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Vulnerability Detection with Interprocedural Context in Multiple Languages: Assessing Effectiveness and Cost of Modern LLMs
Kevin Lira, Baldoino Fonseca, Davy Baía +2
Large Language Models (LLMs) have been a promising way for automated vulnerability detection. However, most prior studies have explored the use of LLMs to detect vulnerabilities on…
Investigating the Performance of Small Language Models in Detecting Test Smells in Manual Test Cases
Keila Lucas, Rohit Gheyi, Márcio Ribeiro +3
Manual testing, in which testers follow natural language instructions to validate system behavior, remains crucial for uncovering issues not easily captured by automation. However,…
RefModel: Detecting Refactorings using Foundation Models
Pedro Simões, Rohit Gheyi, Rian Melo +3
Refactoring is a common software engineering practice that improves code quality without altering program behavior. Although tools like ReExtractor+, RefactoringMiner, and RefDiff…
Bugs in the Shadows: Static Detection of Faulty Python Refactorings
Jonhnanthan Oliveira, Rohit Gheyi, Márcio Ribeiro +1
Python is a widely adopted programming language, valued for its simplicity and flexibility. However, its dynamic type system poses significant challenges for automated refactoring…
Code Generation with Small Language Models: A Codeforces-Based Study
Débora Souza, Rohit Gheyi, Lucas Albuquerque +2
Large Language Models (LLMs) demonstrate capabilities in code generation, potentially boosting developer productivity. However, their adoption remains limited by high computational…
Agentic LMs: Hunting Down Test Smells
Rian Melo, Pedro Simões, Rohit Gheyi +5
Test smells reduce test suite reliability and complicate maintenance. While many methods detect test smells, few support automated removal, and most rely on static analysis or mach…