Context: An excessive number of code smells make a software system hard to evolve and maintain. Machine learning methods, in addition to metric-based and heuristic-based methods, have been recently applied to detect code smells; however, current methods are considered far from mature. Objective: First, explore the feasibility of applying deep learning models to detect smells without extensive feature engineering. Second, investigate the possibility of applying transfer-learning in the context of detecting code smells. Methods: We train smell detection models based on Convolution Neural Networks and Recurrent Neural Networks as their principal hidden layers along with autoencoder models. For the first objective, we perform training and evaluation on C# samples, whereas for the second objective, we train the models from C# code and evaluate the models over Java code samples and vice-versa. Results: We find it feasible to detect smells using deep learning methods though the models’ performance is smell-specific. Our experiments show that transfer-learning is definitely feasible for implementation smells with performance comparable to that of direct-learning. This work opens up a new paradigm to detect code smells by transfer-learning especially for the programming languages where the comprehensive code smell detection tools are not available.

Context:: Architecture of a software system represents the key design decisions and therefore its quality plays an important role to keep the software maintainable. Code smells are indicators of quality issues in a software system and are classified based on their granularity, scope, and impact. Despite a plethora of existing work on smells, a detailed exploration of architecture smells, their characteristics, and their relationships with smells in other granularities is missing. Objective:: The paper aims to study architecture smells characteristics, investigate correlation, collocation, and causation relationships between architecture and design smells. Method:: We implement smell detection support for seven architecture smells. We mine 3 073 open-source repositories containing more than 118 million lines of C# code and empirically investigate the relationships between seven architecture and 19 design smells. Results:: We find that smell density does not depend on repository size. Cumulatively, architecture smells are highly correlated with design smells. Our collocation analysis finds that the majority of design and architecture smell pairs do not exhibit collocation. Finally, our causality analysis reveals that design smells cause architecture smells.

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 smells — indicator, 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.

Context: Databases are an integral element of enterprise applications. Similarly to code, database schemas are also prone to smells - best practice violations. Objective: We aim to explore database schema quality, associated characteristics and their relationships with other software artifacts. Method: We present a catalog of 13 database schema smells and elicit developers' perspective through a survey. We extract embedded sql statements and identify database schema smells by employing the DbDeo tool which we developed. We analyze 2925 production-quality systems (357 industrial and 2568 well-engineered open-source projects) and empirically study quality characteristics of their database schemas. In total, we analyze 629 million lines of code containing more than 393 thousand sql statements. Results: We find that the index abuse smell occurs most frequently in database code, that the use of an orm framework doesn't immune the application from database smells, and that some database smells, such as adjacency list, are more prone to occur in industrial projects compared to open-source projects. Our co-occurrence analysis shows that whenever the clone table smell in industrial projects and the values in attribute definition smell in open-source projects get spotted, it is very likely to find other database smells in the project. Conclusion: The awareness and knowledge of database smells are crucial for developing high-quality software systems and can be enhanced by the adoption of better tools helping developers to identify database smells early.

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.

The role of exceptions is crucial for the robustness of modern applications and critical systems. Despite this, there is a long debate among researchers, programming language designers, and practitioners regarding the usefulness and appropriateness of the available exception types and their classification. In this paper, we examine Java exceptions and propose a new class hierarchy and compile-time mechanisms that take into account the context in which exceptions can arise. We believe that the increased specificity of exception handling based on our proposal can boost its effectiveness and lead to fewer application failures.

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.