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Lang."],"published-print":{"date-parts":[[2024,4,29]]},"abstract":"<jats:p>Traditional program slicing techniques are crucial for early bug detection and manual\/automated debugging of online code snippets. Nevertheless, their inability to handle incomplete code hinders their real-world applicability in such scenarios. To overcome these challenges, we present NS-Slicer, a novel learning-based approach that predicts static program slices for both complete and partial code Our tool leverages a pre-trained language model to exploit its understanding of fine-grained variable-statement dependencies within source code. With this knowledge, given a variable at a specific location and a statement in a code snippet, NS-Slicer determines whether the statement belongs to the backward slice or forward slice, respectively. We conducted a series of experiments to evaluate NS-Slicer's performance. On complete code, it predicts the backward and forward slices with an F1-score of 97.41% and 95.82%, respectively, while achieving an overall F1-score of 96.77%. Notably, in 85.20% of the cases, the static program slices predicted by NS-Slicer exactly match entire slices from the oracle. For partial programs, it achieved an F1-score of 96.77%\u201397.49% for backward slicing, 92.14%\u201395.40% for forward slicing, and an overall F1-score of 94.66%\u201396.62%. Furthermore, we demonstrate NS-Slicer's utility in vulnerability detection (VD), integrating its predicted slices into an automated VD tool. In this setup, the tool detected vulnerabilities in Java code with a high F1-score of 73.38%. We also include the analyses studying NS-Slicer\u2019s promising performance and limitations, providing insights into its understanding of intrinsic code properties such as variable aliasing, leading to better slicing.<\/jats:p>","DOI":"10.1145\/3649814","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T17:53:50Z","timestamp":1714413230000},"page":"83-109","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["A Learning-Based Approach to Static Program Slicing"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8785-6319","authenticated-orcid":false,"given":"Aashish","family":"Yadavally","sequence":"first","affiliation":[{"name":"University of Texas at Dallas, Dallas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0143-0677","authenticated-orcid":false,"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"University of Texas at Dallas, Dallas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5777-7759","authenticated-orcid":false,"given":"Shaohua","family":"Wang","sequence":"additional","affiliation":[{"name":"Central University of Finance and Economics, Bejing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7962-6090","authenticated-orcid":false,"given":"Tien N.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Texas at Dallas, Dallas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MS.2008.130","article-title":"Using Static Analysis to Find Bugs","volume":"25","author":"Ayewah Nathaniel","year":"2008","unstructured":"Nathaniel Ayewah, David Hovemeyer, J. 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