Software Design Smell Detection: a systematic mapping study

Abstract

Design Smells are indicators of situations that negatively affect software quality attributes such as understandability, testability, extensibility, reusability, and maintainability in general. Improving maintainability is one of the cornerstones of making software evolution easier. Hence, Design Smell Detection is important in helping developers when making decisions that can improve software evolution processes. After a long period of research, it is important to organize the knowledge produced so far and to identify current challenges and future trends. In this paper, we analyze 18 years of research into Design Smell Detection. There is a wide variety of terms that have been used in the literature to describe concepts which are similar to what we have defined as “Design Smells,” such as design defect, design flaw, anomaly, pitfall, antipattern, and disharmony. The aim of this paper is to analyze all these terms and include them in the study. We have used the standard systematic literature review method based on a comprehensive set of 395 articles published in different proceedings, journals, and book chapters. We present the results in different dimensions of Design Smell Detection, such as the type or scope of smell, detection approaches, tools, applied techniques, validation evidence, type of artifact in which the smell is detected, resources used in evaluation, supported languages, and relation between detected smells and software quality attributes according to a quality model. The main contributions of this paper are, on the one hand, the application of domain modeling techniques to obtain a conceptual model that allows the organization of the knowledge on Design Smell Detection and a collaborative web application built on that knowledge and, on the other, finding how tendencies have moved across different kinds of smell detection, as well as different approaches and techniques. Key findings for future trends include the fact that all automatic detection tools described in the literature identify Design Smells as a binary decision (having the smell or not), which is an opportunity to evolve to fuzzy and prioritized decisions. We also find that there is a lack of human experts and benchmark validation processes, as well as demonstrating that Design Smell Detection positively influences quality attributes.