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Overlooking performance testing leads to either a slowdown or an outage. Yet, lots of teams do it. Deadlines force them to prioritize functionality. Lack of expertise pushes them to cover the essentials that barely prepare the app for the real world. And the effort going into supplying environments is often a little too much. Still, what crews skip here comes back as overwhelming in-prod hotfixes, product delays, customer churn, and more.  This is a game of choosing the lesser evil. And you’re never going to win it. Because what you’re really choosing between are two risks with an incredible potential to escalate. So, what you need to do is to change the game’s rules. Today, we review the web performance testing checklist that allows you some leeway and prevents most production issues.  Understanding Web Application Performance Testing  A few things to know first. Number one, performance testing services are classified as non-functional. And that often makes us perceive them...

We explore the shift from traditional, "brittle" automation to Agentic AI testing — a system capable of reasoning, learning, and self-healing. You will learn the technical differences between fixed scripts and autonomous agents, see how they integrate into CI/CD pipelines, and discover how to transition your current QA strategy to a model that prioritizes business goals over manual maintenance. Traditional automation isn't good enough. Its business value will never be disregarded. But lately, more and more companies feel the pain of its limitations. They look for ways to bypass static tests, high maintenance, and mandatory human intervention. Agentic AI testing is that way. Not only does it minimize automation's risks, such as missed bugs, climbing costs, and slower releases, but it also offers superior adaptability and autonomy. AI testing agents don't just blindly follow instructions and fall apart when encountering the edge of the box. They can think and decide on their own, lear...

Development is approaching the speed of light. So, it's no wonder quality tends to lag behind. Companies now prioritize fast deliveries. And QA is being pushed to the side, out of the strategy core. With that, leadership loses visibility into quality processes. So, decisions regarding releases and their future success are based on incomplete data. You might be doing well with your deadlines. But delivery turns risky and unpredictable. Cue in metrics. They act as solid indicators of how your product is doing. They translate technical performance into business insights. And they prevent disconnect between dev teams and leadership by offering an accessible reference. Today, we discuss what performance testing metrics CEOs and product leaders should track to have better control, predictability, and confidence in release decisions. Transitioning to Measurable Quality with Software Performance Testing Metrics Performance metrics in software testing are values that help understand system be...

For your product to decline, a lot of things need to go wrong... is a misconception! Usually, it all begins with a very mundane matter. If you're careless with API testing or struggle with it for a while, everything can fall apart. Your team gets overloaded, development slows down, regression testing becomes a bottleneck, hotfixes and user-reported bugs pile up... If this carries on, your business doesn't. Today, we introduce another small change that has a big impact --- API testing with AI. The Impact of AI in API Testing AI-based API testing won't be the miracle you've always wanted. But it will be a point of origin for modifications you definitely need. Let's break down how these changes happen. Limitations of Current API Testing Practices First, we need a benchmark. We'll see how AI shifts API testing services by reviewing traditional approaches. Manual checks offer flexibility. They make it easier to work with new or unstable APIs and are generally simpler to kick off. They als...

Growth is painful. The transition from a small project with a tight-knit team to a large-scale enterprise requires strategic speed. In 2026, your customers won't wait for you to figure out your next steps; they expect perfection. Under this pressure, many business owners prioritize speed over quality, entering a "danger zone" where the QA process is rushed, coverage is unbalanced, and developers waste time on hotfixes. How do you resolve this? By working with a specialist who brings structure and quality ownership. Today, we discuss QA Lead responsibilities and why this expert is vital for your software testing company strategy. Originally Published: Oct 16, 2025 | Last Updated: March 14, 2026 Why are QA Team Lead Roles and Responsibilities Essential for Fast Growth? QA Lead is somewhat of an exception. They are both the jack and the master of all trades. And their duties result in two core aspects that secure confident product growth: Quality in every action of the development te...

We like to marvel at how technology keeps changing. Yet, some things stay the same. Some teams struggle with an avalanche of regression tests. Some with the complexities of scaling. Some with how difficult hiring talent has become. And the lucky ones grapple with all of them and more. The progress of tech creates new challenges. It's like the great filter: to survive and thrive, you must overcome the barriers in front of you. Today, we discuss a solution that can help you with that — automated test case generation. What Is Automated Test Case Generation? Automated test case generation (ATCG) is the use of tools and algorithms to create test cases. With the traditional approach, specialists go through specs, user stories, requirements, etc. Then, they identify relevant test scenarios. And finally, QA engineers create test cases themselves, writing them by hand. When done manually, this process can take around ten minutes. But per one test. So, this adds up significantly. And given t...

The cost of poor QA is high. Critical bugs in production, negative client feedback, hotfixes that eat away at dev time... That's just scratching the surface. If you've ever dealt with that, you know the value of root cause analysis in refining your team, product, and business. But you must also know how demanding it is. Luckily for everyone, AI found its way into this discipline as well. And it made it simpler, faster, and more precise, saving your resources. Today, we break down what AI-powered root cause analysis is, how it works, and why it matters for your business. What Is AI Root Cause Analysis? AI-based root cause analysis is the use of artificial intelligence to handle data work during RCA. You may have expected a definition that made it sound like a silver bullet. Well, don't be disappointed. Most of RCA is gathering, cleaning, organizing, and analyzing data. And AI is perfect for handling that. AI can quickly sift through massive log files or monitoring data. It can pinpo...

A critical bug in production isn't your greatest nemesis. Events that created it are. If you don't want to waste money, resources, and your team's precious time on treating just the symptoms, you should develop expertise in root cause analysis techniques. Because they transform mistakes into lessons that make your product better. What Is Root Cause Analysis? Root cause analysis (RCA) is a problem-solving method that aims to identify the reason for an issue. Its defining characteristic is that it doesn't treat the bug itself as an error. Only as a symptom of something bigger. For example, a software bug doesn't necessarily mean that someone failed. It can be due to poor code coverage, incorrect prioritization, faulty testing processes, or everything combined. An important aspect of techniques for root cause analysis is that they avoid assigning blame. They also view errors as growth opportunities. For instance, if a junior QA engineer missed a test, you should upskill them. If a devel...

Automating business workflows is a key competitive advantage. To establish a fine-working automated system, a CRM solution is not just a choice, but a necessity. Growth exposes process gaps; the next necessary step is to standardize them in a CRM. However, the "stumbling block" for making the right decision is data migration. This step has stopped many businesses on their path to automation because the data was messy, inconsistent, or incomplete. According to Forbes, only 36 % of data migration projects succeed, meaning 64 % fail in meeting objectives or staying within budget and schedule. But we are here to help you! Today, our team, together with CRMiUM experts, wants to share practical advice on migrating data into a new CRM system. This post will highlight how good planning ensures that no records are lost and your company can start using the CRM with confidence. Common Pitfalls in CRM Data Migration Moving to a new CRM promises clarity and automation of workflows, yet data migra...