Applying Transformer Models for Automatic Build Errors Classification of Java-Based Open Source Projects
Part Of
Proceedings - International Conference on Software Engineering
Start Page
282
End Page
283
ISSN
02705257
ISBN (of the container)
979-840070502-1
Date Issued
2024-04-14
Author(s)
Abstract
In open-source development, encountering build failures is a common challenge. Addressing these issues requires analyzing the causes of errors and developing solutions for fixing them. In this work, we fine-tuned Google's BERT, a well-known language model excellent in transfer learning, to address build issues in Gradle Java projects. Our strategy utilizes this model to classify error logs and identify fixing solutions. This approach extends our previous work, Gradle ACFix, an automated build error fixing system, to explore the potential of using machine learning to classify error types and identify appropriate fixing strategies for software projects. We gathered a dataset of 11,483 open-source Gradle Java projects from GitHub for this research. The model's evaluation on the error logs of these projects demonstrated a high accuracy rate exceeding 98%.
Event(s)
International Conference on Software Engineering: Companion, ICSE-Companion 2024
Publisher
ACM
Type
conference paper