# Rapid quality assurance with Requirements Smells

arXiv: https://arxiv.org/abs/1611.08847 · HTML: https://mlsanity.com/paper/1611.08847/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2016.

## Built on by (most cited first, 12 of 12)

- [Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions](https://mlsanity.com/paper/2602.14878/) (2026)
- [The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review](https://mlsanity.com/paper/2206.00421/) (2022)
- [Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work](https://mlsanity.com/paper/2108.03087/) (2021)
- [Don't Make Models Guess Security and Safety: Symbolic Guardrails for Domain-Specific AI Agents](https://mlsanity.com/paper/2604.15579/) (2026)
- [Classification of Natural Language Processing Techniques for Requirements Engineering](https://mlsanity.com/paper/2204.04282/) (2022)
- [Natural Language Requirements Testability Measurement Based on Requirement Smells](https://mlsanity.com/paper/2403.17479/) (2024)
- [DeepQuali: Initial results of a study on the use of large language models for assessing the quality of user stories](https://mlsanity.com/paper/2602.08887/) (2026)
- [QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation](https://mlsanity.com/paper/2603.11890/) (2026)
- [A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements](https://mlsanity.com/paper/2607.04436/) (2026)
- [Measuring What a Specification Determines: A Formal Semantic-Block Model and an Execution-Judged Benchmark](https://mlsanity.com/paper/2608.19475/) (2026)
- [Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authority](https://mlsanity.com/paper/2609.30219/) (2026)
- [Specification-Based Testing of Large Language Models](https://mlsanity.com/paper/2610.14443/) (2026)

## 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.