Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. ACL, 65-72.
Nghi DQ Bui, Yijun Yu, and Lingxiao Jiang. 2019. Autofocus: Interpreting Attention-based Neural Networks by Code Perturbation. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering. ACM, 38-41.
Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019. When Deep Learning Met Code Search. In Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 964-974.
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2022. Deep Learning Based Vulnerability Detection: Are We There Yet? IEEE Transactions on Software Engineering 48, 9 (2022), 3280-3296.
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. ACL, 1536-1547.
Stephen R Foster, William G Griswold, and Sorin Lerner. 2012. WitchDoctor: IDE Support for Real-time Auto-completion of Refactorings. In Proceedings of the 34th International Conference on Software Engineering. ACM, 222-232.
Shuzheng Gao, Cuiyun Gao, Yulan He, Jichuan Zeng, Lunyiu Nie, Xin Xia, and Michael Lyu. 2023. Code Structure-Guided Transformer for Source Code Summarization. ACM Transactions on Software Engineering and Methodology 32, 1 (2023), 1-32.
Zi Gong, Cuiyun Gao, Yasheng Wang, Wenchao Gu, Yun Peng, and Zenglin Xu. 2022. Source Code Summarization with Structural Relative Position Guided Transformer. In Proceedings of the 29th IEEE International Conference on Software Analysis, Evolution and Reengineering. IEEE, 13-24.
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. http://OpenReview.net.
Shin Hong and Moonzoo Kim. 2013. Effective Pattern-driven Concurrency Bug Detection for Operating Systems. Journal of Systems and Software 86, 2 (2013), 377-388.
Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, and Zhi Jin. 2018. Summarizing Source Code with Transferred API Knowledge. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. ACM, 2269-2275.
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019. Codesearchnet Challenge: Evaluating the State of Semantic Code Search. ArXiv Preprint ArXiv:1909.09436 (2019).
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436-444.
Chin-Yew Lin. 2004. Rouge: A Package for Automatic Evaluation of Summaries. In Text Summarization Branches Out. ACL, 74-81.
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. 2019. Definitions, Methods, and Applications in Interpretable Machine Learning. Proceedings of the National Academy of Sciences 116, 44 (2019), 22071-22080.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: A Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. ACL, 311-318.
Md Rafiqul Islam Rabin, Vincent J Hellendoorn, and Mohammad Amin Alipour. 2021. Understanding Neural code Intelligence through Program Simplification. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 441-452.
Md Rafiqul Islam Rabin, Aftab Hussain, and Mohammad Amin Alipour. 2022. Syntax-guided program reduction for understanding neural code intelligence models. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. ACM, 70-79.
Ensheng Shi, Yanlin Wang, Lun Du, Junjie Chen, Shi Han, Hongyu Zhang, Dongmei Zhang, and Hongbin Sun. 2022. On the Evaluation of Neural Code Summarization. In Proceedings of the 44th International Conference on Software Engineering. ACM, 1597-1608.
Giriprasad Sridhara, Emily Hill, Divya Muppaneni, Lori Pollock, and K Vijay-Shanker. 2010. Towards Automatically Generating Summary Comments for Java Methods. In Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering. ACM, 43-52.
Giriprasad Sridhara, Lori Pollock, and K Vijay-Shanker. 2011. Automatically Detecting and Describing High Level Actions within Methods. In Proceedings of the 33rd International Conference on Software Engineering. ACM, 101-110.
Chengnian Sun, Yuanbo Li, Qirun Zhang, Tianxiao Gu, and Zhendong Su. 2018. Perses: Syntax-Guided Program Reduction. In Proceedings of the 40th International Conference on Software Engineering. ACM, 361-371.
Sahil Suneja, Yunhui Zheng, Yufan Zhuang, Jim A Laredo, and Alessandro Morari. 2021. Probing Model Signal-awareness via Prediction-preserving Input Minimization. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 945-955.
Ze Tang, Xiaoyu Shen, Chuanyi Li, Jidong Ge, Liguo Huang, Zhelin Zhu, and Bin Luo. 2022. AST-Trans: Code Summarization with Efficient Tree-Structured Attention. In Proceedings of the 44th International Conference on Software Engineering. ACM, 150-162.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems. Mit Press.
Petar Velikovi, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In Proceddings of the 6th International Conference on Learning Representations. http://OpenReview.net.
Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, and Philip S Yu. 2018. Improving Automatic Source Code Summarization via Deep Reinforcement Learning. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. ACM, 397-407.
Yu Wang, Ke Wang, and Linzhang Wang. 2021. WheaCha: A Method for Explaining the Predictions of Code Summarization Models. ArXiv Preprint ArXiv:2102.04625 (2021).
Hongqiu Wu, Hai Zhao, and Min Zhang. 2021. Code Summarization with Structure-induced Transformer. In The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. ACL, 1078-1090.
Andreas Zeller and Ralf Hildebrandt. 2002. Simplifying and Isolating Failureinducing Input. IEEE Transactions on Software Engineering 28, 2 (2002), 183-200.
Zhaowei Zhang, Hongyu Zhang, Beijun Shen, and Xiaodong Gu. 2022. Diet Code is Healthy: Simplifying Programs for Pre-trained models of Code. In Proceedings of the 30th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 1073-1084.
