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Large language models are redefining software engineering by implementing AI-powered techniques throughout the whole software development process, including requirement gathering, software architecture, code generation, testing, and deployment.
A survey of agile methodologies
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Selecting Empirical Methods for Software Engineering Research
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A multi-case study of agile requirements engineering and the use of test cases as requirements
Elizabeth Bjarnason, Michael Unterkalmsteiner, Markus Borg, and Emelie Engström. 2016 · 2016
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Agile software development methods: Review and analysis
Pekka Abrahamsson, Outi Salo, Jussi Ronkainen, and Juhani Warsta. 2017 · 2017
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Improving language understanding by generative pre-training
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Training language models to follow instructions with human feedback
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Towards human-bot collaborative software architecting with chatgpt. In Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering . 279–285
Aakash Ahmad, Muhammad Waseem, Peng Liang, Mahdi Fahmideh, Mst Shamima Aktar, and Tommi Mikkonen. 2023 · 2023
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Is ChatGPT leading generative AI? What is beyond expectations?
Ömer Aydın and Enis Karaarslan. 2023 · 2023
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Large language model assisted software engineering: prospects, challenges, and a case study. In International Conference on Bridging the Gap between AI and Reality . Springer, 355–374
Lenz Belzner, Thomas Gabor, and Martin Wirsing. 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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Generative AI for software practitioners
Christof Ebert and Panos Louridas. 2023 · 2023
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Investigating Code Generation Performance of Chat-GPT with Crowdsourcing Social Data. In Proceedings of the 47th IEEE Computer Software and Applications Conference . 1–10
Yunhe Feng, Sreecharan Vanam, Manasa Cherukupally, Weijian Zheng, Meikang Qiu, and Haihua Chen. 2023 · 2023
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Regulating ChatGPT and other large generative AI models. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency . 1112–1123
Philipp Hacker, Andreas Engel, and Marco Mauer. 2023 · 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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GPT understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2023 · 2023
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The Scope of ChatGPT in Software Engineering: A Thorough Investigation
Wei Ma, Shangqing Liu, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, and Yang Liu. 2023 · 2023
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Experiences from using code explanations generated by large language models in a web software development e-book. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 . 931–937
Stephen MacNeil, Andrew Tran, Arto Hellas, Joanne Kim, Sami Sarsa, Paul Denny, Seth Bernstein, and Juho Leinonen. 2023 · 2023
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Generative Artificial Intelligence for Software Engineering–A Research Agenda
Anh Nguyen-Duc, Beatriz Cabrero-Daniel, Adam Przybylek, Chetan Arora, Dron Khanna, Tomas Herda, Usman Rafiq, Jorge Melegati, Eduardo Guerra, Kai-Kristian Kemell, et al · 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
Cited alongside, same era.
Autonomous Agents in Software Development: A Vision Paper
Zeeshan Rasheed, Muhammad Waseem, Kai-Kristian Kemell, Wang Xiaofeng, Anh Nguyen Duc, Kari Systä, and Pekka Abrahamsson. 2023 · 2023
Cited alongside, same era.
Chatgpt and open-ai models: A preliminary review
Konstantinos I Roumeliotis and Nikolaos D Tselikas. 2023 · 2023
Cited alongside, same era.
Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models
A systematic literature review of pre-requirements specification traceability
Julia Mucha, Andreas Kaufmann, and Dirk Riehle. 2024 · 2024
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PlantUML
PlantUML Team. 2024 · 2024
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Evaluating ChatGPT’s Proficiency in Understanding and Answering Microservice Architecture Queries Using Source Code Insights
Ernesto Quevedo, Amr S Abdelfattah, Alejandro Rodriguez, Jorge Yero, and Tomas Cerny. 2024 · 2024
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Accelerating Software Development Using Generative AI: ChatGPT Case Study. In Proceedings of the 17th Innovations in Software Engineering Conference . 1–11
Asha Rajbhoj, Akanksha Somase, Piyush Kulkarni, and Vinay Kulkarni. 2024 · 2024
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Zeeshan Rasheed, Muhammad Waseem, Aakash Ahmad, Kai-Kristian Kemell, Wang Xiaofeng, Anh Nguyen Duc, and Pekka Abrahamsson. 2024a · 2024
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Archana Tikayat Ray, Bjorn F Cole, Olivia J Pinon Fischer, Anirudh Prabhakara Bhat, Ryan T White, and Dimitri N Mavris. 2023 · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Navigating Complexity in Software Engineering: A Prototype for Comparing GPT-n Solutions
Christoph Treude. 2023 · 2023
Cited alongside, same era.
Using ChatGPT throughout the Software Development Life Cycle by Novice Developers
Muhammad Waseem, Teerath Das, Aakash Ahmad, Mahdi Fehmideh, Peng Liang, and Tommi Mikkonen. 2023a · 2023
Cited alongside, same era.
ChatUniTest: a ChatGPT-based automated unit test generation tool
Zhuokui Xie, Yinghao Chen, Chen Zhi, Shuiguang Deng, and Jianwei Yin. 2023 · 2023
Cited alongside, same era.
Language agent tree search unifies reasoning acting and planning in language models
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. 2023 · 2023
Cited alongside, same era.
A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2024
Cited alongside, same era.
Prompting Is All You Need: Automated Android Bug Replay with Large Language Models. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering . 1–13
Sidong Feng and Chunyang Chen. 2024 · 2024
Cited alongside, same era.
Codepori: Large scale model for autonomous software development by using multi-agents
Zeeshan Rasheed, Muhammad Waseem, Mika Saari, Kari Systä, and Pekka Abrahamsson. 2024b · 2024
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Large Language Model Evaluation Via Multi AI Agents: Preliminary results. In ICLR 2024 Workshop on Large Language Model (LLM) Agents
Zeeshan Rasheed, Muhammad Waseem, Kari Systä, and Pekka Abrahamsson. [n. d.] · 2024
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Software Engineering Research in a World with Generative Artificial Intelligence. In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 3–7
Martin Rinard. 2024 · 2024
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Abdul Malik Sami, Zeeshan Rasheed, Kai-Kristian Kemell, Muhammad Waseem, Terhi Kilamo, Mika Saari, Anh Nguyen Duc, Kari Systä, and Pekka Abrahamsson. 2024a · 2024
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Future of software development with generative AI
Jaakko Sauvola, Sasu Tarkoma, Mika Klemettinen, Jukka Riekki, and David Doermann. 2024 · 2024
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Not all requirements prioritization criteria are equal at all times: A quantitative analysis
Richard Berntsson Svensson and Richard Torkar. 2024 · 2024
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Collaborative agents for software engineering
Daniel Tang, Zhenghan Chen, Kisub Kim, Yewei Song, Haoye Tian, Saad Ezzini, Yongfeng Huang, and Jacques Klein Tegawende F Bissyande. 2024a · 2024
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Chatgpt vs sbst: A comparative assessment of unit test suite generation
Yutian Tang, Zhijie Liu, Zhichao Zhou, and Xiapu Luo. 2024b · 2024
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Software testing with large language models: Survey, landscape, and vision
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2024a · 2024
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Zhitao Wang, Wei Wang, Zirao Li, Long Wang, Can Yi, Xinjie Xu, Luyang Cao, Hanjing Su, Shouzhi Chen, and Jun Zhou. 2024b · 2024
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LLM-based agents for automating the enhancement of user story quality: An early report
Zheying Zhang, Maruf Rayhan, Tomas Herda, Manuel Goisauf, and Pekka Abrahamsson. 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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