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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25935 |
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| _version_ | 1866908622157512704 |
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| author | Dorado, Antía Folgueira, Iván Martín, Sofía Martín, Gonzalo Porto, Álvaro Ramos, Alejandro Wallace, John |
| author_facet | Dorado, Antía Folgueira, Iván Martín, Sofía Martín, Gonzalo Porto, Álvaro Ramos, Alejandro Wallace, John |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25935 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows Dorado, Antía Folgueira, Iván Martín, Sofía Martín, Gonzalo Porto, Álvaro Ramos, Alejandro Wallace, John Software Engineering Artificial Intelligence 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. |
| title | A Process Mining-Based System For The Analysis and Prediction of Software Development Workflows |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.25935 |