
MultiConf is an innovative technique proposed in the paper "Isolating Compiler Faults via Multiple Pairs of Adversarial Compilation Configurations" for effective compiler fault localization. This implementation evaluates MultiConf's performance using GCC. Prerequisites - Linux workstation - Python 3.x - GCC build dependencies Getting Started 1. Setting Up Bug Information Add compiler bugs to the respective summary files using this format: bugId,trunk_revision,non_triggering_optimization,triggering_optimization,faulty_file Example (from gccbugs_summary.txt): `56478,r196310,-O1+-c,-O2+-c,gcc/predict.c` Note: Comprehensive bug information is available in the benchmark directory. 2. Running MultiConf Analysis Execute the script: python3 gcc-run.py The scripts automatically process all bugs listed in the corresponding summary files. 3. Verifying Results Verify the experimental results in the paper with: python3 verify-results.py Metrics Reported: - Top-N (Top 1, 5, 10, 20) - MFR (Mean First Rank) - MAR (Mean Average Rank)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
