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Found 4 papers, 2 papers with code
Resolving Crash Bugs via Large Language Models: An Empirical Study
Evaluating IntDiagSolver on multiple LLMs reveals consistent enhancement in the accuracy of crash bug resolution, including ChatGPT, Claude, and CodeLlama.
CodeGen4Libs: A Two-Stage Approach for Library-Oriented Code Generation
To evaluate the effectiveness of our approach, we conduct extensive experiments on a dataset of 403, 780 data items.
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
Third, we find that generating the entire class all at once (i. e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3. 5, while method-by-method generation (i. e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information.
Learning Diverse Policies in MOBA Games via Macro-Goals
Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings.
