Institution profile

Universidade Federal de Minas Gerais

Academic institutionsouthamerica · br
Official website
Research library134linked papers
Opportunities0open roles
Selected work

Representative Papers

Exceptional Behaviors: How Frequently Are They Tested?

Apr 28, 2025International Conference/Workshop on Automation of Software Test

This study addresses the lack of systematic understanding regarding test coverage of anomalous behaviors in real-world systems, particularly anomalies that do not propagate to test failures. For the first time, it jointly examines both propagated and non-propagated exceptions by dynamically instrumenting 25 Python projects, monitoring 5,372 methods, 17.9 million method calls, and 1.4 million exceptions. The analysis reveals that 21.4% of methods raise exceptions, with approximately 20% of these doing so frequently—exhibiting a median rate of one exception per ten invocations. These findings challenge the conventional assumption that exceptions are rare events and demonstrate that anomalous behavior is far more prevalent in practice than previously believed.

3 citationsRead paper

Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity

Jan 26, 2026

This study addresses the lack of systematic empirical evidence regarding the real-world impact of coding agents in software development. Leveraging Mining Software Repositories (MSR) methods, it presents the first large-scale analysis of activity traces from large language model–based coding agents on GitHub, systematically identifying their behavioral patterns, potential risks, and effective usage strategies in authentic development environments. The research yields a set of empirically grounded insights concerning optimal timing for agent adoption, reliability concerns, and practical heuristics for deployment. These findings fill a critical gap in the literature, offering actionable guidance for developers and establishing a foundation for future investigations into AI-assisted programming.

2 citationsRead paper

GitHub Proxy Server: A tool for supporting massive data collection on GitHub

Oct 05, 2022Brazilian Symposium on Software Engineering

To address data collection bottlenecks in large-scale GitHub repository mining—caused by API rate limiting, IP blocking, and cross-language integration challenges—this paper proposes a lightweight, stateless, language-agnostic proxy architecture. The design employs automated credential rotation, intelligent request routing, and self-adaptive rate control to transparently manage throttling logic, thereby minimizing client-side adaptation overhead. Implemented in Node.js, it supports HTTP/HTTPS transparent proxying, token pool management, and resilient retry mechanisms. Experimental evaluation demonstrates a 3.2× throughput improvement, an error rate below 0.8%, and stable, robust metadata harvesting from over ten million open-source repositories. The core contribution is the first stateless proxy paradigm for GitHub API access, uniquely balancing scalability, generality, and engineering practicality.

2 citationsRead paper

GitEvo: Code Evolution Analysis for Git Repositories

Jan 31, 2026

Existing tools struggle to support fine-grained analysis of software code evolution effectively. To address this limitation, this work proposes GitEvo—a multilingual, extensible analysis framework that uniquely integrates Git version metadata with syntactic code structures, such as abstract syntax trees (ASTs), enabling deep co-modeling of version history and code structure for the first time. GitEvo facilitates cross-language tracking of code evolution, computation of evolutionary metrics, and interactive visualization. Its effectiveness has been validated on real-world repositories, demonstrating its utility both as a foundation for empirical software engineering research and as an educational platform for understanding the patterns and principles of software evolution.

1 citationsRead paper

Agentic Much? Adoption of Coding Agents on GitHub

Jan 26, 2026

This study presents the first systematic quantification of the real-world adoption of highly autonomous coding agents in open-source communities and their impact on software development practices. Through an analysis of engineering artifacts from 129,134 GitHub projects, augmented by metadata mining of commits and pull requests alongside identification of agent collaboration traces, we find an adoption rate ranging from 15.85% to 22.60%, demonstrating widespread penetration across varying project maturity levels, organizational types, and programming languages. Projects employing these agents exhibit larger commit sizes and a stronger focus on feature development and bug fixes, indicating deep integration into authentic development workflows. These findings provide empirical grounding for understanding AI-driven software engineering in practice.

1 citationsRead paper
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