Operations | Monitoring | ITSM | DevOps | Cloud

Speedscale was built primarily to provide engineering teams with better insight into their applications over time, replaying single transactions for root cause analysis that give developers and SREs confidence that tomorrow’s application code will work just as well in production as it did yesterday.

SQL injection is one of the most destructive ways an application can be attacked. This kind of attack is targeted toward the application database, which can result in consequences that are irreversible, lead to loss of money, and reduce user trust in your company. There are far too many application data breaches happening every day, usually when a malicious agent attacks the database.

To transform their application delivery, many teams are employing model-based testing (MBT). Through this increasingly popular approach, teams are realizing a number of significant benefits, including improving test coverage, reducing testing effort, and improving quality. In this blog, I’ll examine the use of MBT, but with a particular focus on some of the objectives we often hear DevOps teams say they’re focused on.

Testing in production simply means testing new code changes in production, with live traffic, in order to test the system’s reliability, resiliency, and stability. It helps teams solve bugs and other issues faster, as well as effectively analyze the performance of newly released changes. Its overall purpose is to expose problems that can’t be identified in non-production environments for reasons that may include not being able to mimic the concurrency, load, or user behavior.

For the people using Azure Service Bus namespaces – we often have the need to ensure the Azure Service Bus Performance by testing our system integration by generating some test messages on the Azure Service Bus resources. You might need this for QA/Development for performance testing, load testing etc. This blog will explain how to simulate the test environment using Serverless360 to check Azure Service Bus performance and its throughput.

In this video with with Kohsuke Kawaguchi (KK) from Launchable and Viktor Farcic we talk about testing K8s applications. Do we really need to write all the tests every time we make a change to the source code or make a release? That could take minutes or even hours. Wouldn’t it be better to run only the tests related to the changes we are making or the phase of the lifecycle of an application? Is the future of testing in AI and ML?

Data is the backbone of any organization. Good data supports the business and helps ensure smart business decisions are made. Bad data, on the other hand… well, you don’t want bad data. But how do you make sure you have accurate and valid data? Data validation and testing are great places to start.

This is part one of a two-part series. Achieving excellence in continuous testing is not just about mastering all the new tools, programming languages, and frameworks. It involves developing a deep understanding of the product you are testing. What follows are some tips that can help. I think a lot about what it takes to be a good tester.

This is part two of a two-part series. If you have not done so, read Part 1. Achieving excellence in continuous testing is not just about mastering all the new tools, programming languages, and frameworks. It involves developing a deep understanding of the product you are testing. What follows are some additional tips that can help.