Paired Code Smells and Test Smells: A Fine-Grained Longitudinal Empirical Study

Artifact for Paired Code Smells and Test Smells: A Fine-Grained Longitudinal Empirical Study ├── gitlog_plus_smell.py # Enriches smell data with Git commit logs├── filtering.py # Processing the input scv by filtering for resolved smells├── filter_paired.py # filters out paired smells from code smell and test smell├── single_smell.py # Post-processing: Adds Git stats (LOC, files) to survival data├── rq1_analysis.py # RQ1: Association Rule Mining ├── rq2_analysis.py # RQ2: Survival Analysis (KM Curves, Cox PH, Log-rank)│├── stat/ # Statistical aggregation & Evaluation scripts│ ├── counts_rq1.py # Aggregates RQ1 association rules from multiple files│ ├── counts_rq2.py # Aggregates RQ2 survival statistics│ ├── eval_direct_root_agreement.py # Evaluates agreement on manual vs. AI labeling│ ├── gpt_eval.py # Main script to query OpenAI for causal analysis│ ├── gpt-as-judge.py # Uses LLM to evaluate/judge removal reasons│ ├── run.sh # Shell script for batch execution│ │├── paired_smell_annotation.csv # Annotations for paired smells├── unpaired_smell_annotation.csv # Annotations for unpaired smells└── rules_summary.csv # Summary of rules