Apple researchers explore how AI can predict bugs, write tests, and even fix code

Apple has published three interesting studies that offer some insight into how AI can improve workflows, quality, and productivity for developers.

Apple: AI Model Detects Software Errors with 98 Percent Accuracy

Apple Research presents ADE-QVAET, an AI model for predicting software errors with 98 percent accuracy in tests.

Apple's research explains how AI can identify bugs in code and generate QE tests

New papers on Apple's machine learning blog detail how AI can be used for faster, cheaper, and more effective QE testing, as ...

Teaching AI to Predict What Cells Will Look Like Before Running Any Experiments

This is a sponsored article brought to you by MBZUAI. If you’ve ever tried to guess how a cell will change shape after a drug ...
EurekAlert!

Deep variational autoencoder for proteomics mass spectrometry data analysis

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...
Visual Studio Magazine

Generating Synthetic Data Using a Variational Autoencoder with PyTorch

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...
Semiconductor Engineering

Improving ML-Based Device Modeling Using Variational Autoencoder Techniques

A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and ...