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However, statisticsbased approaches based on formulae are often rigid, which calls for learning-based techniques. Amongst all,\n Grace<\/jats:italic>\n , a graph-neural network (GNN) based technique has achieved state-of-the-art due to its capacity to preserve coverage spectra, i.e., test-to-source coverage relationships, as precise abstract syntax-enhanced graph representation, mitigating the limitation of other learning-based technique which compresses the feature representation. However, such representation is not scalable due to the increasing complexity of software, correlating with increasing coverage spectra and AST graph, making it challenging to extend, let alone train the graph neural network in practice. In this work, we proposed a new graph representation,\n DepGraph<\/jats:italic>\n , that reduces the complexity of the graph representation by\n \n \n 70<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n in nodes and edges by integrating the interprocedural call graph in the graph representation of the code. Moreover, we integrate additional features\u2014code change information\u2014into the graph as attributes so the model can leverage rich historical project data. We evaluate\n DepGraph<\/jats:italic>\n using Defects4j 2.0.0, and it outperforms\n Grace<\/jats:italic>\n by locating\n \n \n 20<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n more faults in Top-1 and improving the Mean First Rank (MFR) and the Mean Average Rank (MAR) by over\n \n \n 50<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n while decreasing GPU memory usage by\n \n \n 44<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n and training\/inference time by\n \n \n 85<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n . Additionally, in cross-project settings,\n DepGraph<\/jats:italic>\n surpasses the state-of-the-art baseline with a\n \n \n 42<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n higher Top-1 accuracy, and\n \n \n 68<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n \n \n 65<\/mml:mn>\n %<\/mml:mo>\n <\/mml:math>\n <\/jats:inline-formula>\n improvement in MFR and MAR, respectively. Our study demonstrates\n DepGraph<\/jats:italic>\n \u2019s robustness, achieving state-of-the-art accuracy and scalability for future extension and adoption.\n <\/jats:p>","DOI":"10.1145\/3660793","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T10:22:09Z","timestamp":1720779729000},"page":"1937-1959","source":"Crossref","is-referenced-by-count":17,"title":["Towards Better Graph Neural Network-Based Fault Localization through Enhanced Code Representation"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4707-8985","authenticated-orcid":false,"given":"Md Nakhla","family":"Rafi","sequence":"first","affiliation":[{"name":"Concordia University, Montreal, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3181-0001","authenticated-orcid":false,"given":"Dong Jae","family":"Kim","sequence":"additional","affiliation":[{"name":"DePaul University, Chicago, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3137-7540","authenticated-orcid":false,"given":"An Ran","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Alberta, Edmonton, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4027-0905","authenticated-orcid":false,"given":"Tse-Hsun (Peter)","family":"Chen","sequence":"additional","affiliation":[{"name":"Concordia University, Montreal, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3823-1771","authenticated-orcid":false,"given":"Shaowei","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Manitoba, Winnipeg, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Rui Abreu Peter Zoeteweij and Arjan J.c. 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