发表机构
Ontario Tech University; Universidade Federal de Minas Gerais (UFMG)（安大略理工大学; 米纳斯吉拉斯联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过工业案例评估Claude Code智能体迁移企业ERP系统功能的效果，发现其平均等价率70%，低级功能表现优于高级功能，同时探讨了该方法的适用场景及局限。
AI 中文摘要
构建于已停产平台之上的遗留系统，是依赖这些应用维持关键业务流程的组织中反复出现的技术负担。尽管现代化改造具有战略必要性，但仅通过人工执行既成本高昂又易出错。本文报告一项在真实工业环境中开展的案例研究，评估AI编码智能体在支持遗留系统向现代平台迁移方面的有效性与效率。我们使用一个版本的Claude Code智能体，在单代迁移策略下，将某企业ERP系统中12个不同复杂度级别的功能从Visual Basic 6迁移至C#.NET 10。迁移会话完成后，我们测量了迁移功能与原始功能的等价性，并收集了过程中智能体的耗时及消耗的token数量数据。实验中，该智能体的平均等价率为70%，且在不同复杂度级别间存在显著不对称性：低级功能达到92%，高复杂度功能仅为47%。在基于成本的指标中也观察到类似不对称性：低级功能消耗147万token（成本1.66美元），而高级功能消耗909万token（成本10.28美元）。我们揭示了该迁移策略更有效的实际场景与情况，并讨论了使用AI智能体进行遗留系统现代化的局限性与挑战。
英文摘要
Legacy systems built on discontinued platforms are a recurring technological liability in organizations that depend on these applications to sustain critical business processes. Although modernization is strategically necessary, it is costly and error-prone when performed exclusively through manual effort. In this paper, we report on a case study conducted in a real industrial setting, where we evaluate both the effectiveness and efficiency of AI coding agents in supporting the migration of legacy systems to modern platforms. By using one version of Claude Code agent, we migrated 12 features of distinct complexity levels from a corporate ERP system written in Visual Basic 6 to C# .NET 10 under a single-generation strategy. Once the migration sessions were completed, we measured the equivalence of the migrated features against the original ones, and collected data about the time spent and the number of tokens consumed by the agent during the process. In our experiment, the agent achieved an average equivalence of 70%, with a strong asymmetry across complexity levels; low-level features achieved 92%, and high-complexity ones scored 47%. Similar asymmetry was observed in the cost-based metrics; low-level features consumed 1.47M tokens ($1.66), whereas high-level ones used 9.09M ($10.28). We reveal the practical scenarios and circumstances where this migration strategy is more effective, as well as discuss the limitations and challenges of using AI agents for legacy system modernization.
