Rapid quality assurance with Requirements Smells

Abstract

Context: Bad requirements quality can cause expensive consequences during the software development lifecycle, especially if iterations are long and feedback comes late. Objectives: We aim at a light-weight static requirements analysis approach that allows for rapid checks immediately when requirements are written down. Method: 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. Results: 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. Conclusion: 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.

Which Requirements Artifact Quality Defects are Automatically Detectable? A Case Study

“…III (classification extends previous work [10]). The major reason was, in our studied case, that the rules themselves are still imprecise or unclear.…”

“…All in all, few works have tried to take a different viewpoint and understand what cannot be automatically checked. In previous work [10], we approached this question in a qualitative manner, by looking not at definitions, but at instances of defects. We did not quantify the portion of automatically discoverable defects, since this depends heavily on the requirements at hand (which defects does an author introduce and a reviewer find?…”

“…[7], [8], [9], [10]) or cloning [11]. However, it is still an open question to what degree quality defects can be detected automatically or require human expertise (i.e.…”

A Lightweight Approach for Specification and Detection of SOAP Anti-Patterns

“…A review on product line based architectural bad smells is presented by Vale et al [31]. The concept of bad smells at requirements level and their detection is presented by Femmer et al [32]. All of the above discussed approaches focused on bad smells for object oriented software applications.…”

Using NLP to Detect Requirements Defects: An Industrial Experience in the Railway Domain

“…Several natural language processing (NLP) approaches have been developed to assist requirements review. Part of these works focuses on the identification of typical defective terms and constructions [4,15,14,20,2,11], while other focus on artificial intelligence techniques [7,21,13]. However, the literature is lacking large-scale case studies concerning industrial applications of NLP approaches for defect detection [11].…”