A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows

TL;DR

This paper introduces CodeSight, a process mining and machine learning system that analyzes GitHub data to predict software development deadline compliance, aiding proactive project management.

Contribution

It presents an end-to-end system combining process mining logs with LSTM models for accurate deadline prediction in software workflows.

Findings

High precision and F1 scores in deadline prediction

Effective integration of process mining with machine learning

Actionable insights for workflow efficiency

Abstract

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.