Year: 2025
Type: article
Abstract: Due to advancements in graph neural networks, graph learning-based fault localization (GBFL) methods have achieved promising results. However, as these methods are supervised learning paradigms and deep learning is typically data-hungry, they can only be trained on fully labeled large-scale datasets. This is impractical because labeling failed tests is similar to manual fault localization, which is time-consuming and labor-intensive, leading to only a small portion of failed tests that can be labeled within limited budgets. These data labeling limitations would lead to the sub-optimal effectiveness of supervised GBFL techniques. Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model’s performance and address data labeling limitations. However, as these methods are not specifically designed for fault localization, directly utilizing them might lead to sub-optimal effectiveness. In response, we propose a novel semi-supervised GBFL framework, Legato . Legato first leverages the attention mechanism to identify and augment likely fault-unrelated sub-graphs in unlabeled graphs and then quantifies the suspiciousness distribution of unlabeled graphs to estimate pseudo-labels. Through training the model on augmented unlabeled graphs and pseudo-labels, Legato can utilize the unlabeled data to improve the effectiveness of fault localization and address the restrictions in data labeling. By extensive evaluations against 3 baselines SSL methods, Legato demonstrates superior performance by outperforming all the methods in comparison.
DOI: 10.1145/3715788
Authors: Chun Li, Hui Li, Zhong Li, Minxue Pan, Xuandong Li
Institutions: Nanjing University, Samsung Electronics (South Korea)
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G5661312949, G5734368089, G8404754680
OpenAlex ID: W4411450015