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Eng."],"published-print":{"date-parts":[[2025,6,19]]},"abstract":"<jats:p>\n                    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\u2019s 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,\n                    <jats:sc>Legato<\/jats:sc>\n                    .\n                    <jats:sc>Legato<\/jats:sc>\n                    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,\n                    <jats:sc>Legato<\/jats:sc>\n                    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,\n                    <jats:sc>Legato<\/jats:sc>\n                    demonstrates superior performance by outperforming all the methods in comparison.\n                  <\/jats:p>","DOI":"10.1145\/3715788","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T11:15:34Z","timestamp":1750331734000},"page":"1522-1544","source":"Crossref","is-referenced-by-count":3,"title":["Improving Graph Learning-Based Fault Localization with Tailored Semi-supervised Learning"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9787-3586","authenticated-orcid":false,"given":"Chun","family":"Li","sequence":"first","affiliation":[{"name":"Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2646-4251","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Samsung Electronics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3849-3416","authenticated-orcid":false,"given":"Zhong","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4011-5350","authenticated-orcid":false,"given":"Minxue","family":"Pan","sequence":"additional","affiliation":[{"name":"Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3090-9568","authenticated-orcid":false,"given":"Xuandong","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2024. 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