Context: Smells in software systems impair software quality and make them hard to maintain and evolve. The software engineering community has explored various dimensions concerning smells and produced extensive research related to smells. The plethora of information poses challenges to the community to comprehend the state-of-the-art tools and techniques. Objective: We aim to present the current knowledge related to software smells and identify challenges as well as opportunities in the current practices. Method: We explore the definitions of smells, their causes as well as effects, and their detection mechanisms presented in the current literature. We studied 445 primary studies in detail, synthesized the information, and documented our observations. Results: The study reveals five possible defining characteristics of smellsindicator, poor solution, violates best-practices, impacts quality, and recurrence. We curate ten common factors that cause smells to occur including lack of skill or awareness and priority to features over quality. We classify existing smell detection methods into five groups-metrics, rules/heuristics, history, machine learning, and optimization-based detection. Challenges in the smells detection include the tools' proneness to false-positives and poor coverage of smells detectable by existing tools.
Infrastructure as Code (IaC) is the practice of specifying computing system configurations through code, and managing them through traditional software engineering methods. The wide adoption of configuration management and increasing size and complexity of the associated code, prompt for assessing, maintaining, and improving the configuration code's quality. In this context, traditional software engineering knowledge and best practices associated with code quality management can be leveraged to assess and manage configuration code quality. We propose a catalog of 13 implementation and 11 design configuration smells, where each smell violates recommended best practices for configuration code. We analyzed 4,621 Puppet repositories containing 8.9 million lines of code and detected the cataloged implementation and design configuration smells. Our analysis reveals that the design configuration smells show 9% higher average co-occurrence among themselves than the implementation configuration smells. We also observed that configuration smells belonging to a smell category tend to co-occur with configuration smells belonging to another smell category when correlation is computed by volume of identified smells. Finally, design configuration smell density shows negative correlation whereas implementation configuration smell density exhibits no correlation with the size of a configuration management system. CCS Concepts •Software and its engineering → Specification languages; Software maintenance tools; Software libraries and repositories; Software design engineering;
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