Evaluation of LLM-Based Software Engineering Tools: Practices, Challenges, and Future Directions

2026-04-27

AI Summary

This study addresses the challenges in evaluating large language model (LLM)-based software engineering tools, which stem from the open-ended, multi-solution, and non-deterministic nature of their outputs—properties incompatible with traditional evaluation paradigms reliant on single ground truths and determinism. The work presents the first systematic analysis of LLM evaluation difficulties from a software engineering task perspective, proposing an integrated framework that accounts for subjectivity, multi-dimensional quality, non-determinism, and fragmented evaluation practices. Through qualitative review, case studies, and modeling of evaluation dimensions, it elucidates the limitations and applicability boundaries of automated metrics, self-evaluation by models, and human assessment. The study identifies critical bottlenecks and advocates for the development of robust, scalable, and trustworthy evaluation methodologies, laying a theoretical foundation for standardized practices in the community.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) are increasingly embedded in software engineering (SE) tools, powering applications such as code generation, automated code review, and bug triage. As these LLM-based AI for Software Engineering (AI4SE) systems transition from experimental prototypes to widely deployed tools, the question of what it means to evaluate their behavior reliably has become both critical and unanswered. Unlike traditional SE or machine learning systems, LLM-based tools often produce open-ended, natural language outputs, admit multiple valid answers, and exhibit non-deterministic behavior across runs. These characteristics fundamentally challenge long-standing evaluation assumptions such as the existence of a single ground truth, deterministic outputs, and objective correctness. In this paper, we examine LLM evaluation as a general, task-dependent concept through the lens of SE tasks. We discuss why reliable evaluation is essential for trust, adoption, and meaningful assessment of LLM-based tools, summarize the current state of evaluation practices, and highlight their limitations in realistic AI4SE settings. We then identify key challenges facing current approaches, including the absence of stable ground truth, subjectivity and multi-dimensional quality, evaluation instability due to non-determinism, limitations of automated and model-based evaluation, and fragmentation of evaluation practices. Finally, we outline future directions aimed at advancing LLM evaluation toward more robust, scalable, and trustworthy methodologies, to stimulate discussion on principled evaluation practices that can keep pace with the growing role of LLMs in SE.

Problem

• LLM evaluation
• software engineering
• ground truth
• non-determinism
• AI4SE

Innovation

• LLM evaluation
• AI for Software Engineering
• non-determinism
• ground truth
• evaluation methodology