Rapid quality assurance with Requirements Smells
arXiv:1611.08847 · doi:10.1016/j.jss.2016.02.047
Abstract
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.
arXiv commentThe Journal of Systems and Software, 2016
References in corpus (3)
- Naming the Pain in Requirements Engineering: Design of a Global Family of Surveys and First Results from Germany
- Naming the Pain in Requirements Engineering: A Design for a Global Family of Surveys and First Results from Germany
- Can clone detection support quality assessments of requirements specifications?
Cited by in corpus (17)
- The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review
- Identifying Relevant Information Cues for Vulnerability Assessment Using CVSS
- Detecting Requirements Smells With Deep Learning: Experiences, Challenges and Future Work
- From Specifications to Prompts: On the Future of Generative LLMs in Requirements Engineering
- A Live Extensible Ontology of Quality Factors for Textual Requirements
- Requirements Quality Research: a harmonized Theory, Evaluation, and Roadmap
- A Catalog of Transformations to Remove Smells From Natural Language Tests
- Causality in Requirements Artifacts: Prevalence, Detection, and Impact
- Requirements Quality Assurance in Industry: Why, What and How?
- Applying Bayesian Data Analysis for Causal Inference about Requirements Quality: A Controlled Experiment
- Which Requirements Artifact Quality Defects are Automatically Detectable? A Case Study
- A Second Look at the Impact of Passive Voice Requirements on Domain Modeling: Bayesian Reanalysis of an Experiment
- Identifying relevant Factors of Requirements Quality: an industrial Case Study
- Explainable Software for Cyber-Physical Systems (ES4CPS): Report from the GI Dagstuhl Seminar 19023, January 06-11 2019, Schloss Dagstuhl
- What Makes Agile Test Artifacts Useful? An Activity-Based Quality Model from a Practitioners' Perspective
- A Reliabel and an efficient web testing system
- How Do Practitioners Interpret Conditionals in Requirements?