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Previous SDP methods often struggled in industrial applications, primarily due to the need for sufficient historical data. Thus, clustering\u2010based unsupervised defect prediction (CUDP) and cross\u2010project defect prediction (CPDP) emerged to address this challenge. However, the former exhibited limitations in capturing semantic and structural features, while the latter encountered constraints due to differences in data distribution across projects. Therefore, we introduce a novel framework called improved clustering with graph\u2010embedding\u2010based features (IC\u2010GraF) for SDP without the reliance on historical data. First, a preprocessing operation is performed to extract program dependence graphs (PDGs) and mark distinct dependency relationships within them. Second, the improved deep graph infomax (IDGI) model, an extension of the DGI model specifically for SDP, is designed to generate graph\u2010level representations of PDGs. Finally, a heuristic\u2010based k\u2010means clustering algorithm is employed to classify the features generated by IDGI. To validate the efficacy of IC\u2010GraF, we conduct experiments based on 24 releases of the PROMISE dataset, using F\u2010measure and G\u2010measure as evaluation criteria. The findings indicate that IC\u2010GraF achieves 5.0%\u221242.7% higher F\u2010measure, 5%\u221239.4% higher G\u2010measure, and 2.5%\u221211.4% higher AUC over existing CUDP methods. Even when compared with eight supervised learning\u2010based SDP methods, IC\u2010GraF maintains a superior competitive edge.<\/jats:p>","DOI":"10.1049\/2024\/8027037","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T06:22:12Z","timestamp":1726467732000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["IC\u2010GraF: An Improved Clustering with Graph\u2010Embedding\u2010Based Features for Software Defect Prediction"],"prefix":"10.1049","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6294-2134","authenticated-orcid":false,"given":"Xuanye","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6372-7088","authenticated-orcid":false,"given":"Lu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-8799","authenticated-orcid":false,"given":"Qingyan","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9400-0844","authenticated-orcid":false,"given":"Haishan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2024,9,16]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"crossref","unstructured":"WangS. 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