用过程挖掘+LSTM预测代码合并进度，提前发现延期风险。
A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows
- 从GitHub抓取数据生成流程日志，分析提交模式和效率
- 基于序列轨迹与静态特征，预测剩余合并时间，准确率高
- 适合项目管理者用于主动预警延期，提升开发流程可控性
CodeSight 是一个端到端系统，旨在预测软件开发工作流中的截止日期合规性。它直接从 GitHub 捕获开发与部署数据，将其转化为过程挖掘日志以进行深入分析。基于这些日志，系统生成指标与仪表盘，提供关于拉取请求（PR）活动模式和工作流效率的可操作洞察。在此结构化表示基础上，CodeSight 采用 LSTM 模型，依据序列化活动轨迹与静态特征预测剩余的 PR 解决时间，实现对潜在截止日期违约的早期识别。在测试中，该系统在预测截止日期合规性方面展现出高精度与高 F1 值，验证了将过程挖掘与机器学习结合在主动软件项目管理中的价值。
原文摘要 · Abstract (English)
CodeSight is an end-to-end system designed to anticipate deadline compliance in software development workflows. It captures development and deployment data directly from GitHub, transforming it into process mining logs for detailed analysis. From these logs, the system generates metrics and dashboards that provide actionable insights into PR activity patterns and workflow efficiency. Building on this structured representation, CodeSight employs an LSTM model that predicts remaining PR resolution times based on sequential activity traces and static features, enabling early identification of potential deadline breaches. In tests, the system demonstrates high precision and F1 scores in predicting deadline compliance, illustrating the value of integrating process mining with machine learning for proactive software project management.
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