Test design is at the heart of effective testing, a crucial process that can make or break your software quality. Why invest time and resources in managing, executing, maintaining, and automating test cases if they don't directly address business needs or mitigate production risks?

Software quality has evolved over time. Initially, the focus was on finding and resolving defects prior to a product launch. As technology advanced, the emphasis shifted towards maintaining quality across the whole development process, particularly with the rise of Agile and DevOps methodologies. Quality is now approached proactively, encompassing all phases of the product lifecycle - from planning to deployment.

AI is becoming a part of our everyday lives, and in the software testing industry, it is starting to show its impact as well. Traditional testing methods can often happen at a later stage of the development life cycle, which may present challenges for meeting the demands of modern software delivery. This is where shift-left testing comes to shine. This testing methodology has become one of the most used strategies for delivering high-quality software without missing bug findings along the way.

Traditional testing approaches can struggle to keep up with the dynamic nature of modern development cycles. While effective in certain contexts, these methods may miss some defects, which can lead to costly fixes, customer dissatisfaction, and damage to the product's reputation. The problem lies in the limitations of scripted testing, which might lack the flexibility required to identify novel defects.

Combining Artificial Intelligence and human intelligence in testing becameessential for delivering high-quality products quickly and efficiently. AI excels at automating repetitive tasks, analyzing vast datasets, and improving test coverage. Humans, on the other hand, bring creativity, critical thinking, and the ability to handle complex scenarios that machines can’t easily navigate. Together, they form a powerful synergy that enhances speed and accuracy in testing but also brings challenges..

While still in its early days, artificial intelligence is becoming a driving force behind innovation in software testing. While automation has improved testing efficiency, AI can take it further by influencing critical decision-making. Rather than reacting to issues as they arise, teams can now identify potential problems earlier in the development cycle. In this article, we’ll explore how artificial intelligence can help teams rethink their testing strategies.

The dynamic landscape of product development continuously increases the scope of what is demanded from quality engineering (QE) professionals. So, it is not surprising that workplace learning and upskilling are critical to the organizational success, as highlighted by Workplace Learning Report: Without a doubt, the more technical side of the skill spectrum - e.g.

The exploratory tester's role goes beyond finding bugs. It involves understanding user behavior, improving software quality, gathering feedback, and refining testing strategies. As an exploratory testing expert, you are an investigator who understands the software, uses creativity and intuition to guide the session, and discovers new ways to uncover hidden issues.

Pipelines for continuous integration and continuous deployment, or CI/CD, are critical for contemporary software teams that aim for speed and agility. These pipelines, which automate the integration, testing, and deployment of code changes, enable teams to release new features and improvements quickly. There are obstacles involved in creating an effective CI/CD pipeline, though. This article explores common obstacles teams face in CI/CD implementation.

Increasingly digital consumption of products and services, along with the shift to remote work and education, have significantly affected how software is developed and tested. According to Deloitte’s “2023 Quality Engineering Trends” report, “new technologies” is still the top factor driving testing spend.