CloneForge: Type-IV Clone Generation Pipeline (implementation and dataset)

CloneForge: Type-IV Clone Generation Pipeline A hybrid pipeline for generating Type-IV (semantic) code clones using Large Language Models (LLMs) and deterministic validation. The README.md inside CloneForge.zip contains all the details for each step, including examples, configuration, and workflow. Overview CloneForge is a fully automated pipeline designed to generate behaviorally equivalent but syntactically diverse implementations of programs (Type-IV clones). Unlike naive LLM-based generation, CloneForge enforces: Semantic correctness via test execution Syntactic diversity via CodeBLEU filtering Non-redundancy via clustering This makes it suitable for: Training ML-based clone detectors Benchmark creation Program transformation studies Pipeline Architecture The pipeline consists of six main stages: Normalization → Generation → Syntactic Filtering → Testing → Repairing → Clustering Each stage is modular and can be executed independently. Detailed Pipeline The pipeline is organized into six steps. Each step is documented in detail in the README.md file: Step 1: NormalizationStandardizes input data into a unified format, extracting code, tests, metadata, and generating AST representations. Step 2: Clone GenerationGenerates diverse candidate implementations using configurable LLM prompting strategies and refactorings. Step 3: Syntactic FilteringRemoves clones that are too similar to the original using a CodeBLEU-based similarity threshold. Step 4: Semantic TestingExecutes unit tests to ensure behavioral equivalence between generated clones and the original implementation. Step 5: Repairing Clone CandidatesAttempts to fix partially correct clones using LLM-based re-prompting and test feedback. Step 6: Representative SelectionApplies clustering to select a diverse, non-redundant subset of valid clones. Usage Provide an input dataset with at least a set of test cases. Specify at least one LLM to be prompted. Set syntactic and semantic thresholds. Choose prompt configurations. The pipeline is implemented in Python and is fully automated.