{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:46:22Z","timestamp":1782405982984,"version":"3.54.5"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172097, and 62402108"],"award-info":[{"award-number":["62172097, and 62402108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian-Xinjiang Interregional Cooperation Project","award":["2024I0031"],"award-info":[{"award-number":["2024I0031"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Archit. Code Optim."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Enhancing software reliability mitigates failures and reduces maintenance costs. In the LLVM compiler, different option sequences applied to the intermediate representation (IR) produce binaries with varying reliability levels, making the search for an optimal sequence in reliability-oriented compilation a key challenge. Although reinforcement learning (RL) has been employed to automate this process, existing methods suffer from two major limitations: they rely on structure-based static IR embeddings that overlook the dynamic behaviors induced by compilation transformations, leading to low-fidelity state representations, and construct action spaces that fail to preserve critical inter-option dependencies while maintaining spatial compactness, thereby impairing training efficiency and constraining optimization gains. This article presents Optimizing Software Reliability at Compile-time using Reinforcement Learning (OSRC-RL), which integrates two novel components: a Compilation Behavior-Aware State Representation (CBA-SR) and an Action Space Construction via Option Dependency Awareness and space minimization (ASC-ODA). CBA-SR jointly encodes program semantics and compilation dynamics, using activated options as multi-label supervisory signals to guide an expressive Graph Isomorphism Network in learning IR subgraph patterns correlated with option activations, yielding high-fidelity states that enable more reliable policy learning and faster convergence. ASC-ODA reconciles dependency preservation with spatial compactness by constructing Option Dependency Graph, performing dependency-aware hierarchical clustering and recursive intra-cluster path analysis, and applying a preference-aware selection to generate a compact yet dependency-preserving action space that enhances sample efficiency and policy stability. Evaluations across 20 benchmarks show that OSRC-RL achieves the highest average reliability gain (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(I_{rg}{=}0.6694\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) and competitive convergence speed compared with five baselines. Ablations attribute these gains to its core components: CBA-SR (up to 11.34% improvement via IR-graph learning) and ASC-ODA (up to 15.22% improvement via dependency-faithful actions). On industrial instances, it attains a 0.1818 average gain, surpassing the next-best method by 33.77%. Bounded overheads are mitigated by Lightweight Post-Processing (LPP), ensuring practical feasibility.\n                  <\/jats:p>","DOI":"10.1145\/3801975","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T04:04:02Z","timestamp":1775016242000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["OSRC-RL: Optimizing Software Reliability at Compile-Time Using Reinforcement Learning"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4280-3995","authenticated-orcid":false,"given":"Hanjiang","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youcong","family":"Ni","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Du","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifu","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingbang","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Computer and Cyber Security, Fujian Normal University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongji","family":"Xu","sequence":"additional","affiliation":[{"name":"Fujian Transportation Research Institute Co., Ltd.","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","article-title":"IEEE recommended practice on software reliability","year":"2017","unstructured":"2017. IEEE recommended practice on software reliability. IEEE Std 1633-2016 (Revision of IEEE Std 1633-2008) (2017), 1\u2013261.","journal-title":"IEEE Std 1633-2016 (Revision of IEEE Std 1633-2008)"},{"key":"e_1_3_1_3_2","volume-title":"Proceedings of the Machine Learning for Computer Architecture and Systems 2022","author":"Almakki Mohammed","year":"2022","unstructured":"Mohammed Almakki, Ayman Izzeldin, Qijing Huang, Ameer Haj Ali, and Chris Cummins. 2022. Autophase v2: Towards function level phase ordering optimization. In Proceedings of the Machine Learning for Computer Architecture and Systems 2022."},{"key":"e_1_3_1_4_2","first-page":"40","article-title":"code2vec: Learning distributed representations of code","volume":"3","author":"Alon Uri","year":"2019","unstructured":"Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. code2vec: Learning distributed representations of code. Proc. ACM Program. Lang. 3, POPL, Article 40 (Jan.2019), 29 pages.","journal-title":"Proc. ACM Program. Lang."},{"key":"e_1_3_1_5_2","first-page":"52","volume-title":"Proceedings of the 2017 IEEE 11th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC)","author":"Asher Yosi Ben","year":"2017","unstructured":"Yosi Ben Asher, Gadi Haber, and Esti Stein. 2017. A study of conflicting pairs of compiler optimizations. In Proceedings of the 2017 IEEE 11th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC). 52\u201358."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872421.2872424"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Amir H. Ashouri Andrea Bignoli Gianluca Palermo Cristina Silvano Sameer Kulkarni and John Cavazos. 2017. MiCOMP: Mitigating the compiler phase-ordering problem using optimization sub-sequences and machine learning. ACM Trans. Archit. Code Optim. 14 3 Article 29 (Sept.2017) 28.","DOI":"10.1145\/3124452"},{"issue":"5","key":"e_1_3_1_8_2","first-page":"96","article-title":"A survey on compiler autotuning using machine learning","volume":"51","author":"Ashouri Amir H.","year":"2018","unstructured":"Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, and Cristina Silvano. 2018. A survey on compiler autotuning using machine learning. ACM Comput. Surv. 51, 5, Article 96 (Sept.2018), 42 pages.","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"e_1_3_1_9_2","first-page":"21","article-title":"COBAYN: Compiler autotuning framework using bayesian networks","volume":"13","author":"Ashouri Amir Hossein","year":"2016","unstructured":"Amir Hossein Ashouri, Giovanni Mariani, Gianluca Palermo, Eunjung Park, John Cavazos, and Cristina Silvano. 2016. COBAYN: Compiler autotuning framework using bayesian networks. ACM Trans. Archit. Code Optim. 13, 2, Article 21 (June2016), 25 pages.","journal-title":"ACM Trans. Archit. Code Optim."},{"key":"e_1_3_1_10_2","series-title":"NIPS\u201918","first-page":"3589","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Ben-Nun Tal","year":"2018","unstructured":"Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018. Neural code comprehension: A learnable representation of code semantics. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montr\u00e9al, Canada) (NIPS\u201918). Curran Associates Inc., Red Hook, NY, USA, 3589\u20133601."},{"key":"e_1_3_1_11_2","series-title":"Proceedings of Machine Learning Research","first-page":"2244","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Cummins Chris","year":"2021","unstructured":"Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael F. P. O\u2019Boyle, and Hugh Leather. 2021. ProGraML: A graph-based program representation for data flow analysis and compiler optimizations. In Proceedings of the 38th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 2244\u20132253."},{"key":"e_1_3_1_12_2","series-title":"CGO \u201922","first-page":"92","volume-title":"Proceedings of the 20th IEEE\/ACM International Symposium on Code Generation and Optimization","author":"Cummins Chris","year":"2022","unstructured":"Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, et al. 2022. CompilerGym: Robust, performant compiler optimization environments for AI research. In Proceedings of the 20th IEEE\/ACM International Symposium on Code Generation and Optimization (Virtual Event, Republic of Korea) (CGO \u201922). IEEE Press, 92\u2013105."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/b97673"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_3_1_15_2","series-title":"GECCO \u201916 Companion","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1145\/2908961.2931696","volume-title":"Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion","author":"Garciarena Unai","year":"2016","unstructured":"Unai Garciarena and Roberto Santana. 2016. Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion (Denver, Colorado, USA) (GECCO \u201916 Companion). Association for Computing Machinery, New York, NY, USA, 1159\u20131166."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368826.3377928"},{"key":"e_1_3_1_17_2","unstructured":"Ameer Haj-Ali Qijing (Jenny) Huang John Xiang William Moses Krste Asanovic John Wawrzynek and Ion Stoica. 2020. AutoPhase: Juggling HLS phase orderings in random forests with deep reinforcement learning. In Proceedings of the 3rd MLSys Conference (Austin TX USA 2020) 2 (2020) 70\u201381."},{"key":"e_1_3_1_18_2","series-title":"SC \u201920","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","author":"Hu Yuwei","year":"2020","unstructured":"Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, and Yida Wang. 2020. FeatGraph: A flexible and efficient backend for graph neural network systems. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Atlanta, Georgia) (SC \u201920). IEEE Press, Article 71, 13 pages."},{"issue":"9","key":"e_1_3_1_19_2","first-page":"2012","article-title":"Compiler optimization sequence selection method based on learning model","volume":"56","author":"Hui Liu","year":"2019","unstructured":"Liu Hui, Xu Jinlong, Zhao Rongcai, and Yao Jinyang. 2019. Compiler optimization sequence selection method based on learning model. J. Comput. Res. Dev. 56, 9 (2019), 2012\u20132026.","journal-title":"J. Comput. Res. Dev."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS55109.2022.00012"},{"key":"e_1_3_1_21_2","first-page":"347","volume-title":"Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","author":"Jiang Wu","year":"2020","unstructured":"Wu Jiang, Xu Jianjun, Meng Xiankai, Zhang Zhuo, Zhang Nan, and Zhang Haoyu. 2020. High-reliability compilation optimization sequence generation framework based ANN. In Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). 347\u2013355."},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Dexter C. Kozen. 1992. The design and analysis of algorithms. Springer New York. (1992) 19\u201324.","DOI":"10.1007\/978-1-4612-4400-4_4"},{"key":"e_1_3_1_23_2","first-page":"1","volume-title":"Proceedings of the 2020 Forum for Specification and Design Languages (FDL)","author":"Leather Hugh","year":"2020","unstructured":"Hugh Leather and Chris Cummins. 2020. Machine learning in compilers: Past, present and future. In Proceedings of the 2020 Forum for Specification and Design Languages (FDL). 1\u20138."},{"key":"e_1_3_1_24_2","series-title":"Proceedings of Machine Learning Research","first-page":"3053","volume-title":"Proceedings of the 35th International Conference on Machine Learning","volume":"80","author":"Liang Eric","year":"2018","unstructured":"Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. 2018. RLlib: Abstractions for distributed reinforcement learning. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 80), Jennifer Dy and Andreas Krause (Eds.). PMLR, 3053\u20133062."},{"key":"e_1_3_1_25_2","series-title":"ICML\u201923","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Liang Youwei","year":"2023","unstructured":"Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, and Yuandong Tian. 2023. Learning compiler pass orders using coreset and normalized value prediction. In Proceedings of the 40th International Conference on Machine Learning (Honolulu, Hawaii, USA) (ICML\u201923). JMLR.org, Article 855, 17 pages."},{"key":"e_1_3_1_26_2","series-title":"IJCAI \u201923","volume-title":"Proceedings of the 32nd International Joint Conference on Artificial Intelligence","author":"Liu Chuang","year":"2023","unstructured":"Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, and Dacheng Tao. 2023. Graph pooling for graph neural networks: progress, challenges, and opportunities. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (Macao, P.R.China) (IJCAI \u201923). Article 752, 11 pages."},{"issue":"1","key":"e_1_3_1_27_2","first-page":"2","article-title":"Iterative compilation optimization based on metric learning and collaborative filtering","volume":"19","author":"Liu Hongzhi","year":"2021","unstructured":"Hongzhi Liu, Jie Luo, Ying Li, and Zhonghai Wu. 2021. Iterative compilation optimization based on metric learning and collaborative filtering. ACM Trans. Archit. Code Optim. 19, 1, Article 2 (Dec.2021), 25 pages.","journal-title":"ACM Trans. Archit. Code Optim."},{"key":"e_1_3_1_28_2","first-page":"11","volume-title":"Proceedings of the 2015 IEEE International Conference on Software Quality, Reliability and Security","author":"Lu Qining","year":"2015","unstructured":"Qining Lu, Mostafa Farahani, Jiesheng Wei, Anna Thomas, and Karthik Pattabiraman. 2015. LLFI: An intermediate code-level fault injection tool for hardware faults. In Proceedings of the 2015 IEEE International Conference on Software Quality, Reliability and Security. 11\u201316."},{"issue":"3","key":"e_1_3_1_29_2","first-page":"88","article-title":"Configurable detection of SDC-causing errors in programs","volume":"16","author":"Lu Qining","year":"2017","unstructured":"Qining Lu, Guanpeng Li, Karthik Pattabiraman, Meeta S. Gupta, and Jude A. Rivers. 2017. Configurable detection of SDC-causing errors in programs. ACM Trans. Embed. Comput. Syst. 16, 3, Article 88 (March2017), 25 pages.","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"e_1_3_1_30_2","series-title":"CASES \u201914","volume-title":"Proceedings of the 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems","author":"Lu Qining","year":"2014","unstructured":"Qining Lu, Karthik Pattabiraman, Meeta S. Gupta, and Jude A. Rivers. 2014. SDCTune: A model for predicting the SDC proneness of an application for configurable protection. In Proceedings of the 2014 International Conference on Compilers, Architecture and Synthesis for Embedded Systems (New Delhi, India) (CASES \u201914). Association for Computing Machinery, New York, NY, USA, Article 23, 10 pages."},{"key":"e_1_3_1_31_2","first-page":"1","volume-title":"Proceedings of the 2020 IEEE\/ACM 6th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC) and Workshop on Hierarchical Parallelism for Exascale Computing (HiPar)","author":"Mammadli Rahim","year":"2020","unstructured":"Rahim Mammadli, Ali Jannesari, and Felix Wolf. 2020. Static neural compiler optimization via deep reinforcement learning. In Proceedings of the 2020 IEEE\/ACM 6th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC) and Workshop on Hierarchical Parallelism for Exascale Computing (HiPar). 1\u201311."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460945.3464952"},{"key":"e_1_3_1_33_2","doi-asserted-by":"crossref","unstructured":"Luiz G. A. Martins Ricardo Nobre Jo\u00e3o M. P. Cardoso Alexandre C. B. Delbem and Eduardo Marques. 2016. Clustering-based selection for the exploration of compiler optimization sequences. ACM Trans. Archit. Code Optim. 13 1 Article 8 (March2016) 28 pages.","DOI":"10.1145\/2883614"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045594"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","unstructured":"Youcong Ni Xin Du Peng Ye Ruliang Xiao Yuan Yuan and Wangbiao Li. 2019. Frequent pattern mining assisted energy consumption evolutionary optimization approach based on surrogate model at GCC compile time. Swarm and Evolutionary Computation 50 (2019) 100574.","DOI":"10.1016\/j.swevo.2019.100574"},{"issue":"5","key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1145\/2980930.2907959","article-title":"A graph-based iterative compiler pass selection and phase ordering approach","volume":"51","author":"Nobre Ricardo","year":"2016","unstructured":"Ricardo Nobre, Luiz G. A. Martins, and Jo\u00e3o M. P. Cardoso. 2016. A graph-based iterative compiler pass selection and phase ordering approach. SIGPLAN Not. 51, 5 (June2016), 21\u201330.","journal-title":"SIGPLAN Not."},{"key":"e_1_3_1_37_2","volume-title":"LLVM Test Suite","author":"Organization LLVM","year":"2025","unstructured":"LLVM Organization. 2025. LLVM Test Suite. Retrieved March 11, 2025 from https:\/\/llvm.org\/docs\/TestSuiteGuide.html"},{"key":"e_1_3_1_38_2","series-title":"ASPLOS \u201924","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1145\/3620665.3640426","volume-title":"Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2","author":"Peng Hongwu","year":"2024","unstructured":"Hongwu Peng, Xi Xie, Kaustubh Shivdikar, Md Amit Hasan, Jiahui Zhao, Shaoyi Huang, Omer Khan, David Kaeli, and Caiwen Ding. 2024. MaxK-GNN: Extremely fast GPU kernel design for accelerating graph neural networks training. In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (La Jolla, CA, USA) (ASPLOS \u201924). Association for Computing Machinery, New York, NY, USA, 683\u2013698."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00357-018-9259-9"},{"key":"e_1_3_1_40_2","first-page":"13","article-title":"Learning based compilation of embedded applications targeting minimal energy consumption","volume":"116","author":"Sachan Akash","year":"2021","unstructured":"Akash Sachan and Bibhas Ghoshal. 2021. Learning based compilation of embedded applications targeting minimal energy consumption. J. Syst. Archit. 116, C (June2021), 13 pages.","journal-title":"J. Syst. Archit."},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"Proceedings of the Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van;den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In Proceedings of the Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings (Heraklion, Greece). Springer-Verlag, Berlin, 593\u2013607."},{"key":"e_1_3_1_42_2","first-page":"1319","volume-title":"Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)","author":"Sharma Vishal Chandra","year":"2016","unstructured":"Vishal Chandra Sharma, Ganesh Gopalakrishnan, and Sriram Krishnamoorthy. 2016. Towards resiliency evaluation of vector programs. In Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 1319\u20131328."},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/2568326.2568328"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1177\/1094342014522573"},{"key":"e_1_3_1_45_2","unstructured":"Yilin Sun. 2022. Optimizing LLVM pass list using reinforcement learning. (2022)."},{"key":"e_1_3_1_46_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net."},{"issue":"4","key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1145\/3418463","article-title":"IR2VEC: LLVM IR based scalable program embeddings","volume":"17","author":"VenkataKeerthy S.","year":"2020","unstructured":"S. VenkataKeerthy, Rohit Aggarwal, Shalini Jain, Maunendra Sankar Desarkar, Ramakrishna Upadrasta, and Y. N. Srikant. 2020. IR2VEC: LLVM IR based scalable program embeddings. ACM Trans. Archit. Code Optim. 17, 4, Article 32 (Dec.2020), 27 pages.","journal-title":"ACM Trans. Archit. Code Optim."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2817118"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1963.10500845"},{"issue":"9","key":"e_1_3_1_50_2","first-page":"12","article-title":"A reduction of a graph to a canonical form and an algebra arising during this reduction","volume":"2","author":"Weisfeiler Boris","year":"1968","unstructured":"Boris Weisfeiler and A. A. Lehman. 1968. A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Tech. Inf. Ser. 2, N9 (1968), 12\u201316.","journal-title":"Nauchno-Tech. Inf."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2021.3105531"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/SMC42975.2020.9283132"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2020EDL8006"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3016758"},{"key":"e_1_3_1_55_2","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?International Conference on Learning Representations (ICLR\u201919). New Orleans Louisiana United States. arxiv:1810.00826. Retrieved from https:\/\/arxiv.org\/abs\/1810.00826 (2019)"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3339363.3339382"},{"issue":"1","key":"e_1_3_1_57_2","first-page":"42","article-title":"Pinh\u00e3o: An auto-tunning system for compiler optimizations guided by hot functions","volume":"25","author":"Siraichi Marcos Yukio","year":"2019","unstructured":"Marcos Yukio Siraichi, Caio Henrique Segawa Tonetti, and Anderson Faustino da Silva. 2019. Pinh\u00e3o: An auto-tunning system for compiler optimizations guided by hot functions. J. Univers. Comput. Sci. 25, 1 (2019), 42\u201372.","journal-title":"J. Univers. Comput. Sci."},{"key":"e_1_3_1_58_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.patcog.2022.108661","article-title":"Node-feature convolution for graph convolutional networks","volume":"128","author":"Zhang Li","year":"2022","unstructured":"Li Zhang, Heda Song, Nikolaos Aletras, and Haiping Lu. 2022. Node-feature convolution for graph convolutional networks. Pattern Recogn. 128, C (Aug.2022), 12 pages.","journal-title":"Pattern Recogn."}],"container-title":["ACM Transactions on Architecture and Code Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3801975","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:54:55Z","timestamp":1782402895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3801975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,25]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6,30]]}},"alternative-id":["10.1145\/3801975"],"URL":"https:\/\/doi.org\/10.1145\/3801975","relation":{},"ISSN":["1544-3566","1544-3973"],"issn-type":[{"value":"1544-3566","type":"print"},{"value":"1544-3973","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,25]]},"assertion":[{"value":"2025-05-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}