Boost-Classifier-Driven Fault Prediction Across Heterogeneous Open-Source Repositories

Ensuring reliability, availability, and security in modern software systems hinges on early fault detection, yet predicting which parts of a codebase are most at risk remains a significant challenge. In this paper, we analyze 2.4 million commits drawn from 33 heterogeneous open-source projects, span...

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Bibliographic Details
Main Authors:	Philip König, Sebastian Raubitzek, Alexander Schatten, Dennis Toth, Fabian Obermann, Caroline König, Kevin Mallinger
Format:	Article
Language:	English
Published:	MDPI AG 2025-07-01
Series:	Big Data and Cognitive Computing
Subjects:	fault prediction machine learning CatBoost code metrics feature importance artificial intelligence
Online Access:	https://www.mdpi.com/2504-2289/9/7/174
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Boost-Classifier-Driven Fault Prediction Across Heterogeneous Open-Source Repositories

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