Abstract Bad requirements quality can cause expensive consequences during the software development lifecycle, especially if iterations are long and feedback comes late. 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.
In summary, in our contribution, we extend the current state of reported evidence on automatic QA for requirements artifacts via systematic studies in terms of distribution, precision, recall, and relevance, as well as by means of a systematic evaluation with practitioners under realistic conditions. We perform this on both existing, as well as new quality defects taken from the ISO 29148. Therefore, we extend our previously published first empirical steps @cite_41 to close these gaps by thorough empirical evaluation.
@cite_41
2023128682
Regression testing is the verification that previously functioning software remains after a change. In this paper we report on a systematic review of empirical evaluations of regression test selection techniques, published in major software engineering journals and conferences. Out of 2,923 papers analyzed in this systematic review, we identified 28 papers reporting on empirical comparative evaluations of regression test selection techniques. They report on 38 unique studies (23 experiments and 15 case studies), and in total 32 different techniques for regression test selection are evaluated. Our study concludes that no clear picture of the evaluated techniques can be provided based on existing empirical evidence, except for a small group of related techniques. Instead, we identified a need for more and better empirical studies were concepts are evaluated rather than small variations. It is also necessary to carefully consider the context in which studies are undertaken.