{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:26:28Z","timestamp":1743020788933,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031702389"},{"type":"electronic","value":"9783031702396"}],"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\/978-3-031-70239-6_32","type":"book-chapter","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T06:02:09Z","timestamp":1726725729000},"page":"470-484","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DeepCodeGraph: A Language Model for\u00a0Compile-Time Resource Optimization Using Masked Graph Autoencoders"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7762-0117","authenticated-orcid":false,"given":"Federico","family":"Cichetti","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6607-7367","authenticated-orcid":false,"given":"Emanuele","family":"Parisi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7323-759X","authenticated-orcid":false,"given":"Andrea","family":"Acquaviva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5155-6883","authenticated-orcid":false,"given":"Francesco","family":"Barchi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","unstructured":"Allamanis, M., et\u00a0al.: A survey of machine learning for big code and naturalness. ACM Comput. Surv. 51(4), 81:1\u201381:37 (2018). https:\/\/doi.org\/10.1145\/3212695","DOI":"10.1145\/3212695"},{"key":"32_CR2","doi-asserted-by":"publisher","unstructured":"Barchi, F., et\u00a0al.: Code mapping in heterogeneous platforms using deep learning and llvm-ir. In: Proceedings of the 56th Annual Design Automation Conference 2019. Dac \u201919. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3316781.3317789","DOI":"10.1145\/3316781.3317789"},{"key":"32_CR3","doi-asserted-by":"publisher","unstructured":"Barchi, F., et\u00a0al.: Exploration of convolutional neural network models for source code classification. Eng. Appl. Artif. Intell. 97, 104075 (2021). https:\/\/doi.org\/10.1016\/j.engappai.2020.104075","DOI":"10.1016\/j.engappai.2020.104075"},{"key":"32_CR4","unstructured":"Ben-Nun, T., et\u00a0al.: Neural code comprehension: A learnable representation of code semantics. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr\u00e9al, Canada, pp. 3589\u20133601 (2018). https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/17c3433fecc21b57000debdf7ad5c930-Abstract.html"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Bjertnes, L., et\u00a0al.: Autotuning cuda: Applying nlp techniques to ls-cat. In: Norsk IKT-konferanse for forskning og utdanning, pp. 72\u201385. No.\u00a01 (2021)","DOI":"10.1109\/ICAPAI49758.2021.9462050"},{"key":"32_CR6","unstructured":"Bjertnes, L., et\u00a0al.: LS-CAT: A large-scale CUDA autotuning dataset. CoRR abs\/2103.14409 (2021). https:\/\/arxiv.org\/abs\/2103.14409"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Bojanowski, P., et\u00a0al.: Enriching word vectors with subword information (2017)","DOI":"10.1162\/tacl_a_00051"},{"key":"32_CR8","doi-asserted-by":"publisher","unstructured":"Brauckmann, A., et\u00a0al.: Compiler-based graph representations for deep learning models of code. In: CC \u201920: 29th International Conference on Compiler Construction, San Diego, CA, USA, February 22-23, 2020, pp. 201\u2013211. ACM (2020). https:\/\/doi.org\/10.1145\/3377555.3377894","DOI":"10.1145\/3377555.3377894"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Fang, S., Monperrus, M.: Supersonic: Learning to generate source code optimisations in c\/c++. arXiv preprint arXiv:2309.14846 (2023)","DOI":"10.1109\/TSE.2024.3423769"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Cho, K., et\u00a0al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR abs\/1406.1078 (2014). http:\/\/arxiv.org\/abs\/1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"32_CR11","doi-asserted-by":"publisher","unstructured":"Cummins, C., et\u00a0al.: End-to-end deep learning of optimization heuristics. In: 26th International Conference on Parallel Architectures and Compilation Techniques, PACT 2017, Portland, OR, USA, September 9-13, 2017, pp. 219\u2013232. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/pact.2017.24","DOI":"10.1109\/pact.2017.24"},{"key":"32_CR12","doi-asserted-by":"publisher","unstructured":"Cummins, C., et\u00a0al.: Synthesizing benchmarks for predictive modeling. In: 2017 IEEE\/ACM International Symposium on Code Generation and Optimization (CGO), pp. 86\u201399 (2017). https:\/\/doi.org\/10.1109\/cgo.2017.7863731","DOI":"10.1109\/cgo.2017.7863731"},{"key":"32_CR13","unstructured":"Cummins, C., et\u00a0al.: Deep data flow analysis (2020)"},{"key":"32_CR14","unstructured":"Cummins, C., et\u00a0al.: Compilergym: Robust, performant compiler optimization environments for AI research. CoRR abs\/2109.08267 (2021). https:\/\/arxiv.org\/abs\/2109.08267"},{"key":"32_CR15","unstructured":"Cummins, C., et\u00a0al.: Programl: A graph-based program representation for data flow analysis and compiler optimizations. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 2244\u20132253. Pmlr, June 2021. https:\/\/proceedings.mlr.press\/v139\/cummins21a.html"},{"issue":"4","key":"32_CR16","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1145\/115372.115320","volume":"13","author":"R Cytron","year":"1991","unstructured":"Cytron, R., et al.: Efficiently computing static single assignment form and the control dependence graph. ACM Trans. Program. Lang. Syst. 13(4), 451\u2013490 (1991). https:\/\/doi.org\/10.1145\/115372.115320","journal-title":"ACM Trans. Program. Lang. Syst."},{"key":"32_CR17","unstructured":"Devlin, J., et\u00a0al.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018). http:\/\/arxiv.org\/abs\/1810.04805"},{"key":"32_CR18","unstructured":"Fang, C., et al.: Large language models for code analysis: Do llms really do their job? (2024)"},{"key":"32_CR19","unstructured":"Fey, M., et\u00a0al.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Hou, Z., et\u00a0al.: Graphmae: Self-supervised masked graph autoencoders (2022)","DOI":"10.1145\/3534678.3539321"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Hou, Z., et\u00a0al.: Graphmae2: A decoding-enhanced masked self-supervised graph learner (2023)","DOI":"10.1145\/3543507.3583379"},{"key":"32_CR22","doi-asserted-by":"publisher","unstructured":"Karmakar, A., Robbes, R.: What do pre-trained code models know about code? In: 2021 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 1332\u20131336 (2021). https:\/\/doi.org\/10.1109\/ASE51524.2021.9678927","DOI":"10.1109\/ASE51524.2021.9678927"},{"key":"32_CR23","unstructured":"Lattner, C., et\u00a0al.: Llvm: A compilation framework for lifelong program analysis & transformation. In: Proceedings of the 2004 International Symposium on Code Generation and Optimization (CGO\u201904). Palo Alto, California, March 2004"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Li, J., et\u00a0al.: What\u2019s behind the mask: Understanding masked graph modeling for graph autoencoders (2023)","DOI":"10.1145\/3580305.3599546"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Mou, L., et\u00a0al.: Convolutional neural networks over tree structures for programming language processing (2015)","DOI":"10.1609\/aaai.v30i1.10139"},{"key":"32_CR26","doi-asserted-by":"publisher","unstructured":"Parisi, E., et\u00a0al.: Making the most of scarce input data in deep learning-based source code classification for heterogeneous device mapping. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 41(6), 1636\u20131648 (2022). https:\/\/doi.org\/10.1109\/tcad.2021.3114617","DOI":"10.1109\/tcad.2021.3114617"},{"key":"32_CR27","doi-asserted-by":"publisher","unstructured":"Tian, Y., et\u00a0al.: Heterogeneous graph masked autoencoders. In: Williams, B., Chen, Y., Neville, J. (eds.) Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, pp. 9997\u201310005. AAAI Press (2023). https:\/\/doi.org\/10.1609\/aaai.v37i8.26192","DOI":"10.1609\/aaai.v37i8.26192"},{"key":"32_CR28","unstructured":"Tu, W., et\u00a0al.: Rare: Robust masked graph autoencoder (2023)"},{"key":"32_CR29","doi-asserted-by":"publisher","unstructured":"Vavaroutsos, P., et\u00a0al.: Towards making the most of nlp-based device mapping optimization for opencl kernels. In: 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), pp.\u00a01\u20136 (2022). https:\/\/doi.org\/10.1109\/coins54846.2022.9855002","DOI":"10.1109\/coins54846.2022.9855002"},{"key":"32_CR30","unstructured":"Wu, L., et\u00a0al.: Self-supervised learning on graphs: Contrastive, generative,or predictive (2021)"},{"key":"32_CR31","doi-asserted-by":"publisher","unstructured":"Yamaguchi, F., Golde, N., Arp, D., Rieck, K.: Modeling and discovering vulnerabilities with code property graphs. In: 2014 IEEE Symposium on Security and Privacy, pp. 590\u2013604 (2014). https:\/\/doi.org\/10.1109\/SP.2014.44","DOI":"10.1109\/SP.2014.44"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70239-6_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T06:07:51Z","timestamp":1726726071000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70239-6_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031702389","9783031702396"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70239-6_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLDB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applications of Natural Language to Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","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":"25 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nldb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nldb2024.di.unito.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}