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NALABS: Detecting Bad Smells in Natural Language Requirements and Test Specifications

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arxiv 2202.05641 v2 pith:7VRQ4WDJ submitted 2022-02-11 cs.SE

Kostadin Rajkovic , Eduard Enoiu This is my paper · ORCID
classification cs.SE
keywords specificationslanguagenaturalrequirementsmellsnalabsduringengineering
verification ladder T0 review T1 audit T2 compute T3 formal
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In large-scale embedded system development, requirement and test specifications are often expressed in natural language. In the context of developing such products, requirement review is performed in many cases manually using these specifications as a basis for quality assurance. Low-quality specifications can have expensive consequences during the requirement engineering process. Especially, if feedback loops during requirement engineering are long, leading to artifacts that are not easily maintainable, are hard to understand, and are inefficient to port to other system variants. We use the idea of smells to specifications expressed in natural language, defining a set of specifications for bad smells. We developed a tool called NALABS (NAtural LAnguage Bad Smells), available on https://github.com/eduardenoiu/NALABS and used for automatically checking specifications. We discuss some of the decisions made for its implementation, and future work.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Investigating the Performance of Small Language Models in Detecting Test Smells in Manual Test Cases

    cs.SE 2025-07 conditional novelty 5.0 of 10

    Small language models with a targeted prompting scheme detect seven test-smell types in natural-language Ubuntu manual tests, with pass@2 scores of 90-97% across three models.

  2. SmartDelta Methodology: Automated Quality Assurance and Optimization for Incremental System Engineering

    cs.SE 2025-01 conditional novelty 4.0 of 10

    The paper introduces a six-stage SmartDelta Methodology for quality assurance across software versions and variants and illustrates it with seven project tools, but does not empirically validate the methodology.

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