A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows
This addresses software project management by enabling proactive deadline prediction, though it is incremental as it combines existing process mining and LSTM techniques.
The researchers tackled the problem of predicting deadline compliance in software development workflows by developing CodeSight, an end-to-end system that integrates process mining with an LSTM model to analyze GitHub data and predict PR resolution times, achieving high precision and F1 scores in tests.
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.