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

• URL: http://arxiv.org/abs/2604.24621v1
• Date: Mon, 27 Apr 2026 15:51:22 GMT
• Title: Evaluation of LLM-Based Software Engineering Tools: Practices, Challenges, and Future Directions
• Abstract summary: Large Language Models (LLMs) are increasingly embedded in software engineering (SE) tools.<n>We discuss why reliable evaluation is essential for trust, adoption, and meaningful assessment of LLM-based tools.
• Score: 1.9774267722954466
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Failure Modes in LLM Systems: A System-Level Taxonomy for Reliable AI Applications [0.0]
  Large language models (LLMs) are being rapidly integrated into decision-support tools, automation, and AI-enabled software systems.<n>This paper presents a system-level taxonomy of fifteen hidden failure modes that arise in real-world LLM applications.
  arXiv  Detail & Related papers  (2025-11-25T05:19:23Z)
• The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs [57.1838332916627]
  Large Language Models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP)<n>Their widespread deployment has also raised significant safety concerns.<n>LLMs-generated content can exhibit unsafe behaviors such as toxicity, bias, or misinformation, especially in adversarial contexts.
  arXiv  Detail & Related papers  (2025-06-06T05:50:50Z)
• Computational Reasoning of Large Language Models [51.629694188014064]
  We introduce textbfTuring Machine Bench, a benchmark to assess the ability of Large Language Models (LLMs) to execute reasoning processes.<n> TMBench incorporates four key features: self-contained and knowledge-agnostic reasoning, a minimalistic multi-step structure, controllable difficulty, and a theoretical foundation based on Turing machine.
  arXiv  Detail & Related papers  (2025-04-29T13:52:47Z)
• Tracking the Moving Target: A Framework for Continuous Evaluation of LLM Test Generation in Industry [0.5735035463793009]
  Large Language Models (LLMs) have shown great potential in automating software testing tasks, including test generation.<n>Their rapid evolution poses a critical challenge for companies implementing DevSecOps.<n>This work presents a measurement framework for the continuous evaluation of commercial LLM test generators in industrial environments.
  arXiv  Detail & Related papers  (2025-04-26T18:08:13Z)
• From Code to Courtroom: LLMs as the New Software Judges [29.77858458399232]
  Large Language Models (LLMs) have been increasingly used to automate software engineering tasks such as code generation and summarization.<n>Human evaluation, while effective, is very costly and time-consuming.<n>The LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged.
  arXiv  Detail & Related papers  (2025-03-04T03:48:23Z)
• The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance? [1.3810901729134184]
  Large Language Models (LLMs) excel at standardized tests while failing to demonstrate genuine language understanding and adaptability.<n>Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum.<n>We lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks.
  arXiv  Detail & Related papers  (2024-12-02T20:49:21Z)
• Auto-PRE: An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation [52.76508734756661]
  Auto-PRE is an automatic evaluation framework inspired by the peer review process.<n>Unlike previous approaches that rely on human annotations, Auto-PRE automatically selects evaluators based on three core traits.<n> Experiments on three representative tasks, including summarization, non-factoid QA, and dialogue generation, demonstrate that Auto-PRE achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-10-16T06:06:06Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Post Turing: Mapping the landscape of LLM Evaluation [22.517544562890663]
  This paper traces the historical trajectory of Large Language Models (LLMs) evaluations, from the foundational questions posed by Alan Turing to the modern era of AI research. We emphasize the pressing need for a unified evaluation system, given the broader societal implications of these models. This work serves as a call for the AI community to collaboratively address the challenges of LLM evaluation, ensuring their reliability, fairness, and societal benefit.
  arXiv  Detail & Related papers  (2023-11-03T17:24:50Z)
• Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks [65.69651759036535]
  We analyze whether large language models (LLMs) can serve as reliable alternatives to humans.<n>This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning)<n>We find that LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts.
  arXiv  Detail & Related papers  (2023-10-30T17:04:35Z)
• A Survey on Evaluation of Large Language Models [87.60417393701331]
  Large language models (LLMs) are gaining increasing popularity in both academia and industry. This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
  arXiv  Detail & Related papers  (2023-07-06T16:28:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.