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Configuration settings are essential for tailoring software behavior to meet specific performance requirements.
Why pcs are fragile and what we can do about it: A study of windows registry problems. In International Conference on Dependable Systems and Networks, 2004 . IEEE, 561–566
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An empirical study on configuration errors in commercial and open source systems. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles . 159–172
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Understanding and detecting real-world performance bugs
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Do not blame users for misconfigurations. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles (Farminton, Pennsylvania) (SOSP ’13) . New York, NY, USA, 244–259
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Detecting performance anti-patterns for applications developed using object-relational mapping. In Proceedings of the 36th international conference on software engineering . 1001–1012
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Tracking load-time configuration options. In Proceedings of the 29th ACM/IEEE international conference on Automated software engineering . 445–456
Max Lillack, Christian Kästner, and Eric Bodden. 2014 · 2014
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Characterizing and detecting performance bugs for smartphone applications. In Proceedings of the 36th international conference on software engineering . 1013–1024
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Caramel: Detecting and fixing performance problems that have non-intrusive fixes. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering
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Latency critical big data computing in finance
Xinhui Tian, Rui Han, Lei Wang, Gang Lu, and Jianfeng Zhan. 2015 · 2015
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Performance prediction of configurable software systems by fourier learning (t). In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 365–373
Yi Zhang, Jianmei Guo, Eric Blais, and Krzysztof Czarnecki. 2015 · 2015
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CacheOptimizer: helping developers configure caching frameworks for hibernate-based database-centric web applications. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (Seattle, WA, USA) (FSE 2016) . 666–677
Tse-Hsun Chen, Weiyi Shang, Ahmed E. Hassan, Mohamed Nasser, and Parminder Flora. 2016 · 2016
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Optimizing the performance-related configurations of object-relational mapping frameworks using a multi-objective genetic algorithm. In Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering . 309–320
Ravjot Singh, Cor-Paul Bezemer, Weiyi Shang, and Ahmed E Hassan. 2016 · 2016
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Autoconfig: Automatic configuration tuning for distributed message systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering . 29–40
Liang Bao, Xin Liu, Ziheng Xu, and Baoyin Fang. 2018 · 2018
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Finding code that explodes under symbolic evaluation
James Bornholt and Emina Torlak. 2018 · 2018
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Statically inferring performance properties of software configurations. In Proceedings of the Fifteenth European Conference on Computer Systems . 1–16
Chi Li, Shu Wang, Henry Hoffmann, and Shan Lu. 2020 · 2020
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ConfigCrusher: towards white-box performance analysis for configurable systems
Miguel Velez, Pooyan Jamshidi, Florian Sattler, Norbert Siegmund, Sven Apel, and Christian Kästner. 2020 · 2020
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Leveraging code generation to improve code retrieval and summarization via dual learning. In Proceedings of The Web Conference 2020 . 2309–2319
Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, and Shikun Zhang. 2020 · 2020
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MLASP: Machine learning assisted capacity planning: An industrial experience report
Arthur Vitui and Tse-Hsun Chen. 2021 · 2021
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Would you like a quick peek? providing logging support to monitor data processing in big data applications. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 516–526
Zehao Wang, Haoxiang Zhang, Tse-Hsun Chen, and Shaowei Wang. 2021 · 2021
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Chateval: Towards better llm-based evaluators through multi-agent debate
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. 2023 · 2023
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ChatUniTest: a ChatGPT-based automated unit test generation tool
Zhuokui Xie, Yinghao Chen, Chen Zhi, Shuiguang Deng, and Jianwei Yin. 2023 · 2023
Later among the works it cites.
No more manual tests? evaluating and improving ChatGPT for unit test generation
Zhiqiang Yuan, Yiling Lou, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, and Xin Peng. 2023 · 2023
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Retrieving multimodal information for augmented generation: A survey
Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, et al · 2023
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How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring Conversations
Eman Abdullah AlOmar, Anushkrishna Venkatakrishnan, Mohamed Wiem Mkaouer, Christian D Newman, and Ali Ouni. 2024 · 2024
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Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al · 2023
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DiagConfig: Configuration Diagnosis of Performance Violations in Configurable Software Systems. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023) . 566–578
Zhiming Chen, Pengfei Chen, Peipei Wang, Guangba Yu, Zilong He, and Genting Mai. 2023a · 2023
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Anthropomorphization of AI: opportunities and risks
Ameet Deshpande, Tanmay Rajpurohit, Karthik Narasimhan, and Ashwin Kalyan. 2023 · 2023
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Self-collaboration code generation via chatgpt
Yihong Dong, Xue Jiang, Zhi Jin, and Ge Li. 2023 · 2023
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Metagpt: Meta programming for multi-agent collaborative framework
Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al · 2023
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AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
Dong Huang, Qingwen Bu, Jie M Zhang, Michael Luck, and Heming Cui. 2023 · 2023
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LangGraph
langchain. 2023 · 2023
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Communicative agents for software development
Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
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Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang. 2024 · 2024
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Personal llm agents: Insights and survey about the capability, efficiency and security
Yuanchun Li, Hao Wen, Weijun Wang, Xiangyu Li, Yizhen Yuan, Guohong Liu, Jiacheng Liu, Wenxing Xu, Xiang Wang, Yi Sun, et al · 2024
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Configuration Validation with Large Language Models
Xinyu Lian, Yinfang Chen, Runxiang Cheng, Jie Huang, Parth Thakkar, Minjia Zhang, and Tianyin Xu. 2024 · 2024
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When llm-based code generation meets the software development process
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LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing. In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 883–883
Zeyang Ma, An Ran Chen, Dong Jae Kim, Tse-Hsun Chen, and Shaowei Wang. 2024a · 2024
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2024
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Breaking the Silence: the Threats of Using LLMs in Software Engineering. In Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results (, Lisbon, Portugal,) (ICSE-NIER’24) . Association for Computing Machinery, New York, NY, USA, 102–106
June Sallou, Thomas Durieux, and Annibale Panichella. 2024 · 2024
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Replication Package for SensitiveTeeth
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Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs
Shiwen Shan, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, and Zibin Zheng. 2024 · 2024
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Automatic Commit Message Generation: A Critical Review and Directions for Future Work
Yuxia Zhang, Zhiqing Qiu, Klaas-Jan Stol, Wenhui Zhu, Jiaxin Zhu, Yingchen Tian, and Hui Liu. 2024 · 2024
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LDB: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step
Li Zhong, Zilong Wang, and Jingbo Shang. 2024 · 2024
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