NALABS: Detecting Bad Smells in Natural Language Requirements and Test Specifications
This work addresses the issue of manual and inefficient requirement review for developers in large-scale embedded systems, though it appears incremental as it applies an existing concept (smells) to a specific domain.
The paper tackles the problem of low-quality natural language requirements and test specifications in embedded system development, which can lead to expensive consequences and long feedback loops, by developing a tool called NALABS to automatically detect bad smells in these specifications.
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.