João Eduardo Montandon

Helio Victor F. Santos, Vitor Costa, Joao Eduardo Montandon et al.

Agentic code assistants are a new generation of AI systems capable of performing end-to-end software engineering tasks. While these systems promise unprecedented productivity gains, their behavior and effectiveness depend heavily on configuration files that define architectural constraints, coding practices, and tool usage policies. However, little is known about the structure and content of these configuration artifacts. This paper presents an empirical study of the configuration ecosystem of Claude Code, one of the most widely used agentic coding systems. We collected and analyzed 328 configuration files from public Claude Code projects to identify (i) the software engineering concerns and practices they specify and (ii) how these concerns co-occur within individual files. The results highlight the importance of defining a wide range of concerns and practices in agent configuration files, with particular emphasis on specifying the architecture the agent should follow.

Saymon Souza, Amanda Santana, Eduardo Figueiredo et al.

Code smells are symptoms of potential code quality problems that may affect software maintainability, thus increasing development costs and impacting software reliability. Large language models (LLMs) have shown remarkable capabilities for supporting various software engineering activities, but their use for detecting code smells remains underexplored. However, unlike the rigid rules of static analysis tools, LLMs can support flexible and adaptable detection strategies tailored to the unique properties of code smells. This paper evaluates the effectiveness of four LLMs -- DeepSeek-R1, GPT-5 mini, Llama-3.3, and Qwen2.5-Code -- for detecting nine code smells across 30 Java projects. For the empirical evaluation, we created a ground-truth dataset by asking 76 developers to manually inspect 268 code-smell candidates. Our results indicate that LLMs perform strongly for structurally straightforward smells, such as Large Class and Long Method. However, we also observed that different LLMs and tools fare better for distinct code smells. We then propose and evaluate a detection strategy that combines LLMs and static analysis tools. The proposed strategy outperforms LLMs and tools in five out of nine code smells in terms of F1-Score. However, it also generates more false positives for complex smells. Therefore, we conclude that the optimal strategy depends on whether Recall or Precision is the main priority for code smell detection.

Andre Hora, João Eduardo Montandon, Diego Elias Costa

GitHub is the largest code hosting platform, with millions of repositories spanning multiple technologies. Despite this, little is known about the actual contents of GitHub's repositories in the wild. This paper presents an initial empirical analysis to better understand the contents of real-world GitHub repositories. We analyze the files, directories, and extensions present in 10,000 GitHub repositories, as well as their evolution over ten years. Our results show major changes in GitHub over the last decade: (1) the consolidation of README.md, .gitignore, and LICENSE as standard artifacts; (2) the rise of GitHub Actions as the dominant CI/CD platform; (3) the growth of configuration formats such as TOML, YAML, and JSON, alongside a decline in XML; (4) new trends, such as the growth of Dockerfile; and (5) emerging content related to LLMs and generative AI (e.g., AGENTS.md). Based on our findings, we discuss implications, including that open source is not only evolving organically but also increasingly guided by GitHub's standards, the rise and fall of technologies, and the potential support for mining software repository studies.

Helio Victor F. dos Santos, Vitor Costa, Joao Eduardo Montandon et al.

Coding agents are increasingly used to automate software engineering tasks. To guide their behavior, these agents commonly rely on configuration files, typically named AGENTS.md or CLAUDE.md, which provide instructions about architecture, workflows, coding conventions, and testing practices. Despite their growing importance, little is known about common problems affecting the definition and maintenance of these files. In this paper, we present the first catalog of smells for coding-agent configuration files. To identify such smells, we first conducted a grey literature review and a repository mining analysis. As a result, we identified six configuration smells and proposed automated heuristics to detect them. To evaluate the prevalence of the proposed smells, we analyzed 100 popular open-source repositories containing either an AGENTS.md or a CLAUDE.md file. Our results show that configuration smells are widespread. Lint Leakage was the most common smell, affecting 62% of the files, followed by Context Bloat (42%) and Skill Leakage (35%). We further show that several smells frequently co-occur, particularly Context Bloat, Skill Leakage, and Conflicting Instructions.

Gabriel C. Ullmann, Cristiano Politowski, Yann-Gaël Guéhéneuc et al.

Project Management anti-patterns are well-documented in the software-engineering literature, and studying them allows understanding their impacts on teams and projects. The video game development industry is known for its mismanagement practices, and therefore applying this knowledge would help improving game developers' productivity and well-being. In this paper, we map project management anti-patterns to anti-patterns reported by game developers in the gray literature. We read 440 postmortems problems, identified anti-pattern candidates, and related them with definitions from the software-engineering literature. We discovered that most anti-pattern candidates could be mapped to anti-patterns in the software-engineering literature, except for Feature Creep, Feature Cuts, Working on Multiple Projects, and Absent or Inadequate Tools. We discussed the impact of the unmapped candidates on the development process while also drawing a parallel between video games and traditional software development. Future works include validating the definitions of the candidates via survey with practitioners and also considering development anti-patterns.

João Eduardo Montandon, Cristiano Politowski, Luciana Lourdes Silva et al.

Context: There is a growing demand for information on how IT companies look for candidates to their open positions. Objective: This paper investigates which hard and soft skills are more required in IT companies by analyzing the description of 20,000 job opportunities. Method: We applied open card sorting to perform a high-level analysis on which types of hard skills are more requested. Further, we manually analyzed the most mentioned soft skills. Results: Programming languages are the most demanded hard skills. Communication, collaboration, and problem-solving are the most demanded soft skills. Conclusion: We recommend developers to organize their resumé according to the positions they are applying. We also highlight the importance of soft skills, as they appear in many job opportunities.

Joao Eduardo Montandon, Luciana Lourdes Silva, Marco Tulio Valente

Software development increasingly depends on libraries and frameworks to increase productivity and reduce time-to-market. Despite this fact, we still lack techniques to assess developers expertise in widely popular libraries and frameworks. In this paper, we evaluate the performance of unsupervised (based on clustering) and supervised machine learning classifiers (Random Forest and SVM) to identify experts in three popular JavaScript libraries: facebook/react, mongodb/node-mongodb, and socketio/socket.io. First, we collect 13 features about developers activity on GitHub projects, including commits on source code files that depend on these libraries. We also build a ground truth including the expertise of 575 developers on the studied libraries, as self-reported by them in a survey. Based on our findings, we document the challenges of using machine learning classifiers to predict expertise in software libraries, using features extracted from GitHub. Then, we propose a method to identify library experts based on clustering feature data from GitHub; by triangulating the results of this method with information available on Linkedin profiles, we show that it is able to recommend dozens of GitHub users with evidences of being experts in the studied JavaScript libraries. We also provide a public dataset with the expertise of 575 developers on the studied libraries.