用生成式AI分析银行旧系统故障，发现七成问题源于代码缺陷。
Breaking the Cycle of Recurring Failures: Applying Generative AI to Root Cause Analysis in Legacy Banking Systems
- 结合AI与五问法，自动挖掘事故深层原因。
- 分析5000+项目后，发现400多个文件存在相同根因。
- 适合金融、IT运维等需提升系统稳定性的团队。
传统银行在数字化转型中面临重大挑战，主要源于旧系统限制和责任归属碎片化。近期事件显示，这种碎片化常导致表面修复，未能解决根本原因，引发重复故障。本文提出一种新型事后分析方法，将基于知识的生成式AI代理与‘五问法’结合，分析问题描述和变更请求数据。结果显示，约70%此前归因于管理或供应商的问题，实为内部代码缺陷所致。通过扫描超过5000个项目的代码库，我们识别出400多个具有相同根因的文件。该方法利用知识型代理实现根因分析自动化，推动其向更主动的流程转变，未来可扩展至软件开发生命周期的其他阶段，进一步提升开发效率。
原文摘要 · Abstract (English)
Traditional banks face significant challenges in digital transformation, primarily due to legacy system constraints and fragmented ownership. Recent incidents show that such fragmentation often results in superficial incident resolutions, leaving root causes unaddressed and causing recurring failures. We introduce a novel approach to post-incident analysis, integrating knowledge-based GenAI agents with the "Five Whys" technique to examine problem descriptions and change request data. This method uncovered that approximately 70% of the incidents previously attributed to management or vendor failures were due to underlying internal code issues. We present a case study to show the impact of our method. By scanning over 5,000 projects, we identified over 400 files with a similar root cause. Overall, we leverage the knowledge-based agents to automate and elevate root cause analysis, transforming it into a more proactive process. These agents can be applied across other phases of the software development lifecycle, further improving development processes.
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