Human-In-The-Loop Software Development Agents: Challenges and Future Directions

• URL: http://arxiv.org/abs/2506.11009v1
• Date: Fri, 25 Apr 2025 01:52:59 GMT
• Title: Human-In-The-Loop Software Development Agents: Challenges and Future Directions
• Authors: Jirat Pasuksmit, Wannita Takerngsaksiri, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Ruixiong Zhang, Shiyan Wang, Fan Jiang, Jing Li, Evan Cook, Kun Chen, Ming Wu,
• Abstract summary: At Atlassian, we deployed Human-in-the-Loop Software Development Agents to resolve Jira work items and evaluated the generated code quality using functional correctness testing and GPT-based similarity scoring.<n>This paper highlights two major challenges: the high computational costs of unit testing and the variability in LLM-based evaluations.
• Score: 14.81934634773595
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-agent LLM-driven systems for software development are rapidly gaining traction, offering new opportunities to enhance productivity. At Atlassian, we deployed Human-in-the-Loop Software Development Agents to resolve Jira work items and evaluated the generated code quality using functional correctness testing and GPT-based similarity scoring. This paper highlights two major challenges: the high computational costs of unit testing and the variability in LLM-based evaluations. We also propose future research directions to improve evaluation frameworks for Human-In-The-Loop software development tools.

Related papers

• MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks [56.34018316319873]
  We propose MERA Code, a benchmark for evaluating code for the latest code generation LLMs in Russian.<n>This benchmark includes 11 evaluation tasks that span 8 programming languages.<n>We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages.
  arXiv  Detail & Related papers  (2025-07-16T14:31:33Z)
• MLGym: A New Framework and Benchmark for Advancing AI Research Agents [51.9387884953294]
  We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing large language models on AI research tasks.<n>This is the first Gym environment for machine learning (ML) tasks, enabling research on reinforcement learning (RL) algorithms for training such agents.<n>We evaluate a number of frontier large language models (LLMs) on our benchmarks such as Claude-3.5-Sonnet, Llama-3.1 405B, GPT-4o, o1-preview, and Gemini-1.5 Pro.
  arXiv  Detail & Related papers  (2025-02-20T12:28:23Z)
• Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design [0.0]
  This research aims to develop reliable, LM-powered methods for code optimization that effectively integrate human feedback.<n>This work aligns with the broader objectives of advancing cooperative and human-centric aspects of software engineering.
  arXiv  Detail & Related papers  (2025-02-10T18:48:45Z)
• Human-In-the-Loop Software Development Agents [12.830816751625829]
  Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks.<n>In this paper, we introduce a Human-in-the-loop LLM-based Agents framework (HULA) for software development.<n>We design, implement, and deploy the HULA framework into Atlassian for internal uses.
  arXiv  Detail & Related papers  (2024-11-19T23:22:33Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents [50.82665351100067]
  FlowGen is a code generation framework that emulates software process models based on multiple Large Language Model (LLM) agents. We evaluate FlowGenScrum on four benchmarks: HumanEval, HumanEval-ET, MBPP, and MBPP-ET.
  arXiv  Detail & Related papers  (2024-03-23T14:04:48Z)
• Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study [72.24266814625685]
  We explore the performance of large language models (LLMs) across the entire software development lifecycle with DevEval.<n>DevEval features four programming languages, multiple domains, high-quality data collection, and carefully designed and verified metrics for each task.<n> Empirical studies show that current LLMs, including GPT-4, fail to solve the challenges presented within DevEval.
  arXiv  Detail & Related papers  (2024-03-13T15:13:44Z)
• Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming [12.355284125578342]
  Large Language Models (LLMs) have become a focal point in modern software development. LLMs offer the potential to significantly augment developer productivity by serving as intelligent, chat-driven programming assistants. However, each system requires the LLM to be honed to its set of workspaces to ensure the best performance.
  arXiv  Detail & Related papers  (2024-02-22T03:51:34Z)
• CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology [4.2990995991059275]
  Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) have transformed the field of Software Engineering. We introduce CodePori, a novel system designed to automate code generation for large and complex software projects. Results: CodePori is able to generate running code for large-scale projects, aligned with the typical software development process.
  arXiv  Detail & Related papers  (2024-02-02T13:42:50Z)
• Experiential Co-Learning of Software-Developing Agents [83.34027623428096]
  Large language models (LLMs) have brought significant changes to various domains, especially in software development. We introduce Experiential Co-Learning, a novel LLM-agent learning framework. Experiments demonstrate that the framework enables agents to tackle unseen software-developing tasks more effectively.
  arXiv  Detail & Related papers  (2023-12-28T13:50:42Z)
• AgentBench: Evaluating LLMs as Agents [88.45506148281379]
  Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
  arXiv  Detail & Related papers  (2023-08-07T16:08:11Z)

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.