Abstract Both software engineering researchers and practitioners have increasingly shifted their focus from single software systems to software families, reflecting the need for software industrialization through systematic reuse of implementation artifacts. Interestingly, several vibrant ecosystems produce software families in a radically different way than classical variability-intensive systems, notably software product lines (SPLs). The Bitcoin community, for instance, evolves its ecosystem through crowdsourced improvement proposals being continuously shaped and autonomously implemented by independent actors. While this novel paradigm of Community-Driven Variability (CDV) has proven effective for driving flourishing technologies like Bitcoin and others, it also comes with unique challenges calling for novel solutions. In this paper, we define the key characteristics of ecosystems exposing CDV and derive a taxonomy that hierarchically decomposes each characteristic into constituting sub-characteristics. Building on the taxonomy, we conduct a systematic analysis of 14 software ecosystems to evaluate the presence and nature of CDV. We highlight the novel problems they face, such as the lack of ecosystem overview, difficulties in impact assessment, misalignment between proposals and implementations, and interoperability breakdowns – challenges that transcend classical variability management. Based on the problem analysis, we outline our research vision to tackle these challenges, including a sketch of concrete starting points for technical solutions. While classical SPLs and CDV ecosystems differ drastically, we believe that feature-oriented modeling and analysis offers promising concepts for addressing CDV challenges without enforcing product-line processes. Conversely, the unique demands of CDV can inspire advances in variability research with impact beyond its original domains.

GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers' perceptions of their feasibility and usefulness. Our results show that over 80 % of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.

In version control systems such as Git, concurrent modifications on the same artifacts can cause merge conflicts that may disrupt the development workflow by requiring manual intervention. While research on software merging focused on sophisticated techniques that hardly had any impact in practice, we present an empirical feasibility study on semi-automated conflict resolution using a fixed set of only a few language-agnostic conflict resolution patterns. In a large-scale quantitative analysis, we simulate the performance of our hypothetical conflict resolution strategy by classifying 131,154 merge conflict resolutions of a diverse sample of 10,000 GitHub projects according to these resolution patterns. We shed light on the derivability of merges on multiple levels of granularity: the conflicting merge commit, its conflicting files and their individual conflicting chunks. 87.9% of chunks are derivable individually, while 34.5% of merges are derivable as a whole. Interestingly, however, by inspecting potential factors affecting derivability, we observe that there are stronger correlations considering individual files than considering the entire merge. A short yet preliminary answer to whether semi-automated conflict resolution is easier than we expected is: yes, it might be, particularly if we use the right level of granularity for proposing conflict resolutions. Through our comprehensive analysis, we aspire to bridge the gap between academic innovations on sophisticated merge techniques and real-world merge conflict scenarios, laying the groundwork for more effective and widely accepted automatic merge tools.

Code comments are crucial for program comprehension and maintenance. To better understand the nature and content of comments, previous work proposed taxonomies of comment information for textual languages, notably classical programming languages. However, paradigms such as model-driven or model-based engineering often promote the use of visual languages, to which existing taxonomies are not directly applicable. Taking MATLAB/Simulink as a representative of a sophisticated and widely used modeling environment, we extend a multi-language comment taxonomy onto new (visual) comment types and two new languages: Simulink and MATLAB. Furthermore, we outline Simulink commenting practices and compare them to textual languages. We analyze 259,267 comments from 9095 Simulink models and 17,792 MATLAB scripts. We identify the comment types, their usage frequency , classify comment information, and analyze their correlations with model metrics. We manually analyze 757 comments to extend the taxonomy. We also analyze commenting guidelines and developer adherence to them. Our extended taxonomy, SCoT (Simulink Comment Taxonomy), contains 25 categories. We find that Simulink comments, although often duplicated, are used at all model hierarchy levels. Of all comment types, Annotations are used most often; Notes scarcely. Our results indicate that Simulink developers, instead of extending comments, add new ones, and rarely follow commenting guidelines. Overall, we find Simulink comment information comparable to textual languages, which highlights commenting practice similarity across languages.

Abstract Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN (Range Queries on $$\text {N}$$ N input models) in Java and empirically evaluate the matching quality and runtime performance on several datasets of different origins and model types. Compared to the state of the art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.

Abstract Simulink is an example of a successful application of the paradigm of model-based development into industrial practice. Numerous companies create and maintain Simulink projects for modeling software-intensive embedded systems, aiming at early validation and automated code generation. However, Simulink projects are not as easily available as code-based ones, which profit from large publicly accessible open-source repositories, thus curbing empirical research. In this paper, we investigate a set of 1734 freely available Simulink models from 194 projects and analyze their suitability for empirical research. We analyze the projects considering (1) their development context, (2) their complexity in terms of size and organization within projects, and (3) their evolution over time. Our results show that there are both limitations and potentials for empirical research. On the one hand, some application domains dominate the development context, and there is a large number of models that can be considered toy examples of limited practical relevance. These often stem from an academic context, consist of only a few Simulink blocks, and are no longer (or have never been) under active development or maintenance. On the other hand, we found that a subset of the analyzed models is of considerable size and complexity. There are models comprising several thousands of blocks, some of them highly modularized by hierarchically organized Simulink subsystems. Likewise, some of the models expose an active maintenance span of several years, which indicates that they are used as primary development artifacts throughout a project’s lifecycle. According to a discussion of our results with a domain expert, many models can be considered mature enough for quality analysis purposes, and they expose characteristics that can be considered representative for industry-scale models. Thus, we are confident that a subset of the models is suitable for empirical research. More generally, using a publicly available model corpus or a dedicated subset enables researchers to replicate findings, publish subsequent studies, and use them for validation purposes. We publish our dataset for the sake of replicating our results and fostering future empirical research.