{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T12:57:27Z","timestamp":1753275447757,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031702440"},{"type":"electronic","value":"9783031702457"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70245-7_26","type":"book-chapter","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T20:18:02Z","timestamp":1725999482000},"page":"373-390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards the\u00a0Use of\u00a0Domain Knowledge to\u00a0Enhance Transformer-Based Vulnerability Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6833-896X","authenticated-orcid":false,"given":"Alessandro","family":"Marchetto","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7701-1634","authenticated-orcid":false,"given":"Rosma\u00ebl Zidane","family":"Lekeufack Foulefack","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1613\/jair.1.12228","volume":"70","author":"N Burkart","year":"2021","unstructured":"Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245\u2013317 (2021)","journal-title":"J. Artif. Intell. Res."},{"issue":"09","key":"26_CR2","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1109\/TSE.2021.3087402","volume":"48","author":"S Chakraborty","year":"2022","unstructured":"Chakraborty, S., Krishna, R., Ding, Y., Ray, B.: Deep learning based vulnerability detection: are we there yet? IEEE Trans. Softw. Eng. 48(09), 3280\u20133296 (2022)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Vulchecker: achieving more effective taint analysis by identifying sanitizers automatically. In: Proceedings of International Conference on Trust, Security and Privacy in Computing and Communications, pp. 774\u2013782 (2021)","DOI":"10.1109\/TrustCom53373.2021.00112"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Chicco, D.: Ten quick tips for machine learning in computational biology. BioData Min. 10(35) (2017)","DOI":"10.1186\/s13040-017-0155-3"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Das, S.S., Serra, E., Halappanavar, M., Pothen, A., Al-Shaer, E.: V2W-BERT: a framework for effective hierarchical multiclass classification of software vulnerabilities. In: Proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1\u201312 (2021)","DOI":"10.1109\/DSAA53316.2021.9564227"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Fu, M., Tantithamthavorn, C.: LineVul: a transformer-based line-level vulnerability prediction. In: Proceedings of International Conference on Mining Software Repositories, pp. 608\u2013620. ACM (2022)","DOI":"10.1145\/3524842.3528452"},{"key":"26_CR7","unstructured":"Guo, D., Ren, S., Lu, S., et\u00a0al.: GraphCodeBERT: pre-training code representations with data flow. In: Proceedings of International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021)"},{"key":"26_CR8","unstructured":"Jeong, S.: Integrating domain knowledge into transformer-based approaches to vulnerability detection. Master\u2019s thesis, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Ge (2023)"},{"issue":"5","key":"26_CR9","doi-asserted-by":"publisher","first-page":"4961","DOI":"10.1007\/s10489-021-02635-5","volume":"52","author":"T Kanae","year":"2022","unstructured":"Kanae, T., Kouji, Y., Aya, K., Tatsuki, K.: Confidence interval for micro-averaged F1 and macro-averaged F1 scores. Appl. Intell. 52(5), 4961\u20134972 (2022)","journal-title":"Appl. Intell."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, S., Nguyen, T.N.: Vulnerability detection with fine-grained interpretations. In: Proceedings of ACM Joint Meeting on European Software Engineering Conf. and Symposium on the Foundations of Software Engineering, ESEC\/FSE 2021, pp. 292\u2013303. Association for Computing Machinery (2021)","DOI":"10.1145\/3468264.3468597"},{"issue":"04","key":"26_CR11","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.1109\/TDSC.2021.3051525","volume":"19","author":"Z Li","year":"2022","unstructured":"Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., Chen, Z.: SySeVR: a framework for using deep learning to detect software vulnerabilities. IEEE Trans. Dependable Secure Comput. 19(04), 2244\u20132258 (2022)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: VulDeePecker: a deep learning-based system for vulnerability detection. In: Proceedings of Network and Distributed System Security Symposium, San Diego, CA. Internet Society (2018)","DOI":"10.14722\/ndss.2018.23158"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Liu, J., Ai, J., Lu, M., Wang, J., Shi, H.: Semantic feature learning for software defect prediction from source code and external knowledge. J. Syst. Softw. 204(C) (2023)","DOI":"10.1016\/j.jss.2023.111753"},{"issue":"1","key":"26_CR14","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1109\/TDSC.2020.2984505","volume":"19","author":"S Liu","year":"2022","unstructured":"Liu, S., et al.: CD-VuLD: cross-domain vulnerability discovery based on deep domain adaptation. IEEE Trans. Dependable Secure Comput. 19(1), 438\u2013451 (2022)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"26_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/978-3-030-45439-5_25","volume-title":"Advances in Information Retrieval","author":"Z Lu","year":"2020","unstructured":"Lu, Z., Du, P., Nie, J.-Y.: VGCN-BERT: augmenting BERT with graph embedding for text classification. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12035, pp. 369\u2013382. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-45439-5_25"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Mamede, C., Pinconschi, E., Abreu, R., Campos, J.: Exploring transformers for multi-label classification of Java vulnerabilities. In: Proceedings of IEEE International Conference on Software Quality, Reliability and Security (QRS), pp. 43\u201352 (2022)","DOI":"10.1109\/QRS57517.2022.00015"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Mamede, C., Pinconschi, E., Abreu, R.: A transformer-based ide plugin for vulnerability detection. In: Proceedings of International Conference on Automated Software Engineering. ASE, ACM (2023)","DOI":"10.1145\/3551349.3559534"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Marchetto, A.: Can explainability and deep-learning be used for localizing vulnerabilities in source code? In: Proceedings of International Conference on Automation of Software Test (2024)","DOI":"10.1145\/3644032.3644448"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Ponta, S.E., Plate, H., Sabetta, A., Bezzi, M., Dangremont, C.: A manually-curated dataset of fixes to vulnerabilities of open-source software, pp. 383\u2013387. MSR, IEEE Press (2019)","DOI":"10.1109\/MSR.2019.00064"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Sotgiu, A., Pintor, M., Biggio, B.: Explainability-based debugging of machine learning for vulnerability discovery. In: Proceedings of International Conference on Availability, Reliability and Security, USA. ARES, ACM (2022)","DOI":"10.1145\/3538969.3543809"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Sousa, N.D., Hasselbring, W.: JavaBERT: training a transformer-based model for the Java programming language. In: Proceedings of International Conference on Automated Software Engineering Workshops (ASEW), USA, pp. 90\u201395. IEEE (2021)","DOI":"10.1109\/ASEW52652.2021.00028"},{"key":"26_CR22","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TIFS.2020.3044773","volume":"16","author":"H Wang","year":"2021","unstructured":"Wang, H., et al.: Combining graph-based learning with automated data collection for code vulnerability detection. IEEE Trans. Inf. Forensics Secur. 16, 1943\u20131958 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, Y., Dongjin, X.: VulGraB: graph-embedding-based code vulnerability detection with bi-directional gated graph neural network. Softw. Practi. Experience 53 (2023)","DOI":"10.1002\/spe.3205"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Zeng, P., Lin, G., Pan, L., Tai, Y., Zhang, J.: Software vulnerability analysis and discovery using deep learning techniques: a survey. IEEE Access 8 (2020)","DOI":"10.1109\/ACCESS.2020.3034766"}],"container-title":["Communications in Computer and Information Science","Quality of Information and Communications Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70245-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T20:21:14Z","timestamp":1725999674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70245-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031702440","9783031702457"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70245-7_26","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"11 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"QUATIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Quality of Information and Communications Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pisa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"quatic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.quatic.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}