We asked these tool providers to share more information on how their solutions help organizations test applications in their DevOps environments. Their responses are below. Gil Sever, co-founder and CEO of Applitools Modern software development teams are rapidly delivering innovation to market through more frequent and shorter release cycles, but they struggle to fully test … continue reading
Testing is a crucial piece of the software life cycle but QA teams often can’t produce enough test coverage quickly enough because they are bogged down by test maintenance. Test maintenance often takes more than 50% of a QA team’s time. testRigor, a plain English-based testing system, is easing the pain points of QA teams … continue reading
Over the last two decades, the swing of the pendulum from monolithic global tooling to highly specific tooling unique to each group and their needs has led to the birth of the tooling suite; the daisy chain of tools never intended to be linked together. One of the main challenges that has emerged is a … continue reading
Qualitest has announced the release of Qualisense, a new AI-powered software testing and QA toolkit. Qualisense is the next iteration of the company’s Qualisense Test Predictor service, and will be a standalone product. The new solution will leverage machine learning to optimize testing and quality delivery, remove bottlenecks, reduce the need for certain tests, help … continue reading
We test because something broke in the past, because we care about quality code, and we want to make sure the same thing doesn’t happen again. Quality Assurance (QA) testing was a response to the realization that we should proactively seek out problems in our software, before any new code is deployed into production, so … continue reading
Programmers err as much as any of us — between 15 and 50 errors per 1,000 lines of code to be more exact. QA tests for these bugs, attempting to ensure that releases are as bug-free as possible. Customers who trust their operations to software won’t tolerate poorly written code, and teams go out of … continue reading
The uptake in Agile and DevOps processes has led to changes in how software is written, tested, secured and deployed. Among the key changes organizations are making is to decentralize their test and QA teams. This is being done in response to organizations first looking to modernize testing practices by shifting testing to the left … continue reading
I like to see any company’s Quality Assurance (QA) as a living, breathing ecosystem. The ecosystem is defined by your business needs, the complexity of your application, and the innumerable ways in which you QA your system. Together, you, your developers, stakeholders, and customers all live in this ecosystem and vie for the free energy … continue reading
In recent years, the ubiquity and high speed of our current internet connections have led to important breakthroughs in the software industry. A relevant example would be the DevOps movement, which blurred the lines between developers and operations. A similar phenomenon is happening right now with the quality assurance field. Words and phrases like “testing,” … continue reading
I remember the cold sweat forming behind my ears the first time a stakeholder asked me, “What’s the Test Plan?” I had no idea. As a new product manager, I had put most of my energy into defining requirements. Without a Quality Assurance team in place, testing became an afterthought – one I didn’t really … continue reading
Today’s companies must become software companies to keep pace with competitive pressures and customer demands. As organizations become increasingly software-enabled, their footprints are extending out to cloud environments and the Internet of Things (IoT), increasing application complexity and the associated risks. With Synopsys, software teams can avoid the usual trade-offs between faster time-to-market imperatives, security … continue reading
The advent of Agile methodology, DevOps, and emerging technologies like artificial intelligence, robotic process automation and machine learning creates a seismic shift in application testing requirements and test tools. As companies gravitate towards digital transformation strategies, there are more applications and new technologies that require testing at increasingly rapid rates. Key challenges include the elimination … continue reading
