Abstract Context Modern software systems change continuously. Larger changes like architectural redesigns, feature additions, or system-wide refactorings regularly impact the file structures within a software repository (i.e., developers adding, deleting, or moving files). Objective While a normal evolutionary phenomenon, file-structure changes in software repositories have received little attention in past research. An important question that arises is whether outliers (i.e., changes increasing or decreasing file structures more strongly) are potential signs of quality issues in repository management and tooling. Method In this article, we contribute the first large-scale study on outliers of file-structure changes. To this end, we investigated more than 12.2 million file-structure changes from 94,247 GitHub repositories that span ten programming languages. We first performed a quantitative analysis of all these changes to establish a baseline regarding the depths of file structures and changes to these. Using this baseline, we identified and manually inspected 3,049 outliers. Results Our quantitative data shows that file-structure changes are pervasive in the evolution of software repositories, representing 17.9% of all commits across the studied projects. Via our manual inspection, we found that outliers are often associated with programming errors, initial setups, and misconfigurations of package managers. Thus, they are an indicator of potential mistakes and quality problems. Conclusions Our findings demonstrate that current practices and tools for managing software repositories should take file-structure changes into account. This could help practitioners monitor for and mitigate erroneous or unintended structural changes. Researchers can use our methodology and findings to design follow-up studies and new techniques for more robust repository management.

This paper presents the Layered BubbleTea Visualisation, a novel approach to software visualisation that enhances the comprehension of software systems from an architectural perspective. The visualisation addresses the challenge of understanding complex software structures by mapping software components to architectural layers and elucidating their contents according to their relation to the stereotypical functionality of architectural layers. The paper details the development and presentation of the Layered BubbleTea Visualisation technique, illustrated using the source code of an Android email client application, K-9 Mail. We highlight the technique's potential to identify design issues and guide refactoring efforts. This work contributes to the field of software visualisation by offering a new approach to understanding and managing the complexity of software systems. Video URL-https://youtu.be/wvQNhmbyLtk

Abstract Modeling is often associated with complex and heavy tooling, leading to a negative perception among practitioners. However, alternative paradigms, such as everything-as-code or low-code, are gaining acceptance due to their perceived ease of use. This paper explores the dichotomy between these perceptions through the lens of “modeler experience” (MX). MX includes factors such as user experience, motivation, integration, collaboration and versioning, and language complexity. We examine the relationships between these factors and their impact on different modeling usage scenarios. Our findings highlight the importance of considering MX when understanding how developers interact with modeling tools and the complexities of modeling and associated tooling.

Maintaining a software system is a continuous and complex process, typically following a workflow defined by the responsible organization. However, in practice, developers often deviate from the defined process due to personal preferences, varying customer requirements, or urgent deadlines. Such deviations may cause problems later on, or they may indicate potential process improvements. To deal with deviations and improve processes, it is first necessary to fully understand the processes actually employed by developers. For this purpose, process-mining techniques have been proposed that primarily build on version-control data. In this paper, we present a complementary process-mining technique that uses Jira issues to recover process activities not visible in version-control data, particularly focusing on developers’ interactions with issues and each other. We conducted a case study with 74 repositories of 24 developer teams from one large international company to understand the technique’s merits. Our technique revealed process differences across teams and depending on the types of Jira issues, providing novel insights for the company that helped to better understand the employed processes.

This work presents a knowledge-representation-based approach for analysing software systems. Its main components are: a generic and extensible knowledge model, and a knowledge extractor tool that generates instance-level knowledge graphs from software repositories (currently Java). Our knowledge model can be used as a shared data-model in a software analysis pipeline. We illustrate the potential uses of our knowledge representation by performing experimental architecture recovery and identifying design pattern instance. We intend to use our ontology and extraction tool as a partial foundation for automated reasoning on software systems.

UML is the de facto standard notation for graphically representing software. UML diagrams are used in the analysis, construction, and maintenance of software systems. Mostly, UML diagrams capture an abstract view of a (piece of a) software system. A key purpose of UML diagrams is to share knowledge about the system among developers. The quality of the layout of UML diagrams plays a crucial role in their comprehension. In this paper, we present an automated method for evaluating the layout quality of UML class diagrams. We use machine learning based on features extracted from the class diagram images using image processing. Such an automated evaluator has several uses: (1) From an industrial perspective, this tool could be used for automated quality assurance for class diagrams (e.g., as part of a quality monitor integrated into a DevOps toolchain). For example, automated feedback can be generated once a UML diagram is checked in the project repository. (2) In an educational setting, the evaluator can grade the layout aspect of student assignments in courses on software modeling, analysis, and design. (3) In the field of algorithm design for graph layouts, our evaluator can assess the layouts generated by such algorithms. In this way, this evaluator opens up the road for using machine learning to learn good layouting algorithms. We use machine learning techniques to build (linear) regression models based on features extracted from the class diagram images using image processing. As ground truth, we use a dataset of 600+ UML Class Diagrams for which experts manually label the quality of the layout. This paper makes the following contributions: (1) We show the feasibility of the automatic evaluation of the layout quality of UML class diagrams. (2) We analyze which features of UML class diagrams are most strongly related to the quality of their layout. (3) We evaluate the performance of our layout evaluator. (4) We offer a dataset of labeled UML class diagrams. In this dataset, we supply for every diagram the following information: (a) a manually established ground truth of the quality of the layout, (b) an automatically established value for the layout-quality of the diagram (produced by our classifier), and (c) the values of key features of the layout of the diagram (obtained by image processing). This dataset can be used for replication of our study and others to build on and improve on this work. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board..