{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:07:23Z","timestamp":1778170043633,"version":"3.51.4"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&#x0026;D Program of China","award":["2024YFB4506400"],"award-info":[{"award-number":["2024YFB4506400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IIEEE Trans. Software Eng."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1109\/tse.2025.3605442","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T17:31:02Z","timestamp":1756834262000},"page":"2957-2971","source":"Crossref","is-referenced-by-count":6,"title":["Towards Explainable Vulnerability Detection With Large Language Models"],"prefix":"10.1109","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7259-1087","authenticated-orcid":false,"given":"Qiheng","family":"Mao","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4909-1535","authenticated-orcid":false,"given":"Zhenhao","family":"Li","sequence":"additional","affiliation":[{"name":"York University, Toronto, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0093-3292","authenticated-orcid":false,"given":"Xing","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-615X","authenticated-orcid":false,"given":"Kui","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6302-3256","authenticated-orcid":false,"given":"Xin","family":"Xia","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8799-6020","authenticated-orcid":false,"given":"Jianling","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"10197","article-title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks","volume":"32","author":"Zhou","year":"2019","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468597"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3639110"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1515\/itit-2016-0037"},{"key":"ref5","article-title":"Rough-auditing-tool-for-security","year":"2025"},{"key":"ref6","article-title":"Automatic feature learning for vulnerability prediction","author":"Dam","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23158"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2024.3427815"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ASE56229.2023.00045"},{"key":"ref11","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/APSEC60848.2023.00085"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3708522"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/SANER60148.2024.00102"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3715738"},{"key":"ref16","first-page":"55006","article-title":"LIMA: Less is more for alignment","volume":"36","author":"Zhou","year":"2023","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref17","article-title":"The vulnerability is in the details: Locating fine-grained information of vulnerable code identified by graph-based detectors","author":"Cheng","year":"2024"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3051525"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3607199.3607242"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/icse55347.2025.00038"},{"key":"ref21","article-title":"Evaluation of ChatGPT model for vulnerability detection","author":"Cheshkov","year":"2023"},{"key":"ref22","article-title":"From generation to judgment: Opportunities and challenges of LLM-as-a-judge","author":"Li","year":"2024"},{"key":"ref23","article-title":"Instruction tuning for large language models: A survey","author":"Zhang","year":"2023"},{"key":"ref24","article-title":"Lora: Low-rank adaptation of large language models","author":"Hu","year":"2021"},{"key":"ref25","first-page":"824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref26","article-title":"Link to our replication package","year":"2025"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/icse55347.2025.00110"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/MSR66628.2025.00030"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2023.3286586"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.64"},{"key":"ref31","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref32","article-title":"Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment","author":"Xu","year":"2023"},{"key":"ref33","article-title":"How far have we gone in vulnerability detection using large language models","author":"Gao","year":"2023"},{"key":"ref34","article-title":"Chain-of-thought prompting of large language models for discovering and fixing software vulnerabilities","author":"Nong","year":"2024"},{"key":"ref35","article-title":"LLM4vuln: A unified evaluation framework for decoupling and enhancing LLMs\u2019 vulnerability reasoning","author":"Sun","year":"2024"},{"key":"ref36","article-title":"Your instructions are not always helpful: Assessing the efficacy of instruction fine-tuning for software vulnerability detection","author":"Yusuf","year":"2024"},{"key":"ref37","article-title":"A comprehensive study of the capabilities of large language models for vulnerability detection","author":"Steenhoek","year":"2024"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3076142"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3524842.3528452"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/SANER53432.2022.00114"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3524842.3527949"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE59848.2023.00026"},{"key":"ref43","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref44","article-title":"API Reference - OpenAI API","year":"2024"},{"key":"ref45","article-title":"Deepseek-v3 technical report","author":"Liu","year":"2024"},{"key":"ref46","article-title":"Mdoel Reference - CodeLlama-13b-Instruct-hf"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3639170"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3564625.3567985"},{"key":"ref51","article-title":"PEFT: State-of-the-art parameter-efficient fine-tuning methods","author":"Mangrulkar","year":"2022"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"ref53","article-title":"BERTScore: Evaluating text generation with BERT","author":"Zhang","year":"2019"},{"key":"ref54","volume-title":"Statistics in a Nutshell: A Desktop Quick Reference","author":"Boslaugh","year":"2008"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.11613\/BM.2012.031"}],"container-title":["IEEE Transactions on Software Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/32\/11207080\/11146900.pdf?arnumber=11146900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:14:01Z","timestamp":1761110041000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11146900\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":55,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tse.2025.3605442","relation":{},"ISSN":["0098-5589","1939-3520","2326-3881"],"issn-type":[{"value":"0098-5589","type":"print"},{"value":"1939-3520","type":"electronic"},{"value":"2326-3881","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]}}}