AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Overall, the findings show that static code metrics can support maintainability assessment, while project-level heterogeneity remains a major challenge for reliable prediction in unseen software projects.
Predicting risky software changes before deployment can help prioritize code review and testing, particularly in continuous integration and continuous deployment (CI/CD) environments. This paper presents an interpretable supervised learning framework for commit-level software change risk prediction using structural, hi...
The software quality risk prediction helps in resource allocation for software testing and maintenance by predicting the risk of software components. Recently, the research has moved from statistical and machine-learning classifiers to feature selection, class-imbalance handling, ensemble learning, source-code represen...
Predicting software reliability is important but difficult: traditional statistical models and manually tuned ML approaches fail to express nonlinearities present in modern software and often demand extensive human domain knowledge. We evaluate an end-to-end automated hyperparameter search pipeline of RF, GB, and SVM c...
The increasing adoption of DevOps practices has accelerated software delivery cycles, creating a growing demand for intelligent testing strategies that maintain software quality without delaying continuous integration and continuous deployment (CI/CD) pipelines. Code coverage remains one of the most widely used indicat...
The analysis indicates that no single model is universally optimal; robust software failure prediction requires dataset-aware preprocessing, leakage-safe validation, imbalance-aware evaluation, and an explicit trade-off among predictive performance, computational efficiency, and interpretability.
: Software Defect Prediction (SDP) plays a critical role in improving software quality by identifying fault-prone modules early in the development lifecycle. While machine learning models (particularly black-box) have demonstrated high predictive accuracy, their lack of interpretability limits their adoption in environ...
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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