Published on 01 January 2024
Improving LLM Code Generation via Testing and Static Analysis Feedback
View DatasetArceri, Vincenzo
Description
- assertion: Scripts for the (in)correctness analysis + Results for first generation and repair phase experiments- compilation: Scripts to do code refactoring on the generated files and get the compiling files- correctness_stats: Aggregate stats for the (in)correctness analysis- dataset: Contains the dataset used for the experiments (100_clean_tasks.json) and other additional files- files_to_analyze_strict: Files to analyze in the phases after the generation. These are the 89 files that compile for all the models- first_gen_output_prompt*: Generated output for the first generation of the prompt experiments- generation: Script for interacting and prompting the models to obtain the output for each phase- infer: Vulnerability report created by Infer for the first generation and the vulnerability repair phase + scripts for running Infer- infer_stats: Vulnerability stats for the first generation and the repair phase - including the repair prompt experiments- iterations-correctness: Generated output for the correctness repair experiments at each iteration- iterations-vulnerabilities: Generated output for the vulnerability repair experiments at each iteration- prompt_experiments: Contains prompts and some results for the prompt experiments that we ran- regeneration_output_correctness_prompt*: Generated output for the correctness repair experiments- regeneration_output_vulnerability_prompt*: Generated output for the vulnerability repair experiments- self_correctness_output_prompt*: Generated output for the self-correctness experiments- self_safety_output_prompt*: Generated output for the self-correctness experiments- self_correctness_stats: Generated stats for the self-correctness experiments- self_safety_stats: Generated stats for the self-safety experiments- stats: Python script for obtaining different stats- folder_descr.txt: this file. description of the folders in this directory- README.md: pipeline description with some results reported in the paper
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Metrics Over Time
Publication Details
Subfield
Software
Field
Computer Science
Domain
Physical Sciences
Confidence Score
52%
Source
Scholar Data Model
Keywords
Software quality, processes and metrics