一句话：通过访谈与问卷调研从业者，揭示现有工具难以支持神经网络异常行为的理解与根因定位，指出维护支持的改进机会。
随着神经网络日益融入日常生活，通过有效的测试、调试与维护来保障其质量变得至关重要。传统软件工程方法虽在维护软件质量上行之有效，但在神经网络维护上存在明显的研究与实践空白，尤其缺乏对从业者如何理解与缓解模型不良行为的了解。本项进行中的研究通过访谈与配套问卷的初步调研，收集从业者的实践经验，考察神经网络维护的研究与实践现状。初步发现表明，现有工具主要聚焦于模型构建与训练，虽有一定帮助，却难以支持从业者理解模型行为并定位意外行为的根本原因。通过评估当前流程并识别传统方法的局限，该研究从以开发者为中心的视角指出实践短板，并强调改进神经网络维护支持的机会。
As the potential for neural networks to augment our daily lives grows, ensuring their quality through effective testing, debugging, and maintenance is essential. This is especially the case as we acknowledge the prospects of negative impacts from these technologies. Traditional software engineering methods, such as testing and debugging, have proven effective in maintaining software quality; however, they reveal significant research and practice gaps in maintaining neural networks. In particular, there is a limited understanding of how practitioners currently address challenges related to understanding and mitigating undesirable behaviors in neural networks. In our ongoing research, we explore the current state of research and practice in maintaining neural networks by curating insights from practitioners through a preliminary study involving interviews and supporting survey responses. Our findings thus far indicate that existing tools primarily concentrate on building and training models. While these tools can be beneficial, they often fall short of supporting practitioners' understanding and addressing the underlying causes of unexpected model behavior. By evaluating current procedures and identifying the limitations of traditional methodologies, our study aims to offer a developer-centric perspective on where current practices fall short and highlight opportunities for improving maintenance support in neural networks.