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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>Deep learning has demonstrated its effectiveness in software vulnerability detection, but acquiring a large number of labeled code snippets for training deep learning models is challenging due to labor-intensive annotation. With limited labeled data, complex deep learning models often suffer from overfitting and poor performance. To address this limitation, semi-supervised deep learning offers a promising approach by annotating unlabeled code snippets with pseudo-labels and utilizing limited labeled data together as training sets to train vulnerability detection models. However, applying semi-supervised deep learning for accurate vulnerability detection comes with several challenges. One challenge lies in how to select correctly pseudo-labeled code snippets as training data, while another involves mitigating the impact of potentially incorrectly pseudo-labeled training code snippets during model training. To address these challenges, we propose the semi-supervised vulnerability detection (SSVD) approach. SSVD leverages the information gain of model parameters as the certainty of the correctness of pseudo-labels and prioritizes high-certainty pseudo-labeled code snippets as training data. Additionally, it incorporates the proposed noise-robust triplet loss to maximize the separation between vulnerable and non-vulnerable code snippets to better propagate labels from labeled code snippets to nearby unlabeled snippets and utilizes the proposed noise-robust cross-entropy loss for gradient clipping to mitigate the error accumulation caused by incorrect pseudo-labels. We evaluate SSVD with nine semi-supervised approaches on four widely-used public vulnerability datasets. The results demonstrate that SSVD outperforms the baselines with an average of 29.82% improvement in terms of F1-score and 56.72% in terms of MCC. In addition, SSVD trained on a certain proportion of labeled data can outperform or closely match the performance of fully supervised LineVul and ReVeal vulnerability detection models trained on 100% labeled data in most scenarios. This indicates that SSVD can effectively learn from limited labeled data to enhance vulnerability detection performance, thereby reducing the effort required for labeling a large number of code snippets.<\/jats:p>","DOI":"10.1145\/3699602","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T22:31:07Z","timestamp":1730241067000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Less Is More: Unlocking Semi-Supervised Deep Learning for Vulnerability Detection"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4473-3068","authenticated-orcid":false,"given":"Xiao","family":"Yu","sequence":"first","affiliation":[{"name":"Huawei, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8339-313X","authenticated-orcid":false,"given":"Guancheng","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0093-3292","authenticated-orcid":false,"given":"Xing","family":"Hu","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3803-9600","authenticated-orcid":false,"given":"Jacky Wai","family":"Keung","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6302-3256","authenticated-orcid":false,"given":"Xin","family":"Xia","sequence":"additional","affiliation":[{"name":"Huawei, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,2,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","volume":"76","author":"Abdar Moloud","year":"2021","unstructured":"Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. 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