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.

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.

Variability permeates software development to satisfy ever-changing requirements and mass-customization needs. A prime example is the Linux kernel, which employs the C preprocessor to specify a set of related but distinct kernel variants. To study, analyze, and verify variational software, several formal languages have been proposed. For example, the choice calculus has been successfully applied for type checking and symbolic execution of configurable software, while other formalisms have been used for variational model checking, change impact analysis, among other use cases. Yet, these languages have not been formally compared, hence, little is known about their relationships. Crucially, it is unclear to what extent one language subsumes another, how research results from one language can be applied to other languages, and which language is suitable for which purpose or domain. In this paper, we propose a formal framework to compare the expressive power of languages for static (i.e. compile-time) variability. By establishing a common semantic domain to capture a widely used intuition of explicit variability, we can formulate the basic, yet to date neglected, properties of soundness, completeness, and expressiveness for variability languages. We then prove the (un)soundness and (in)completeness of a range of existing languages, and relate their ability to express the same variational systems. We implement our framework as an extensible open source Agda library in which proofs act as correct compilers between languages or differencing algorithms. We find different levels of expressiveness as well as complete and incomplete languages w.r.t. our unified semantic domain, with the choice calculus being among the most expressive 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.

In clone-and-own development, new variants of a software system are typically created by manually copying and adapting an existing variant. This approach is flexible but suffers from various challenges such as high maintenance cost in the long term. While researchers started to address the challenges of clone-and-own, there is yet little empirical evidence on the efficiency and effectiveness of clone-and-own research. The main reason for this is the lack of appropriate benchmarks, which need to expose a multitude of different data and meta-data serving as input and ground truth for experimental evaluations. We present VEVOS, a benchmark generation framework that picks up these requirements and, given the version history of a software product line, enables the simulation of the evolution of cloned variants, and provides meta-data serving as ground truth.

In clone-and-own - the predominant paradigm for developing multi-variant software systems in practice - a new variant of a software system is created by copying and adapting an existing one. While clone-and-own is flexible, it causes high maintenance effort in the long run as cloned variants evolve in parallel; certain changes, such as bug fixes, need to be propagated between variants manually. On top of the principle of cherry-picking and by collecting lightweight domain knowledge on cloned variants and software changes, a recent line of research proposes to automate such synchronization tasks when migration to a software product line is not feasible. However, it is yet unclear how far this synchronization can actually be pushed. We conduct an empirical study in which we quantify the potential to automate the synchronization of variants in clone-and-own. We simulate the variant synchronization using the history of a real-world multi-variant software system as a case study. Our results indicate that existing patching techniques propagate changes with an accuracy of up to 85%, if applied consistently from the start of a project. This can be even further improved to 93% by exploiting lightweight domain knowledge about which features are affected by a change, and which variants implement affected features. Based on our findings, we conclude that there is potential to automate the synchronization of cloned variants through existing patching techniques.

For highly configurable software systems, such as the Linux kernel, maintaining and evolving variability information along changes to source code poses a major challenge. While source code itself may be edited, also feature-to-code mappings may be introduced, removed, or changed. In practice, such edits are often conducted ad-hoc and without proper documentation. To support the maintenance and evolution of variability, it is desirable to understand the impact of each edit on the variability. We propose the first complete and unambiguous classification of edits to variability in source code by means of a catalog of edit classes. This catalog is based on a scheme that can be used to build classifications that are complete and unambiguous by construction. To this end, we introduce a complete and sound model for edits to variability. In about 21.5ms per commit, we validate the correctness and suitability of our classification by classifying each edit in 1.7 million commits in the change histories of 44 open-source software systems automatically. We are able to classify all edits with syntactically correct feature-to-code mappings and find that all our edit classes occur in practice.

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 N input models) in Java, and empirically evaluate the matching quality and runtime performance on several datasets of different origin and model type. 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.

Tracing requirements to their implementation is crucial to all stakeholders of a software development process. When managing software variability, requirements are typically expressed in terms of features, a feature being a user-visible characteristic of the software. While feature traces are fully documented in software product lines, ad-hoc branching and forking, known as clone-and-own, is still the dominant way for developing multi-variant software systems in practice. Retroactive migration to product lines suffers from uncertainties and high effort because knowledge of feature traces must be recovered but is scattered across teams or even lost. We propose a semi-automated methodology for recording feature traces proactively, during software development when the necessary knowledge is present. To support the ongoing development of previously unmanaged clone-and-own projects, we explicitly deal with the absence of domain knowledge for both existing and new source code. We evaluate feature trace recording by replaying code edit patterns from the history of two real-world product lines. Our results show that feature trace recording reduces the manual effort to specify traces. Recorded feature traces could improve automation in change-propagation among cloned system variants and could reduce effort if developers decide to migrate to a product line.