The latest News and Information on Software Testing and related technologies.
The two key pillars of building reliable applications are: testing and monitoring. With testing, you can verify that each pull request works before it’s merged and deployed to production. Just testing isn’t enough, though. You also need to make sure that the application continues to work on production. Database rollovers, third-party outages, and unexpected spikes in traffic can all cause issues that need to be detected.
This is the fourth part of our 12-day Advent of Monitoring series. In this series, Checkly's engineers will share practical monitoring tips from their own experience. One challenge in conducting end-to-end (E2E) testing is managing the artifacts created during the process. These artifacts are necessary for asserting specific functionalities.
Learn about throughput in performance testing and get a step-by-step guide for determining maximum TPS with production traffic replication.
PyTorch is an open-source machine learning (ML) framework that accelerates the path from research prototyping to production deployment. You can work with PyTorch using regular Python without delving into the underlying native C++ code. It contains a full toolkit for building production-worthy ML applications, including layers for deep neural networks, activation functions and optimizers. It also has associated libraries for computer vision and natural language processing.