need to be defined more clearly, smell detection provides a helpful means to support quality assurance in Requirements Engineering, for instance, as a supplement to reviews.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Nov 2016.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 7.
11 papers.
- 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptionscited by 15
- 2022The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Reviewcited by 5
- 2021Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Workcited by 2
- 2022Classification of Natural Language Processing Techniques for Requirements Engineeringcited by 1
- 2026Don't Make Models Guess Security and Safety: Symbolic Guardrails for Domain-Specific AI Agentscited by 1
- 2024Natural Language Requirements Testability Measurement Based on Requirement Smellscited by 0
- 2026DeepQuali: Initial results of a study on the use of large language models for assessing the quality of user storiescited by 0
- 2026QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiationcited by 0
- 2026A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirementscited by 0
- 2026Measuring What a Specification Determines: A Formal Semantic-Block Model and an Execution-Judged Benchmarkcited by 0
- 2026Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authoritycited by 0
- 2026Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authority24 Sep 2026
- 2026Measuring What a Specification Determines: A Formal Semantic-Block Model and an Execution-Judged Benchmark19 Aug 2026
- 2026A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements5 Jul 2026
- 2026Don't Make Models Guess Security and Safety: Symbolic Guardrails for Domain-Specific AI Agents16 Apr 2026
- 2026QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation12 Mar 2026
- 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions16 Feb 2026
- 2026DeepQuali: Initial results of a study on the use of large language models for assessing the quality of user stories9 Feb 2026
- 2024Natural Language Requirements Testability Measurement Based on Requirement Smells26 Mar 2024
- 2022The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review17 May 2022
- 2022Classification of Natural Language Processing Techniques for Requirements Engineering8 Apr 2022
- 2021Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work6 Aug 2021