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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Fred Brooks in his book "The Mythical Man Month" describes how the inherent properties of software (i.e. complexity, conformity, changeability, and invisibility) make its design an "essential" difficulty. Good design practices are fundamental requisites to address this difficulty. One such good practice is that a software designer should be aware of and address "design smells" that can manifest as a result of his design decisions. However, our study of the vast literature on object-oriented design smells reveals the lack of an effective organization of smells that could better guide a designer in understanding and addressing potential issues in his design. In order to address this gap, we have adopted a novel approach to classify and catalog a number of recurring structural design smells based on how they violate key object oriented (OO) design principles. To evaluate the usefulness of our design smell catalog, we first asked Siemens CT DC AA architects to use it to identify design smells in their projects, and later elicited feedback from them about their experience. The feedback received indicates that these architects found the catalog to be very useful. In this paper, we present our catalog, classification, and naming scheme for design smells and also highlight several interesting observations and insights that result from our work.

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Background: Code smells are indicators of quality problems that make a software hard to maintain and evolve. Given the importance of smells in the source code's maintainability, many studies have explored the characteristics of smells and analyzed their effects on the software's quality. Aim: We aim to investigate fundamental characteristics of code smells through an empirical study on frequently occurring smells that examines inter-category and intra-category correlation between design and implementation smells. Method: The study mines 19 design smells and 11 implementation smells in 1988 C# repositories containing more than 49 million lines of code. The mined data are statistically analyzed using methods such as Spearman's correlation and presented through hexbin and scatter plots. Results: We find that unutilized abstraction and magic number smells are the most frequently occurring smells in C# code. Our results also show that implementation and design smells exhibit strong inter-category correlation. The results of co-occurrence analysis imply that whenever unutilized abstraction or magic number smells are found, it is very likely to find other smells from the same smell category in the project. Conclusions: Our experiment shows high average smell density (14.7 and 55.8 for design and implementation smells respectively) for open source C# programs. Such high smell densities turn a software system into a house of cards reflecting the fragility introduced in the system. Our study advocates greater awareness of smells and the adoption of regular refactoring within the developer community to avoid turning software into a house of cards.