Reproducible, Explainable, and Effective Evaluations of Agentic AI for Software Engineering

• URL: http://arxiv.org/abs/2604.01437v1
• Date: Wed, 01 Apr 2026 22:24:08 GMT
• Title: Reproducible, Explainable, and Effective Evaluations of Agentic AI for Software Engineering
• Abstract summary: Large language models (LLMs) that underpin Agentic AI often function as black boxes, making it difficult to justify the superiority of Agentic AI approaches over baselines.<n>This study analyzes 18 papers on the topic, published or accepted by ICSE 2026, ICSE 2025, FSE 2025, and ISSTA 2025.<n>The analysis identifies prevailing approaches and their limitations in evaluating Agentic AI for SE, both in current research and potential future studies.
• Score: 4.411658619208916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advancement of Agentic AI, researchers are increasingly leveraging autonomous agents to address challenges in software engineering (SE). However, the large language models (LLMs) that underpin these agents often function as black boxes, making it difficult to justify the superiority of Agentic AI approaches over baselines. Furthermore, missing information in the evaluation design description frequently renders the reproduction of results infeasible. To synthesize current evaluation practices for Agentic AI in SE, this study analyzes 18 papers on the topic, published or accepted by ICSE 2026, ICSE 2025, FSE 2025, ASE 2025, and ISSTA 2025. The analysis identifies prevailing approaches and their limitations in evaluating Agentic AI for SE, both in current research and potential future studies. To address these shortcomings, this position paper proposes a set of guidelines and recommendations designed to empower reproducible, explainable, and effective evaluations of Agentic AI in software engineering. In particular, we recommend that Agentic AI researchers make their Thought-Action-Result (TAR) trajectories and LLM interaction data, or summarized versions of these artifacts, publicly accessible. Doing so will enable subsequent studies to more effectively analyze the strengths and weaknesses of different Agentic AI approaches. To demonstrate the feasibility of such comparisons, we present a proof-of-concept case study that illustrates how TAR trajectories can support systematic analysis across approaches.

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