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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>In recent years, pre-trained language models have seen significant success in natural language processing and have been increasingly applied to code-related tasks. Code intelligence tasks have shown promising performance with the support of code pre-trained language models. Pre-processing code simplification methods have been introduced to prune code tokens from the model\u2019s input while maintaining task effectiveness. These methods improve the efficiency of code intelligence tasks while reducing computational costs. Post-prediction code simplification methods provide explanations for code intelligence task outcomes, enhancing the reliability and interpretability of model predictions. However, comprehensive evaluations of these methods across diverse code pre-trained model architectures and code intelligence tasks are lacking. To assess the effectiveness of code simplification methods, we conduct an empirical study integrating these code simplification methods with various pre-trained code models across multiple code intelligence tasks.<\/jats:p>\n          <jats:p>Our empirical findings suggest that developing task-specific code simplification methods would be beneficial. Then, we recommend leveraging post-prediction methods to summarize prior knowledge, which can pre-process code simplification strategies. Moreover, establishing more evaluation mechanisms for code simplification is crucial. Finally, we propose incorporating code simplification methods into the pre-training phase of code pre-trained models to enhance their program comprehension and code representation capabilities.<\/jats:p>","DOI":"10.1145\/3720540","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T10:53:26Z","timestamp":1740653606000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["An Empirical Study of Code Simplification Methods in\u00a0Code\u00a0Intelligence Tasks"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2492-2530","authenticated-orcid":false,"given":"Zongwen","family":"Shen","sequence":"first","affiliation":[{"name":"National Key Laboratory for Novel Software\u00a0Technology, Nanjing University, Nanjing,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1113-8174","authenticated-orcid":false,"given":"Yuning","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software\u00a0Technology, Nanjing University, Nanjing,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1773-0942","authenticated-orcid":false,"given":"Jidong","family":"Ge","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software\u00a0Technology, Nanjing University, Nanjing,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-3891","authenticated-orcid":false,"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Nantong University, Nantong,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9270-5072","authenticated-orcid":false,"given":"Chuanyi","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software\u00a0Technology, Nanjing University, Nanjing,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7790-0195","authenticated-orcid":false,"given":"LiGuo","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Southern Methodist University, Dallas,\u00a0TX,\u00a0USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9036-0063","authenticated-orcid":false,"given":"Bin","family":"Luo","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Novel Software\u00a0Technology, Nanjing University, Nanjing,\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Anonymous GitHub. 2024. Retrieved from https:\/\/anonymous.4open.science\/r\/code_simplify_project-414D"},{"key":"e_1_3_3_3_2","doi-asserted-by":"crossref","first-page":"86121","DOI":"10.1109\/ACCESS.2019.2918202","article-title":"A systematic review on code clone detection","volume":"7","author":"Ain Qurat Ul","year":"2019","unstructured":"Qurat Ul Ain, Wasi Haider Butt, Muhammad Waseem Anwar, Farooque Azam, and Bilal Maqbool. 2019. A systematic review on code clone detection. IEEE Access 7 (2019), 86121\u201386144.","journal-title":"IEEE Access"},{"key":"e_1_3_3_4_2","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/MSR.2013.6624029","volume-title":"2013 10th Working Conference on Mining Software Repositories (MSR)","author":"Allamanis Miltiadis","year":"2013","unstructured":"Miltiadis Allamanis and Charles Sutton. 2013. Mining source code repositories at massive scale using language modeling. In 2013 10th Working Conference on Mining Software Repositories (MSR). 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Smaranda\u2009\u2009 Muresan, Preslav\u2009\u2009 Nakov, and Aline\u2009\u2009 Villavicencio (Eds.), Association for Computational Linguistics, 7212\u20137225."},{"key":"e_1_3_3_13_2","unstructured":"Daya Guo Shuo Ren Shuai Lu Zhangyin Feng Duyu Tang Liu Shujie Long Zhou Nan Duan Alexey Svyatkovskiy Shengyu Fu et al. 2021. GraphCodeBERT: Pre-training Code Representations with Data Flow. In Proceedings of the 9th International Conference on Learning Representations (ICLR \u201921). OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=jLoC4ez43PZ"},{"key":"e_1_3_3_14_2","first-page":"263","volume-title":"20th IEEE\/ACM International Conference on Automated software engineering","author":"Gupta Neelam","year":"2005","unstructured":"Neelam Gupta, Haifeng He, Xiangyu Zhang, and Rajiv Gupta. 2005. Locating faulty code using failure-inducing chops. 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Retrieved from https:\/\/arxiv.org\/abs\/1907.11692"},{"key":"e_1_3_3_21_2","unstructured":"Shuai Lu Daya Guo Shuo Ren Junjie Huang Alexey Svyatkovskiy Ambrosio Blanco Colin Clement Dawn Drain Daxin Jiang Duyu Tang et al. 2021. Codexglue: A machine learning benchmark dataset for code understanding and generation. arXiv:2102.04664. Retrieved from https:\/\/arxiv.org\/abs\/2102.04664"},{"key":"e_1_3_3_22_2","first-page":"142","volume-title":"28th International Conference on Software Engineering","author":"Misherghi Ghassan","year":"2006","unstructured":"Ghassan Misherghi and Zhendong Su. 2006. HDD: Hierarchical delta debugging. 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ACM, New York, NY, 441\u2013452."},{"key":"e_1_3_3_29_2","first-page":"70","volume-title":"6th ACM SIGPLAN International Symposium on Machine Programming","author":"Md  Rafiqul","year":"2022","unstructured":"Md\u2009\u2009 Rafiqul Islam\u2009\u2009 Rabin, Aftab\u2009\u2009 Hussain, and Mohammad Amin Alipour. 2022. Syntax-guided program reduction for understanding neural code intelligence models. In 6th ACM SIGPLAN International Symposium on Machine Programming. ACM, New York, NY, 70\u201379."},{"key":"e_1_3_3_30_2","doi-asserted-by":"crossref","unstructured":"Rafiqul Islam Rabin and Mohammad Amin Alipour. 2022. FeatureExtractor: A tool for extracting key input features of code intelligence models. 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Queen\u2019s School of Computing TR 541, 115 (2007), 64\u201368.","journal-title":"Queen\u2019s School of Computing TR"},{"key":"e_1_3_3_33_2","first-page":"1","volume-title":"14th Asia-Pacific Symposium on Internetware","author":"Shi Chaoxuan","year":"2023","unstructured":"Chaoxuan Shi, Tingwei Zhu, Tian Zhang, Jun Pang, and Minxue Pan. 2023. Structural-semantics guided program simplification for understanding neural code intelligence models. In 14th Asia-Pacific Symposium on Internetware, 1\u201311."},{"key":"e_1_3_3_34_2","first-page":"361","volume-title":"40th International Conference on Software Engineering","author":"Sun Chengnian","year":"2018","unstructured":"Chengnian Sun, Yuanbo Li, Qirun Zhang, Tianxiao Gu, and Zhendong Su. 2018. Perses: syntax-guided program reduction. In 40th International Conference on Software Engineering. ACM, New York, NY, 361\u2013371."},{"key":"e_1_3_3_35_2","unstructured":"Weisong Sun Chunrong Fang Yun Miao Yudu You Mengzhe Yuan Yuchen Chen Quanjun Zhang An Guo Xiang Chen Yang Liu\u2009\u2009et al. 2023. Abstract syntax tree for programming language understanding and representation: How far are we? arXiv:2312.00413. Retrieved from https:\/\/arxiv.org\/abs\/2312.00413"},{"key":"e_1_3_3_36_2","first-page":"945","volume-title":"29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Suneja Sahil","year":"2021","unstructured":"Sahil Suneja, Yunhui Zheng, Yufan Zhuang, Jim A. Laredo, and Alessandro Morari. 2021. Probing model signal-awareness via prediction-preserving input minimization. In 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, New York, NY, 945\u2013955."},{"issue":"6","key":"e_1_3_3_37_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3597202","article-title":"Incorporating signal awareness in source code modeling: An application to vulnerability detection","volume":"32","author":"Suneja Sahil","year":"2023","unstructured":"Sahil Suneja, Yufan Zhuang, Yunhui Zheng, Jim Laredo, Alessandro Morari, and Udayan Khurana. 2023. Incorporating signal awareness in source code modeling: An application to vulnerability detection. ACM Transactions on Software Engineering and Methodology 32, 6 (2023), 1\u201340.","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"e_1_3_3_38_2","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/ICSME.2014.77","volume-title":"2014 IEEE International Conference on Software Maintenance and Evolution","author":"Svajlenko Jeffrey","year":"2014","unstructured":"Jeffrey Svajlenko, Judith F. Islam, Iman Keivanloo, Chanchal K. Roy, and Mohammad Mamun Mia. 2014. Towards a big data curated benchmark of inter-project code clones. In 2014 IEEE International Conference on Software Maintenance and Evolution. IEEE, 476\u2013480."},{"key":"e_1_3_3_39_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/978-981-16-1927-4_7","volume-title":"Code Clone Analysis: Research, Tools, and Practices","author":"Svajlenko Jeffrey","year":"2021","unstructured":"Jeffrey Svajlenko and Chanchal K. Roy. 2021. Bigclonebench. Code Clone Analysis: Research, Tools, and Practices. Springer, 93\u2013105."},{"key":"e_1_3_3_40_2","first-page":"9438","volume-title":"International Conference on Machine Learning","author":"Tay Yi","year":"2020","unstructured":"Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, and Da-Cheng Juan. 2020. Sparse sinkhorn attention. In International Conference on Machine Learning. PMLR, 9438\u20139447."},{"key":"e_1_3_3_41_2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.18653\/v1\/2022.blackboxnlp-1.31","volume-title":"5th BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP","author":"Troshin Sergey","year":"2022","unstructured":"Sergey Troshin and Nadezhda Chirkova. 2022. Probing pretrained models of source codes. In 5th BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 371\u2013383."},{"key":"e_1_3_3_42_2","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1109\/ICSE48619.2023.00203","volume-title":"2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE)","author":"Tufano Rosalia","year":"2023","unstructured":"Rosalia Tufano, Luca Pascarella, and Gabriele Bavota. 2023. Automating code-related tasks through transformers: The impact of pre-training. In 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 2425\u20132437."},{"key":"e_1_3_3_43_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS \u201917). Isabelle Guyon Ulrike von Luxburg Samy Bengio Hanna M. Wallach Rob Fergus S. V. N. Vishwanathan and Roman Garnett (Eds.) 5998\u20136008."},{"key":"e_1_3_3_44_2","first-page":"382","volume-title":"30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Wang Chaozheng","year":"2022","unstructured":"Chaozheng Wang, Yuanhang Yang, Cuiyun Gao, Yun Peng, Hongyu Zhang, and Michael R. Lyu. 2022. No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence. In 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 382\u2013394."},{"issue":"11","key":"e_1_3_3_45_2","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.1109\/TSE.2023.3313881","article-title":"Prompt tuning in code intelligence: An experimental evaluation","volume":"49","author":"Wang Chaozheng","year":"2023","unstructured":"Chaozheng Wang, Yuanhang Yang, Cuiyun Gao, Yun Peng, Hongyu Zhang, and Michael R. Lyu. 2023. Prompt tuning in code intelligence: An experimental evaluation. IEEE Transactions on Software Engineering 49, 11 (2023), 4869\u20134885.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_3_3_46_2","first-page":"287","volume-title":"44th International Conference on Software Engineering","author":"Wang Deze","year":"2022","unstructured":"Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, and Xiangke Liao. 2022. Bridging pre-trained models and downstream tasks for source code understanding. In 44th International Conference on Software Engineering, 287\u2013298."},{"key":"e_1_3_3_47_2","doi-asserted-by":"crossref","unstructured":"Yan Wang Xiaoning Li Tien Nguyen Shaohua Wang Chao Ni and Ling Ding. 2024. Natural is the best: Model-agnostic code simplification for pre-trained large language models. arXiv:2405.11196. Retrieved from https:\/\/arxiv.org\/abs\/2405.11196","DOI":"10.1145\/3643753"},{"key":"e_1_3_3_48_2","doi-asserted-by":"crossref","first-page":"8696","DOI":"10.18653\/v1\/2021.emnlp-main.685","volume-title":"2021 Conference on Empirical Methods in Natural Language Processing","author":"Wang Yue","year":"2021","unstructured":"Yue Wang, Weishi Wang, Shafiq Joty, and Steven C. H. Hoi. 2021. CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. 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In 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38\u201345."},{"issue":"3","key":"e_1_3_3_51_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3630010","article-title":"How important are good method names in neural code generation? A model robustness perspective","volume":"33","author":"Yang Guang","year":"2024","unstructured":"Guang Yang, Yu Zhou, Wenhua Yang, Tao Yue, Xiang Chen, and Taolue Chen. 2024. How important are good method names in neural code generation? A model robustness perspective. ACM Transactions on Software Engineering and Methodology 33, 3 (2024), 1\u201335.","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"e_1_3_3_52_2","doi-asserted-by":"crossref","unstructured":"Guang Yang Yu Zhou Xiangyu Zhang Xiang Chen Tingting Han and Taolue Chen. 2023. Assessing and improving syntactic adversarial robustness of pre-trained models for code translation. arXiv:2310.18587. 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Retrieved from https:\/\/arxiv.org\/abs\/2310.16390"},{"key":"e_1_3_3_55_2","first-page":"1073","volume-title":"30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Zhang Zhaowei","year":"2022","unstructured":"Zhaowei Zhang, Hongyu Zhang, Beijun Shen, and Xiaodong Gu. 2022. Diet code is healthy: Simplifying programs for pre-trained models of code. In 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 1073\u20131084."},{"key":"e_1_3_3_56_2","doi-asserted-by":"crossref","unstructured":"Shuyan Zhou Uri Alon Sumit Agarwal and Graham Neubig. 2023. Codebertscore: Evaluating code generation with pretrained models of code. arXiv:2302.05527. 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