pith. sign in

arxiv: 2408.14354 · v1 · pith:2NEVFU4Pnew · submitted 2024-08-26 · 💻 cs.SE · cs.AI· cs.CL

SWE-bench-java: A GitHub Issue Resolving Benchmark for Java

classification 💻 cs.SE cs.AIcs.CL
keywords issueresolvingswe-bench-javabenchmarkgithubindustryjavallms
0
0 comments X
read the original abstract

GitHub issue resolving is a critical task in software engineering, recently gaining significant attention in both industry and academia. Within this task, SWE-bench has been released to evaluate issue resolving capabilities of large language models (LLMs), but has so far only focused on Python version. However, supporting more programming languages is also important, as there is a strong demand in industry. As a first step toward multilingual support, we have developed a Java version of SWE-bench, called SWE-bench-java. We have publicly released the dataset, along with the corresponding Docker-based evaluation environment and leaderboard, which will be continuously maintained and updated in the coming months. To verify the reliability of SWE-bench-java, we implement a classic method SWE-agent and test several powerful LLMs on it. As is well known, developing a high-quality multi-lingual benchmark is time-consuming and labor-intensive, so we welcome contributions through pull requests or collaboration to accelerate its iteration and refinement, paving the way for fully automated programming.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs

    cs.AI 2026-06 unverdicted novelty 7.0

    Multilingual execution-grounded benchmark finds top open code LLM at 23.64% correctness versus 57.2% human baseline, with compile errors dominating 63% of failures.

  2. Bash-Commenter: Leveraging Syntax-Aware Preference Optimization to Reinforce Large Language Model for Bash Code Comment Generation

    cs.SE 2026-06 unverdicted novelty 5.0

    Bash-Commenter applies CPT, SFT, and Syntax-Aware Preference Optimization (SAPO) via AST atomic operations to LLaMA-3.1-8B, reporting higher BLEU-4/METEOR/ROUGE-L scores than baselines on single-line and multi-line Ba...

  3. How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring

    cs.SE 2026-06 conditional novelty 5.0

    Lightweight call and inheritance topology injected as comments improves function localization by 2.2pp, shortens trajectories, and halves run-to-run variance in LLM code agents, with benefits depending on repository scale.

  4. How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring

    cs.SE 2026-06 unverdicted novelty 5.0

    An empirical study finds that injecting call/inheritance topology as comments improves LLM code agent localization by 2.2pp, shortens trajectories by 1.6 rounds, and halves run-to-run variance on medium repositories v...

  5. PR-Aware Automated Unit Test Generation: Challenges and Opportunities

    cs.SE 2026-05 unverdicted novelty 5.0

    EvoSuite produced at least one fail-to-pass test for 36% of PRs versus 13% for GPT-4o, but both tools generated no meaningful change-capturing tests for 64% of the PRs evaluated.

  6. Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs

    cs.SE 2026-04 unverdicted novelty 5.0

    STITCH trains superior agentic coding and reasoning LLMs by using fewer high-quality trajectories filtered to keep only critical decision tokens, delivering up to 63% relative gains on SWE-bench Verified.

  7. Large Language Model-Based Agents for Software Engineering: A Survey

    cs.SE 2024-09 unverdicted novelty 4.0

    A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.