Large Language Models for Software Engineering: Survey and Open Problems
By Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, Jie M. Zhang. 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE), 2023
496 citations recorded by the source corpus.
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.
Subfields: Information Systems, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/4fbd174e80502f8f3c4b9f48054872b028e2445a
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In Information Systems
Citations in the corpus
- A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, unknown): “In addition, cognitive psychology has been studied for a long time, so many effective human memory theories and architectures have been accumulated, which can support more advanced capabilities of the agents [147].”
- BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions (2024, background): “Automated software engineering, like code generation and repair [19, 24, 72], has attracted significant interest from academia and industry to facilitate the development of advanced models.”
- Software Testing With Large Language Models: Survey, Landscape, and Vision (2023, unknown): “Fan et al. conducted a survey of LLMs for software engineering, and set out open research challenges for the application of LLMs to technical problems faced by software engineers [165].”