The source code of a Java-based software system is often structured into packages. When packages are large, they often carry maintainability quality issues. In the literature, there is a lack of empirical evidence on the specific maintainability issues that occur when packages become too large. Our study fills this gap by performing relationship analysis of package size with respect to internal maintainability characteristics (coupling, cohesion, and complexity) using package-level metrics collected from 111 open-source Java projects provided in Qualitas Corpus . Our results show significantly higher maintainability issues in large packages as indicated by the maintainability metrics. We also report strong relationships of package size with cohesion (represented by the number of connected components in a package) and complexity (measured by the number of internal relationships in a package). Based on these strong associations with package size, we show that these cohesion and complexity metrics can be used to identify large package refactoring opportunities. Furthermore, we also discuss why some maintainability metrics (e.g., coupling metrics) may not be useful for refactoring large packages. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board .

Background: Software smells reflect the sub-optimal patterns in the software. In a similar way, community smells consider the sub-optimal patterns in the organizational and social structures of software teams. Related work performed empirical studies to identify the relationship between community smells and software smells at the architecture and code levels. However, how community smells relate with design smells is still unknown.

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.

Abstract Bad smells tend to have a negative impact on software by degrading its quality. It is beneficial to detect model smells to avoid their propagation to later stages of software development. The objective of this paper is to present the state‐of‐the‐art research on techniques for detecting UML model bad smells. The detection techniques are compared and evaluated using a proposed evaluation framework. The framework consists of two parts. The first part of the framework compares the techniques in terms of the implemented approach, the investigated model, and the explored model smells, while the experimental design is explored in the second part of the framework. We found that the detection of bad smells in class and sequence diagrams is accomplished via design patterns, software metrics, and predefined rules, while model smells in use cases are detected using metrics and predefined rules. We also found that the class diagram is the most investigated UML model in the context of model smell detection, whereas there is a lack of work on other UML models. In addition, there is a scarcity of independent studies on sequence diagrams. Furthermore, the studies investigating class diagrams are mostly validated, whereas use case diagrams and sequence diagrams are rarely validated.

Parallel coordinates plots and RadViz are two visualization techniques that deal with multivariate data. They complement each other in identifying data patterns, clusters, and outliers. In this paper, we analyze multivariate software metrics linking the two approaches for detecting outliers, which could be the indicators for bad smells in software systems. Parallel coordinates plots provide an overview, whereas the RadViz representation allows for comparing a smaller subset of metrics in detail. We develop an interactive visual analytics system supporting automatic detection of bad smell patterns. In addition, we investigate the distinctive properties of outliers that are not considered harmful, but noteworthy for other reasons. We demonstrate our approach with open source Java systems and describe detected bad smells and other outlier patterns.