Towards Generalizable Models in Software Failure Prediction: A Machine Learning Approach скачать в хорошем качестве

Towards Generalizable Models in Software Failure Prediction: A Machine Learning Approach

🔍 What this video covers ✅ Why traditional failure prediction models often fail to generalize ✅ Key challenges: dataset shift, project-specific bias, and class imbalance ✅ Feature engineering for failure prediction (code metrics, process metrics, defect history, logs) ✅ ML pipeline: data preprocessing → training → validation → testing ✅ Techniques to improve generalization: cross-project learning, normalization, regularization, transfer learning, and domain adaptation ✅ Model evaluation using reliable metrics: Precision, Recall, F1-score, AUC, MCC ✅ Practical impact: reducing downtime, improving release quality, and supporting proactive debugging 🎯 Who should watch? Students, researchers, software engineers, QA teams, and DevOps professionals working on software reliability, defect prediction, and ML for software engineering. Publisher: Jack Sparrow Publishers Journal : International Journal of Research and Development in Engineering Sciences (IJRDES) , www.ijrdes.com , e-ISSN: 2582-4201 Paper Title: Towards Generalizable Models in Software Failure Prediction: A Machine Learning Approach Paper Link : https://ijrdes.com/paper-view/towards... DOI : https://doi.org/10.63328/IJRDES-V7CIP3 Software systems today are complex and constantly evolving—making reliable failure prediction a critical need for quality assurance, DevOps, and maintenance. In this video, we present “Towards Generalizable Models in Software Failure Prediction: A Machine Learning Approach”, focusing on how machine learning can predict failures while remaining robust across different projects, versions, and environments. 📌 Disclaimer This video is for educational and research discussion purposes only. Any datasets, results, and examples are presented for learning and may be simplified for explanation. 👍 Like | 💬 Comment | 🔔 Subscribe for more research-based tech content! #SoftwareEngineering #MachineLearning #FailurePrediction #DefectPrediction #SoftwareReliability #AI #DataScience #DevOps #QA #Research #SE4ML