Detec\c{c}\~ao de Conflitos Sem\^anticos com Testes Gerados por LLM

TL;DR

This paper explores enhancing semantic conflict detection in code merges by integrating large language models into test generation, aiming to overcome limitations of traditional tools and improve detection accuracy.

Contribution

It introduces a novel LLM-based test generation approach within SMAT, evaluating its effectiveness for detecting semantic conflicts in complex real-world systems.

Findings

LLM-based test generation shows potential but is computationally expensive.

Traditional tools have high false negative rates in conflict detection.

LLM integration can improve semantic conflict detection in some scenarios.

Abstract

Semantic conflicts arise when a developer introduces changes to a codebase that unintentionally affect the behavior of changes integrated in parallel by other developers. Traditional merge tools are unable to detect such conflicts, so complementary tools like SMAT have been proposed. SMAT relies on generating and executing unit tests: if a test fails on the base version, passes on a developer's modified version, but fails again after merging with another developer's changes, a semantic conflict is indicated. While SMAT is effective at detecting conflicts, it suffers from a high rate of false negatives, partly due to the limitations of unit test generation tools such as Randoop and Evosuite. To investigate whether large language models (LLMs) can overcome these limitations, we propose and integrate a new test generation tool based on Code Llama 70B into SMAT. We explore the model's ability to generate tests using different interaction strategies, prompt contents, and parameter configurations. Our evaluation uses two samples: a benchmark with simpler systems from related work, and a more significant sample based on complex, real-world systems. We assess the effectiveness of the new SMAT extension in detecting conflicts. Results indicate that, although LLM-based test generation remains challenging and computationally expensive in complex scenarios, there is promising potential for improving semantic conflict detection.