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Propheticus ML framework simplifies predictive models for software security

By PulseAugur Editorial · [1 sources] ·

A new machine learning framework named Propheticus has been introduced to simplify the development of predictive models for reliable and secure software. This framework aims to abstract away the complexities of machine learning, making it easier for users to create and deploy models. Two case studies, focusing on vulnerability prediction and online failure prediction, demonstrate how Propheticus can streamline the machine learning workflow. AI

IMPACT Simplifies the application of machine learning for software reliability and security.

RANK_REASON The cluster contains an academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Propheticus ML framework simplifies predictive models for software security

COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jo\~ao R. Campos, Marco Vieira, Ernesto Costa ·

    Propheticus: Machine Learning Framework for the Development of Predictive Models for Reliable and Secure Software

    arXiv:1809.01898v2 Announce Type: replace-cross Abstract: The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learning (ML) has shown its applicability to various complex problems and is frequently…

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