arXiv subjects

Matheus Paixao

Publications and source records attributed to Matheus Paixao.

7 recordsLinked to original sources

arXiv2026

Do Stack Overflow Answer Edits Occur Beyond Java? A Replication on Python and JavaScript

Stack Overflow answers are continually revised by the community, and the edits made to their code snippets are a potential source of improvements for code that has been reused in open-source projects. A recent empirical study established this for Java, reporting that 16.11% of accepted Java answers are edited and that the resulting recommendations concentrate in highly popular GitHub projects. Whether that behaviour is a property of Stack Overflow or a property of the Java community has remained an open question. We replicate the study on Python and JavaScript, the two most widely used languages alongside Java, applying the same SOTorrent-based extraction pipeline, the same clone search tool, Siamese+, and the same project popularity criteria. Analysing 840,132 accepted Python answers and 1,144,185 accepted JavaScript answers, we find that 41.25% and 39.10% respectively have been edited at least once, roughly two and a half times the Java rate, while the number of revisions per edited answer is almost invariant across the three languages at 2.78, 2.68 and 2.82. Searching 100 GitHub projects per language, we find that the number of matched answer edits increases monotonically from low- to medium- to high-popularity projects in both languages, from 80 to 156 to 977 for Python and from 32 to 71 to 353 for JavaScript. The difference is statistically significant for Python but not for JavaScript. The central findings of the original study therefore generalise beyond Java, with the supply of candidate improvements considerably larger in both replication languages than in the original.

cs.SE↗

arXiv2026

What Characterizes a Software Leader? Identifying Leadership Practices from Practitioners Social Media

Context: Leadership has been extensively studied in management and agile software development; however, prior research predominantly focuses on formal roles and predefined leadership models, offering limited insight into how leadership is experienced and demonstrated by software practitioners in everyday practice. Objective: Our goal is to identify and categorize leadership practices as perceived and reported by software development practitioners based on their professional experiences. Method: We conducted a content analysis of 116 practitioner-authored articles published on the Dev.to online community. Articles were systematically collected, screened, and coded, resulting in the extraction, correlation analysis and categorization of leadership practices grounded in practitioners narratives. Results: We identified 103 practices for software project leaders, distinguished between recommended and discouraged ones. These practices were organized into five categories: People Management & Development, Processes & Execution, Professional & Personal Growth, Communication & Articulation and Strategic Vision. The most recurrent recommended practices include Cultivating & Practicing Interpersonal Skills, Managing & Delegating Team Work, and Practicing & Developing Managerial Skills, whereas Micromanagement, Counterproductive Work Patterns, and Counterproductive Communication Styles emerged as the most frequent discouraged practices. We organized all practices into a conceptual map. Conclusion: The findings indicate that software leadership is mainly associated with managerial and interpersonal practices rather than technical expertise. The resulting conceptual map summarizes these practices and can serve as a reference for understanding leadership in software development contexts.

cs.SE↗

arXiv2025

An Empirical Study of Java Code Improvements Based on Stack Overflow Answer Edits

Suboptimal code is prevalent in software systems. Developers often write low-quality code due to factors like technical knowledge gaps, insufficient experience, time pressure, management decisions, or personal factors. Once integrated, the accumulation of this suboptimal code leads to significant maintenance costs and technical debt. Developers frequently consult external knowledge bases, such as API documentation and Q&A websites like Stack Overflow (SO), to aid their programming tasks. SO's crowdsourced, collaborative nature has created a vast repository of programming knowledge. Its community-curated content is constantly evolving, with new answers posted or existing ones edited. In this paper, we present an empirical study of SO Java answer edits and their application to improving code in open-source projects. We use a modified code clone search tool to analyze SO code snippets with version history and apply it to open-source Java projects. This identifies outdated or unoptimized code and suggests improved alternatives. Analyzing 140,840 Java accepted answers from SOTorrent and 10,668 GitHub Java projects, we manually categorized SO answer edits and created pull requests to open-source projects with the suggested code improvements. Our results show that 6.91% of SO Java accepted answers have more than one revision (average of 2.82). Moreover, 49.24% of the code snippets in the answer edits are applicable to open-source projects, and 11 out of 36 proposed bug fixes based on these edits were accepted by the GitHub project maintainers.

cs.SE↗

arXiv2025

Towards Emotionally Intelligent Software Engineers: Understanding Students' Self-Perceptions After a Cooperative Learning Experience

[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

cs.SE↗

arXiv2022

Recommending Code Improvements Based on Stack Overflow Answer Edits

Background: Sub-optimal code is prevalent in software systems. Developers may write low-quality code due to many reasons, such as lack of technical knowledge, lack of experience, time pressure, management decisions, and even unhappiness. Once sub-optimal code is unknowingly (or knowingly) integrated into the codebase of software systems, its accumulation may lead to large maintenance costs and technical debt. Stack Overflow is a popular website for programmers to ask questions and share their code snippets. The crowdsourced and collaborative nature of Stack Overflow has created a large source of programming knowledge that can be leveraged to assist developers in their day-to-day activities. Objective: In this paper, we present an exploratory study to evaluate the usefulness of recommending code improvements based on Stack Overflow answers' edits. Method: We propose Matcha, a code recommendation tool that leverages Stack Overflow code snippets with version history and code clone search techniques to identify sub-optimal code in software projects and suggest their optimised version. By using SOTorrent and GitHub datasets, we will quali-quantitatively investigate the usefulness of recommendations given by \textsc{Matcha} to developers using manual categorisation of the recommendations and acceptance of pull-requests to open-source projects.

cs.SE↗

arXiv2021

Assessing Exception Handling Testing Practices in Open-Source Libraries

Modern programming languages (e.g., Java and C#) provide features to separate error-handling code from regular code, seeking to enhance software comprehensibility and maintainability. Nevertheless, the way exception handling (EH) code is structured in such languages may lead to multiple, different, and complex control flows, which may affect the software testability. Previous studies have reported that EH code is typically neglected, not well tested, and its misuse can lead to reliability degradation and catastrophic failures. However, little is known about the relationship between testing practices and EH testing effectiveness. In this exploratory study, we (i) measured the adequacy degree of EH testing concerning code coverage (instruction, branch, and method) criteria; and (ii) evaluated the effectiveness of the EH testing by measuring its capability to detect artificially injected faults (i.e., mutants) using 7 EH mutation operators. Our study was performed using test suites of 27 long-lived Java libraries from open-source ecosystems. Our results show that instructions and branches within $\mathtt{catch}$ blocks and $\mathtt{throw}$ instructions are less covered, with statistical significance than the overall instructions and branches. Nevertheless, most of the studied libraries presented test suites capable of detecting more than 70% of the injected faults. From a total of 12,331 mutants created in this study, the test suites were able to detect 68% of them.

cs.SE↗

arXiv2019

Toxic Code Snippets on Stack Overflow

Online code clones are code fragments that are copied from software projects or online sources to Stack Overflow as examples. Due to an absence of a checking mechanism after the code has been copied to Stack Overflow, they can become toxic code snippets, e.g., they suffer from being outdated or violating the original software license. We present a study of online code clones on Stack Overflow and their toxicity by incorporating two developer surveys and a large-scale code clone detection. A survey of 201 high-reputation Stack Overflow answerers (33% response rate) showed that 131 participants (65%) have ever been notified of outdated code and 26 of them (20%) rarely or never fix the code. 138 answerers (69%) never check for licensing conflicts between their copied code snippets and Stack Overflow's CC BY-SA 3.0. A survey of 87 Stack Overflow visitors shows that they experienced several issues from Stack Overflow answers: mismatched solutions, outdated solutions, incorrect solutions, and buggy code. 85% of them are not aware of CC BY-SA 3.0 license enforced by Stack Overflow, and 66% never check for license conflicts when reusing code snippets. Our clone detection found online clone pairs between 72,365 Java code snippets on Stack Overflow and 111 open source projects in the curated Qualitas corpus. We analysed 2,289 non-trivial online clone candidates. Our investigation revealed strong evidence that 153 clones have been copied from a Qualitas project to Stack Overflow. We found 100 of them (66%) to be outdated, of which 10 were buggy and harmful for reuse. Furthermore, we found 214 code snippets that could potentially violate the license of their original software and appear 7,112 times in 2,427 GitHub projects.

cs.SE↗