Luciana Lourdes Silva

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.

Jailton Coelho, Marco Tulio Valente, Luciano Milen et al.

Context: GitHub hosts an impressive number of high-quality OSS projects. However, selecting "the right tool for the job" is a challenging task, because we do not have precise information about those high-quality projects. Objective: In this paper, we propose a data-driven approach to measure the level of maintenance activity of GitHub projects. Our goal is to alert users about the risks of using unmaintained projects and possibly motivate other developers to assume the maintenance of such projects. Method: We train machine learning models to define a metric to express the level of maintenance activity of GitHub projects. Next, we analyze the historical evolution of 2,927 active projects in the time frame of one year. Results: From 2,927 active projects, 16% become unmaintained in the interval of one year. We also found that Objective-C projects tend to have lower maintenance activity than projects implemented in other languages. Finally, software tools---such as compilers and editors---have the highest maintenance activity over time. Conclusions: A metric about the level of maintenance activity of GitHub projects can help developers to select open source projects.

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.

Fabio Ferreira, Luciana Lourdes Silva, Marco Tulio Valente

Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks. However, since the renaissance of DL techniques is still very recent, we lack works that summarize and condense the most recent and relevant research conducted at the intersection of DL and SE. Therefore, in this paper, we describe the first results of a mapping study covering 81 papers about DL & SE. Our results confirm that DL is gaining momentum among SE researchers over the years and that the top-3 research problems tackled by the analyzed papers are documentation, defect prediction, and testing.

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.

Luciana Silva, Daniel Felix, Marco Tulio Valente et al.

It is widely accepted that traditional modular structures suffer from the dominant decomposition problem. Therefore, to improve current modularity views, it is important to investigate the impact of design decisions concerning modularity in other dimensions, as the evolutionary view. In this paper, we propose the ModularityCheck tool to assess package modularity using co-change clusters, which are sets of classes that usually changed together in the past. Our tool extracts information from version control platforms and issue reports, retrieves co-change clusters, generates metrics related to co-change clusters, and provides visualizations for assessing modularity. We also provide a case study to evaluate the tool. http://youtu.be/7eBYa2dfIS8