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metadata 
language:
  - en
pretty_name: BugWitGym
task_categories:
  - text-generation
tags:
  - code
  - java
  - software-engineering
  - coding-agents
  - bug-validation
  - executable-witness
  - test-generation
  - bug-injection
  - benchmark
  - llm-evaluation
size_categories:
  - 1K<n<10K

BugWitGym: Evaluating Coding Agents for Executable Bug Validation

Anonymous dataset release accompanying the manuscript under double-blind review.

Overview

BugWitGym evaluates coding agents on executable bug validation. Given a buggy repository, a bug-type description, and a reported bug location, an agent must construct an executable witness: a concrete input together with a testing harness that exposes the reported bug through observable faulty behavior.

The task is to validate an already-reported bug through execution, not to discover an unspecified bug or repair production code.

This repository contains the full benchmark release, a representative inspection sample, case-level metadata, Croissant metadata, and integrity checksums.

Benchmark Summary

Property Description
Full benchmark 1,300 execution-validated cases
Inspection sample 51 representative cases
Source projects 6 real-world Java projects
Build ecosystem Maven
Bug types 10
Bug categories API Contract, Value Flow, and Logic
Execution-context buckets Short: 1–40; mid-short: 41–70; mid-long: 71–120; long: 121–150 distinct production-side functions
Transformation depths 1, 3, 5, and 10 applied transformations
Evaluation settings NoEC and WithEC

The inspection sample is provided for reviewing the release structure and representative cases. It is not an additional set of cases beyond the full benchmark.

Benchmark Construction

Cases are automatically constructed by injecting bugs into production-code paths exercised by existing tests. Each accepted case must contain a meaningful production-code change consistent with the selected bug type. The associated construction-time witness must pass on the clean baseline and expose the injected bug in the modified repository.

Bug-preserving transformations subsequently vary the surrounding call-stack, data-flow, and control-flow structure. A transformation is retained only when the project remains compilable and the same construction-time witness continues to expose the injected bug.

The resulting cases contain newly injected bugs embedded in real project contexts rather than a collection of previously disclosed historical bugs.

Task and Evaluation Protocol

BugWitGym supports two evaluation settings:

Setting Agent-visible task information
NoEC Buggy repository, bug-type description, and reported bug location, without an execution-context summary
WithEC The same information, supplemented with the execution-context summary collected during construction

Under the evaluation protocol, the agent creates or modifies Java test files under src/test/java to construct its own executable witness. Production code, build configuration, dependencies, and unrelated files must remain unchanged.

A successful attempt requires a materialized Java test that compiles and exposes the intended injected bug under the recorded Maven replay command. Compilation errors, missing dependencies, infrastructure failures, and unrelated test failures do not count as successful bug validation.

Release inspection and agent evaluation must be kept separate. Before evaluation, the construction-time witness must be removed or rewritten in the agent workspace. Construction-time witness implementations, hidden validation oracles, and construction-time solution artifacts must not be exposed to the evaluated agent.

Repository Contents

File Contents
pocgym_benchmark_release_v1.tar.gz Full 1,300-case benchmark release
pocgym_benchmark_sample_v1.tar.gz Representative 51-case inspection sample
pocgym_croissant.json Machine-readable Croissant dataset metadata
CHECKSUMS.sha256 SHA-256 checksums for uploaded artifacts
MANIFEST.jsonl One machine-readable row per full-release case
DATASET_SCHEMA.md Field definitions and metadata documentation
LICENSES.md Dataset and upstream-source licensing notes
SAMPLE_README.md Inspection-sample construction notes
SAMPLE_SELECTION.json Selected sample case IDs and coverage reasons

The existing pocgym_ artifact filenames are retained for compatibility with the release layout. The benchmark described by this dataset card is BugWitGym.

Per-Case Contents

Path Contents
case.json Benchmark case metadata
bug.patch Ground-truth patch documenting the injected bug
buggy_repo/ Cleaned full buggy repository workspace

Case identifiers are globally sequential, beginning with bj_case_000001. This naming scheme supports appending additional cases without changing existing case identifiers or the directory layout.

Refer to DATASET_SCHEMA.md for the exact metadata fields and their interpretation.

Inspecting the Release

Start with SAMPLE_README.md, SAMPLE_SELECTION.json, and the 51-case inspection archive to review representative cases. Use MANIFEST.jsonl and DATASET_SCHEMA.md to inspect the full-release index and metadata structure.

Verify downloaded artifacts against CHECKSUMS.sha256 before extracting the archives.

The archives contain repository-level benchmark artifacts, not only prompt–completion records. Reproducing execution-based results requires a compatible Java runtime, Maven or Maven-wrapper support, and the case-specific replay environment described in the manuscript and case metadata.

Scope and Limitations

The current release covers ten observable bug types in Maven-based Java projects with replayable tests. Its coverage should not be interpreted as representative of all programming languages, software ecosystems, or bug classes.

The benchmark is intended for evaluating executable witness construction. The representative sample is intended for inspection rather than as a substitute for the full benchmark or the evaluation subsets described in the manuscript.

Injected buggy repositories are benchmark artifacts and are not intended for production deployment.

Licensing

See LICENSES.md for dataset and upstream-source licensing information. Consult the applicable license and notice files included with the bundled repositories before reuse.

Anonymous Review

Author names, affiliations, and identifying project links are omitted from this dataset card for double-blind review.