- author = "Angela Fan and Beliz Gokkaya and Mark Harman and Mitya Lyubarskiy and Shubho Sengupta and Shin Yoo and Jie M. Zhang",
- title = "Large Language Models for Software Engineering: Survey and Open Problems",
- booktitle = "2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE)",
- year = "2023",
- editor = "Hoa Khanh Dam and Xing Hu",
- pages = "31--53",
- address = "Melbourne, Australia",
- month = "17 " # may,
- keywords = "genetic algorithms, genetic programming, genetic improvement, ANN, AI, Automated Program Repair, APR, Documentation generation, Generative AI, Human-Computer Interaction, HCI, Large Language Models, LLM, Refactoring, Requirements engineering, Search Based Software Engineering, SBSE, Software Analytics, Software Engineering Education, Software Processes, Software Maintenance and Evolution, Software Testing",
- isbn13 = "979-8-3503-2497-6",
-
URL = "
https://arxiv.org/pdf/2310.03533",
-
DOI = "
doi:10.1109/ICSE-FoSE59343.2023.00008",
- size = "23 pages",
- abstract = "This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering activities including coding, design, requirements, repair, refactoring, performance improvement, documentation and analytics. However, these very same emergent properties also pose significant technical challenges; we need techniques that can reliably weed out incorrect solutions, such as hallucinations. Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE",
-
notes = "FoSE held in conjunction with ICSE
2023
https://conf.researchr.org/track/icse-2023/fose?#program",
