In 2025, coding agents have seen a very rapid adoption. Coding agents leverage Large Language Models (LLMs) in ways that are markedly different from LLM-based code completion, making their study critical. Moreover, unlike LLM-based completion, coding agents leave visible traces in software repositories, enabling the use of MSR techniques to study their impact on SE practices. This paper documents the promises, perils, and heuristics that we have gathered from studying coding agent activity on GitHub.

The rapid growth of software repositories on development platforms such as GitHub, as well as archives like Software Heritage, prompts the need for better repository classification. Machine learning is increasingly used to automate this classification, but there are no secondary studies analyzing this research landscape.

Understanding API evolution and the introduction of breaking changes (BCs) in software libraries is essential for library maintainers to manage backward compatibility and for researchers to conduct empirical studies on software library evolution. In Java, tools such as Japicmp and Revapi are commonly used to detect BCs between library releases, but their reliance on binary JARs limits their applicability. This restriction hinders large-scale longitudinal studies of API evolution and fine-grained analyses such as commit-level BC detection. In this paper, we introduce Roseau, a novel static analysis tool that constructs technology-agnostic API models from library code equipped with rich semantic analyses. API models can be analyzed to study API evolution and compared to identify BCs between any two versions of a library (releases, commits, branches, etc.). Unlike traditional approaches, Roseau can build API models from source code or bytecode, and is optimized for large-scale longitudinal analyses of library histories. We assess the accuracy, performance, and suitability of ROSEAU for longitudinal studies of API evolution, using Japicmp and Revapi as baselines. We extend and refine an established benchmark of BCs and show that Roseau achieves higher accuracy ($F_{1}=0.99$) than Japicmp ($F_{1}=0.86$) and Revapi ($F_{1}=0.91$). We analyze 60 popular libraries from Maven Central and find that Roseau delivers excellent performance, detecting BCs between versions in under two seconds, including in libraries with hundreds of thousands of lines of code. We further illustrate the limitations of Japicmp and REVAPI for longitudinal studies and the novel analysis capabilities offered by Roseau by tracking the evolution of Google's Guava API and the introduction of BCs over 14 years and 6,839 commits, reducing analysis times from a few days to a few minutes.

Modern software development heavily relies on thirdparty libraries to speed up development and enhance quality. As libraries evolve, they may break the tacit contract established with their clients by introducing behavioral breaking changes (BBCs) that alter run-time behavior and silently break client applications without being detected at compile time. Traditional regression tests on the client side often fail to detect such BBCs, either due to limited library coverage or weak assertions that do not sufficiently exercise the library's expected behavior. To address this issue, we propose a novel approach to clientlibrary compatibility testing that leverages existing client tests in a novel way. Instead of relying on developer-written assertions, we propose recording the actual interactions at the API boundary during the execution of client tests (protocol, input and output values, exceptions, etc.). These sequences of API interactions are stored as snapshots which capture the exact contract expected by a client at a specific point in time. As the library evolves, we compare the original and new snapshots to identify perturbations in the contract, flag potential BBCs, and notify clients. We implement this technique in our prototype tool Gilesi, a Java framework that automatically instruments library APIs, records snapshots, and compares them. Through a preliminary case study on several client-library pairs with artificially seeded BBCs, we show that Gilesi reliably detects BBCs missed by client test suites.

Automatic Static Analysis Tools (ASATs) are widely used by software developers to diffuse and enforce coding practices. Yet, we know little about the documentation of ASATs, despite it being critical to learn about the coding practices in the first place. We shed light on this through several contributions. First, we analyze the documentation of more than 100 rules of 16 ASATs for multiple programming languages, and distill a taxonomy of the purposes of the documentation---What triggers a rule; Why it is important; and how to Fix an issue---and its types of contents. Then, we conduct a survey to assess the effectiveness of the documentation in terms of its goals and types of content. We highlight opportunities for improvement in ASAT documentation. In particular, we find that the Why purpose is missing in half of the rules we survey; moreover, when the Why is present, it is more likely to have quality issues than the What and the Fix.

Designing an effective API is essential for library developers as it is the lens through which clients will judge its usability and benefits, as well as the main friction point when the library evolves. Despite its importance, defining the boundaries of an API is a challenging task, mainly due to the diverse mechanisms provided by programming languages that have non-trivial interplays. In this paper, we present a novel conceptual framework designed to assist library maintainers in understanding the interactions allowed by their APIs via the use of syntactic usage models. These customizable models enable library maintainers to improve their design ahead of release, reducing friction during evolution. The complementary syntactic usage footprints and coverage scores, inferred from client code using the API (e.g., documentation samples, tests, third-party clients), enable developers to understand in-the-wild uses of their APIs and to reflect on the adequacy of their tests and documentation. We implement these models for Java libraries in a new tool UCov and demonstrate its capabilities on three libraries exhibiting diverse styles of interaction: jsoup, commons-cli, and spark. Our exploratory case study shows that UCov provides valuable information regarding API design and fine-grained analysis of client code to identify under-tested and under-documented library code.

Keeping dependencies up to date is a crucial software maintenance task that requires significant effort. Developers must choose which dependencies to update, select appropriate target versions, and minimize the impact of updates in terms of breaking changes and incompatibilities. Several factors influence the choice of a new dependency version, including its freshness, popularity, absence of vulnerabilities, and compatibility.

Jupyter notebooks are increasingly being adopted by teachers to deliver interactive practical sessions to their students. Notebooks come with many attractive features, such as the ability to combine textual explanations, multimedia content, and executable code alongside a flexible execution model which encourages experimentation and exploration. However, this execution model can quickly become an issue when students do not follow the intended execution order of the teacher, leading to errors or misleading results that hinder their learning. To counter this adverse effect, teachers usually write detailed instructions about how students are expected to use the notebooks. Yet, the use of digital media is known to decrease reading efficiency and compliance with written instructions, resulting in frequent notebook misuse and students getting lost during practical sessions. In this article, we present a novel approach, MOON, designed to remedy this problem. The central idea is to provide teachers with a language that enables them to formalize the expected usage of their notebooks in the form of a script and to interpret this script to guide students with visual indications in real time while they interact with the notebooks. We evaluate our approach using a randomized controlled experiment involving 21 students, which shows that MOON helps students comply better with the intended scenario without hindering their ability to progress. Our follow-up user study shows that about 75% of the surveyed students perceived MOON as rather useful or very useful.

Coding practices are increasingly used by software companies. Their use promotes consistency, readability, and maintainability, which contribute to software quality. Coding practices were initially enforced by general-purpose linters, but companies now tend to design and adopt their own company-specific practices. However, these company-specific practices are often not automated, making it challenging to ensure they are shared and used by developers. Converting these practices into linter rules is a complex task that requires extensive static analysis and language engineering expertise.In this paper, we seek to answer the following question: can coding practices be learned automatically from examples manually tagged by developers? We conduct a feasibility study using CodeBERT, a state-of-the-art machine learning approach, to learn linter rules. Our results show that, although the resulting classifiers reach high precision and recall scores when evaluated on balanced synthetic datasets, their application on real-world, unbalanced codebases, while maintaining excellent recall, suffers from a severe drop in precision that hinders their usability.

Jupyter notebooks have gained popularity in educational settings. In France, it is one of the tools used by teachers in post-secondary classes to teach programming. When students complete their assignments, they send their notebooks to the teacher for feedback or grading. However, the teacher may not be able to reproduce the results contained in the notebooks. Indeed, students rely on the non-linearity of notebooks to write and execute code cells in an arbitrary order. Conversely, teachers are not aware of this implicit execution order and expect to reproduce the results by running the cells linearly from top to bottom. These two modes of usage conflict, making it difficult for teachers to evaluate their students' work. This article investigates the use of immediate visual feedback to alleviate the issue of non-reproducibility of students' notebooks. We implemented a Jupyter plug-in called Notebook Reproducibility Monitor (NoRM) that pinpoints the non-reproducible cells of a notebook under modifications. To evaluate the benefits of this approach, we perform a controlled study with 37 students on a programming assignment, followed by a focus group. Our results show that the plug-in significantly improves the reproducibility of notebooks without sacrificing the productivity of students.

Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative-filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns.

OSGi is a module system and service framework that aims to fill Java's lack of support for modular development. Using OSGi, developers divide software into multiple bundles that declare constrained dependencies towards other bundles. However, there are various ways of declaring and managing such dependencies, and it can be confusing for developers to choose one over another. Over the course of time, experts and practitioners have defined "best practices" related to dependency management in OSGi. The underlying assumptions are that these best practices (i) are indeed relevant and (ii) help to keep OSGi systems manageable and efficient. In this paper, we investigate these assumptions by first conducting a systematic review of the best practices related to dependency management issued by the OSGi Alliance and OSGi-endorsed organizations. Using a large corpus of OSGi bundles (1,124 core plug-ins of the Eclipse IDE), we then analyze the use and impact of 6 selected best practices. Our results show that the selected best practices are not widely followed in practice. Besides, we observe that following them strictly reduces classpath size of individual bundles by up to 23% and results in up to ±13% impact on performance at bundle resolution time. In summary, this paper contributes an initial empirical validation of industry-standard OSGi best practices. Our results should influence practitioners especially, by providing evidence of the impact of these best practices in real-world systems.

Domain-Specific Languages (DSLs) manifest themselves in remarkably diverse shapes, ranging from internal DSLs embedded as a mere fluent API within a programming language, to external DSLs with dedicated syntax and tool support. Although different shapes have different pros and cons, combining them for a single language is problematic: language designers usually commit to a particular shape early in the design process, and it is hard to reconsider this choice later. In this new ideas paper, we envision a language engineering approach enabling (i) language users to manipulate language constructs in the most appropriate shape according to the task at hand, and (ii) language designers to combine the strengths of different technologies for a single DSL. We report on early experiments and lessons learned building , our prototype approach to this problem. We illustrate its applicability in the engineering of a simple shape-diverse DSL implemented conjointly in Rascal, EMF, and Java. We hope that our initial contribution will raise the awareness of the community and encourage future research.

The goal of modular language development is to enable the definition of new languages as assemblies of pre-existing ones. Recent approaches in this area are plentiful but usually suffer from two main problems: either they do not support modular language composition both at the specification and implementation levels, or they require advanced knowledge of specific paradigms which hampers wide adoption in the industry. In this paper, we introduce a non-intrusive approach to modular development of language concerns with well-defined interfaces that can be composed modularly at the specification and implementation levels. We present an implementation of our approach atop the Eclipse Modeling Framework, namely Alex, an object-oriented meta-language for semantics definition and language composition. We evaluate Alex in the development of a new DSL for IoT systems modeling resulting from the composition of three independently defined languages (UML activity diagrams, Lua, and the OMG Interface Description Language). We evaluate the effort required to implement and compose these languages using Alex with regards to similar approaches of the literature.

Live modeling enables modelers to incrementally update models as they are running and get immediate feedback about the impact of their changes. Changes introduced in a model may trigger inconsistencies between the model and its run-time state (e.g., deleting the current state in a statemachine); effectively requiring to migrate the run-time state to comply with the updated model. In this paper, we introduce an approach that enables to automatically migrate such run-time state based on declarative constraints defined by the language designer. We illustrate the approach using Nextep, a meta-modeling language for defining invariants and migration constraints on run-time state models. When a model changes, Nextep employs model finding techniques, backed by a solver, to automatically infer a new run-time model that satisfies the declared constraints. We apply Nextep to define migration strategies for two DSLs, and report on its expressiveness and performance.

Executable Domain-Specific Modeling Languages (xDSMLs) are typically defined by metamodels that specify their abstract syntax, and model interpreters or compilers that define their execution semantics. To face the proliferation of xDSMLs in many domains, it is important to provide language engineering facilities for opportunistic reuse, extension, and customization of existing xDSMLs to ease the definition of new ones. Current approaches to language reuse either require to anticipate reuse, make use of advanced features that are not widely available in programming languages, or are not directly applicable to metamodel-based xDSMLs. In this paper, we propose a new language implementation pattern, named Revisitor, that enables independent extensibility of the syntax and semantics of metamodel-based xDSMLs with incremental compilation and without anticipation. We seamlessly implement our approach alongside the compilation chain of the Eclipse Modeling Framework, thereby demonstrating that it is directly and broadly applicable in various modeling environments. We show how it can be employed to incrementally extend both the syntax and semantics of the fUML language without requiring anticipation or re-compilation of existing code, and with acceptable performance penalty compared to classical handmade visitors.

The proliferation of independently-developed and constantly-evolving domain-specific languages (DSLs) in many domains raises new challenges for the software language engineering community. Instead of starting the definition of new DSLs from scratch, language designers would benefit from the reuse of previously defined DSLs. While the support for engineering isolated DSLs is getting more and more mature, there is still little support in language workbenches for importing, assembling, and customizing legacy languages to form new ones. Melange is a new language workbench where new DSLs are built by assembling pieces of syntax and semantics. These pieces can be imported and subsequently extended, restricted, or customized to fit specific requirements. The demonstration will introduce the audience to the main features of Melange through the definition of an executable DSL for the design and execution of Internet of Things systems. Specifically, we will show how such a language can be obtained from the assembly of other popular languages while maintaining the compatibility with their tools and transformations.

Domain-Specific Languages (DSLs) are now developed for a wide variety of domains to address specific concerns in the development of complex systems. When engineering new DSLs, it is likely that previous efforts spent on the development of other languages could be leveraged, especially when their domains overlap. However, legacy DSLs may not fit exactly the end user requirements and thus require further extension, restriction, or specialization. While current language workbenches provide import mechanisms, they usually lack an explicit support for such customizations of imported artifacts. In this paper, we propose an approach for building DSLs by safely assembling and customizing legacy DSLs artifacts. This approach is based on typing relations that provide a reasoning layer for manipulating DSLs while ensuring type safety. On top of this reasoning layer, we provide an algebra of operators for extending, restricting, and assembling separate DSL artifacts. We implemented the typing relations and algebra into the Melange meta-language. We illustrate Melange through the modular definition of an executable modeling language for the Internet Of Things domain. We show how it eases the definition of new DSLs by maximizing the reuse of legacy artifacts without introducing issues in terms of performance, technical ecosystem compatibility, or generated code volume.

In systems engineering, the deployment of software components is error-prone since numerous safety and security rules have to be preserved. Furthermore, many deployments on different heterogeneous platforms are possible. In this paper we present a technological solution to assist industrial practitioners in producing a safe and secure solution out of numerous architectural variants. First, we introduce a pattern technology that provides correct-by-construction deployment models through the reuse of modeling artifacts organized in a catalog. Second, we develop a variability solution, connected to the pattern technology and based on an extension of the common variability language, for supporting the synthesis of model-based architectural variants. This paper describes a live demonstration of an industrial effort seeking to bridge the gap between variability modeling and model-based systems engineering practices. We illustrate the tooling support with an industrial case study (a secure radio platform).

Summary A benefit of model‐driven engineering relies on the automatic generation of artefacts from high‐level models through intermediary levels using model transformations. In such a process, the input must be well designed, and the model transformations should be trustworthy. Because of the specificities of models and transformations, classical software test techniques have to be adapted. Among these techniques, mutation analysis has been ported, and a set of mutation operators has been defined. However, it currently requires considerable manual work and suffers from the test data set improvement activity. This activity is a difficult and time‐consuming job and reduces the benefits of the mutation analysis. This paper addresses the test data set improvement activity. Model transformation traceability in conjunction with a model of mutation operators and a dedicated algorithm allow to automatically or semi‐automatically produce improved test models. The approach is validated and illustrated in two case studies written in Kermeta.Copyright © 2014 John Wiley & Sons, Ltd.