Artifact for ASE 2026 Submission#1047

1. The Benchmark Dataset Dataset- Performance Faults.csv: The core runtime-validated benchmark containing 2,479 bug-fix pairs extracted from six major Java repositories (HBase, Commons Pool, Spring, Guava, Glide, and Elasticsearch).2. Dataset Curation Pipeline (Stages 1–4): 01_mine_repositories.py and 01_mine_commits.py: Implements repository mining to identify candidate performance-relevant commits.02_extract_methods.py: A custom git-diff parser used to isolate before-fix and after-fix method versions.02_generate_jmh_harnesses.py: Automates the construction of JMH microbenchmark harnesses to execute code under controlled conditions.03_dynamic_validation.py: Executes the statistical validation protocol (Wilcoxon signed-rank test, $p < 0.05$) to confirm measurable runtime degradation.3. The FAULTEMBED Framework: 04_generate_embeddings.py: Generates structural vector representations using a frozen GraphCodeBERT backbone to capture data-flow graphs (DFGs).05_faultembed_classifier.py: The primary implementation of the retrieval-augmented classification pipeline (Algorithm 1), integrating FAISS similarity search and Random Forest classification.faultembed_ci.py: A lightweight implementation designed for deterministic screening within performance-aware CI/CD pipelines.4. Evaluation & Baselines 06_baselines.py: Scripts to execute and compare against the 17 baselines evaluated in the study, including rule-based tools (SonarQube, Infer) and LLM prompting paradigms.Technical Requirements: Python 3, Dependencies: transformers (for GraphCodeBERT), faiss-cpu, scikit-learn, and pandas.Java Development Kit (JDK): Required for running the JMH harnesses during the validation stage.