From the 1 of 6 linked papers with an AI index.
6 papers
Multi-Source and Cross-Scenario Strategy-Guided Code Optimization
Yuwei Zhao, Qianyu Xiao, Ye Cui +2
Automated code optimization improves program performance by refactoring source code, and recent studies use LLMs to generate optimization patches. The newest approaches are strateg…
SpecRL: Reinforcement Learning with Test-Based Completeness Rewards for Formal Specification Synthesis
Zhechong Huang, Zhao Zhang, Zeyu Sun +2
SpecRL is a reinforcement learning system that improves automatic generation of program specifications by rewarding candidates that reject impossible behaviors identified through g…
SemOpt: LLM-Driven Code Optimization via Rule-Based Analysis
Yuwei Zhao, Yuan-An Xiao, Qianyu Xiao +2
Automated code optimization improves program performance through refactoring, and recent studies leverage LLMs for this purpose. Existing approaches mine optimization commits from…
Learning to Guarantee Type Correctness in Code Generation through Type-Guided Program Synthesis
Zhechong Huang, Zhao Zhang, Ruyi Ji +6
Language models have shown remarkable proficiency in code generation; nevertheless, ensuring type correctness remains a challenge. Although traditional methods, such as constrained…
Condor: A Code Discriminator Integrating General Semantics with Code Details
Qingyuan Liang, Zhao Zhang, Chen Liu +9
LLMs demonstrate significant potential across various software engineering tasks. However, they still face challenges in generating correct code on the first attempt when addressin…
GramTrans: A Better Code Representation Approach in Code Generation
Zhao Zhang, Qingyuan Liang, Zeyu Sun +6
Code generation has shown great promise in assisting software development. A fundamental yet underexplored question is how the choice of code representation affects model performan…