Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks

• URL: http://arxiv.org/abs/2508.18905v1
• Date: Tue, 26 Aug 2025 10:22:37 GMT
• Title: Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks
• Authors: Dimitrios Rontogiannis, Maxime Peyrard, Nicolas Baldwin, Martin Josifoski, Robert West, Dimitrios Gunopulos,
• Abstract summary: We build on DevAI, a benchmark of 55 programming tasks, by adding ground-truth solutions and evaluating the relevance and utility of interviewer hints.<n>Our results highlight the importance of dynamic evaluation in advancing the development of collaborative code-generating agents.
• Score: 15.072898489107887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard single-turn, static benchmarks fall short in evaluating the nuanced capabilities of Large Language Models (LLMs) on complex tasks such as software engineering. In this work, we propose a novel interactive evaluation framework that assesses LLMs on multi-requirement programming tasks through structured, feedback-driven dialogue. Each task is modeled as a requirement dependency graph, and an ``interviewer'' LLM, aware of the ground-truth solution, provides minimal, targeted hints to an ``interviewee'' model to help correct errors and fulfill target constraints. This dynamic protocol enables fine-grained diagnostic insights into model behavior, uncovering strengths and systematic weaknesses that static benchmarks fail to measure. We build on DevAI, a benchmark of 55 curated programming tasks, by adding ground-truth solutions and evaluating the relevance and utility of interviewer hints through expert annotation. Our results highlight the importance of dynamic evaluation in advancing the development of collaborative code-generating agents.

Related papers

• LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering [90.84806758077536]
  We introduce textbfLoCoBench-Agent, a comprehensive evaluation framework specifically designed to assess large language models (LLMs) agents in realistic, long-context software engineering.<n>Our framework extends LoCoBench's 8,000 scenarios into interactive agent environments, enabling systematic evaluation of multi-turn conversations.<n>Our framework provides agents with 8 specialized tools (file operations, search, code analysis) and evaluates them across context lengths ranging from 10K to 1M tokens.
  arXiv  Detail & Related papers  (2025-11-17T23:57:24Z)
• WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality [62.43165871914528]
  We introduce WebDevJudge, a systematic benchmark for assessing LLM-as-a-judge performance in web development.<n>WebDevJudge comprises human preference labels over paired web implementations, annotated with structured and query-grounded rubrics.<n>In-depth analysis indicates this gap stems from fundamental model limitations, including failures in recognizing functional equivalence, verifying task feasibility, and mitigating bias.
  arXiv  Detail & Related papers  (2025-10-21T12:16:04Z)
• Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Models [0.8193467416247519]
  We introduce a purpose-oriented evaluation framework that employs nine Large Language Models (LLMs)-based metrics spanning four key dimensions of topic quality.<n>The framework is validated through adversarial and sampling-based protocols, and is applied across datasets spanning news articles, scholarly publications, and social media posts.
  arXiv  Detail & Related papers  (2025-09-08T18:46:08Z)
• Multi-Turn Puzzles: Evaluating Interactive Reasoning and Strategic Dialogue in LLMs [21.192619293355502]
  Large language models (LLMs) excel at solving problems with clear and complete statements, but often struggle with nuanced environments.<n>This benchmark provides valuable insights into the strengths and weaknesses of current LLMs in handling complex, interactive scenarios.
  arXiv  Detail & Related papers  (2025-08-13T19:14:45Z)
• CORE: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks [12.465309397733249]
  Large language models (LLMs) have been widely adopted across diverse software engineering domains.<n>These applications require understanding beyond surface-level code patterns.<n>Existing benchmarks primarily evaluate end-to-end outcomes, such as whether code is correctly repaired or generated.
  arXiv  Detail & Related papers  (2025-07-03T01:35:58Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• SIMCOPILOT: Evaluating Large Language Models for Copilot-Style Code Generation [5.880496520248658]
  SIMCOPILOT is a benchmark that simulates the role of large language models (LLMs) as interactive, "copilot"-style coding assistants.<n>The benchmark comprises dedicated sub-benchmarks for Java (SIMCOPILOTJ) and Python.
  arXiv  Detail & Related papers  (2025-05-21T04:59:44Z)
• MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering [57.156093929365255]
  Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents.<n>MLE-Dojo covers diverse, open-ended MLE tasks carefully curated to reflect realistic engineering scenarios.<n>Its fully executable environment supports comprehensive agent training via both supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-12T17:35:43Z)
• LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments [70.91258869156353]
  We introduce LangSuitE, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuitE offers adaptability to diverse environments without multiple simulation engines. We devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information.
  arXiv  Detail & Related papers  (2024-06-24T03:36:29Z)
• Generative Judge for Evaluating Alignment [84.09815387884753]
  We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
  arXiv  Detail & Related papers  (2023-10-09T07:27:15Z)

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.