Artifact for "Fast First, Flawless Later: Re-evaluating Automated Program Repair for Hot Fixing Time-Critical Bugs"

This repository contains the complete results and artifacts associated with the paper “Can Automated Program Repair Tooling Support Hot Fix? An Empirical Study”.The goal of the study is to evaluate the ability of automated program repair (APR) tools to support time-critical hot fix development, with a focus on effectiveness, efficiency, and patch quality. The results include all run data and per-bug outputs generated by all evaluated tools across the HotBugs.jar benchmark. The dataset includes outcomes for the following APR tools: Search-based: ARJA Template-based: Cardumen Learning-based: RepairLLaMA Agentic / Context-aware: AutoCodeRover (multiple model configurations) Each tool was executed against the HotBugs.jar dataset according to the experimental protocol described in the associated paper. File Naming Convention Arja, Cardumen, and RepairLlama tool results follow a consistent naming scheme: results_<tool>_<run>.zipRaw tool execution outputs, including logs, generated patches, intermediate build artifacts, and tool-specific diagnostic information.These files are large due to extensive patch generation and are primarily intended for inspection, auditing, or re-analysis. output_<tool>_<run>.zipAggregated, structured results for a specific tool and run.These archives contain files summarizing run status, patch counts, runtime measurements, and classification outcomes used for quantitative analysis. summaries_<tool>_<run>.zipLightweight summaries extracted from outputs.These include per-bug outcomes, success indicators, and metadata used to generate plots and tables in the paper. AutoCodeRover results with both the Llama3 and Llama3:70B settings are stored in autocoderover_results.zip.