Agent-Based Software Artifact Evaluation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914304375128064 |
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| author | Wu, Zhaonan Zhao, Yanjie Chen, Zhenpeng Wang, Zheng Wang, Haoyu |
| author_facet | Wu, Zhaonan Zhao, Yanjie Chen, Zhenpeng Wang, Zheng Wang, Haoyu |
| contents | Artifact evaluation has been adopted in the Software Engineering (SE) research community for 15 years, substantially improving research reproducibility across major SE conferences. However, this success has introduced a growing scalability challenge, as artifact evaluation relies heavily on reviewers' manual execution and debugging, leading to escalating human effort amid rapidly increasing paper submissions. To address this problem, we investigate automated artifact evaluation. We first conduct a preliminary study on artifacts from top-tier SE conferences and identify three key challenges: perceiving execution states, maintaining stable execution environments, and recovering from execution errors. Inspired by these findings, we propose ArtifactCopilot, the first end-to-end agent-based framework for automated artifact evaluation. ArtifactCopilot automates environment construction, instruction execution, and error recovery by combining an execution normalization strategy to ensure environment stability with an artifact evaluation graph that transforms README documents into dependency-aware command graphs, enabling structured execution planning, execution-state tracking, and error recovery. Evaluation on 48 real-world artifacts shows that ArtifactCopilot matches human artifact evaluation outcomes for 85.42% of the artifacts, outperforming Claude Code by 52.09 percentage points, while costing only \$0.091 per artifact on average and requiring zero human intervention for 45 out of 48 artifacts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02235 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agent-Based Software Artifact Evaluation Wu, Zhaonan Zhao, Yanjie Chen, Zhenpeng Wang, Zheng Wang, Haoyu Software Engineering Artifact evaluation has been adopted in the Software Engineering (SE) research community for 15 years, substantially improving research reproducibility across major SE conferences. However, this success has introduced a growing scalability challenge, as artifact evaluation relies heavily on reviewers' manual execution and debugging, leading to escalating human effort amid rapidly increasing paper submissions. To address this problem, we investigate automated artifact evaluation. We first conduct a preliminary study on artifacts from top-tier SE conferences and identify three key challenges: perceiving execution states, maintaining stable execution environments, and recovering from execution errors. Inspired by these findings, we propose ArtifactCopilot, the first end-to-end agent-based framework for automated artifact evaluation. ArtifactCopilot automates environment construction, instruction execution, and error recovery by combining an execution normalization strategy to ensure environment stability with an artifact evaluation graph that transforms README documents into dependency-aware command graphs, enabling structured execution planning, execution-state tracking, and error recovery. Evaluation on 48 real-world artifacts shows that ArtifactCopilot matches human artifact evaluation outcomes for 85.42% of the artifacts, outperforming Claude Code by 52.09 percentage points, while costing only \$0.091 per artifact on average and requiring zero human intervention for 45 out of 48 artifacts. |
| title | Agent-Based Software Artifact Evaluation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2602.02235 |