2024 has been a busy year so far for Parasoft, the California-based software testing solution provider.
Following a range of new product releases, with the launch of a new platform specifically aimed at developers involved in C and C++ applications as one of its highlights, the company recently made a number of updates to its accessibility testing solution, as well as its application programming interface (API) and micro-services.
Moreover, Parasoft opened a new office in Ohio in June and it hired industry veteran Joanna Schloss – a former SmartBear manager – as the company’s new chief marketing officer.
Amid this flurry of activity, Parasoft, like any other QA player, faces enormous changes within the tech space while the industry is under increasing pressure to deliver faster, better and more efficient solutions as the digital infrastructure of banks, financial services firms and other demanding clients rapidly swells.
Time for QA Financial to sit down with Elizabeth Kolawa, since 2011 the CEO of Parasoft as she explains her strategic vision to expand the company’s market presence while discussing the rise of AI, current market conditions, regulation and compliance, as well as the biggest risks and challenges the QA industry faces at the moment.
Kolawa, whose career started with a Ph.D. in physics in Poland in the early ’80s, has been granted 11 patents throughout her career while she worked for Caltech for more than a decade before joining MTS and eventually moving on to Parasoft 13 years ago.
QA Financial: The market is evolving rapidly at the moment, and the QA space is arguably more competitive than ever. What are some of the biggest challenges you are currently facing?
Elizabeth Kolawa: The challenges we face reflect those of our customers: automating and optimizing testing in increasingly complex and diverse software ecosystems. Modern applications are interconnected, built with microservices across various technological stacks, and incorporate an array of technologies like APIs, cloud services, and open-source solutions.
These environments require sophisticated testing strategies for comprehensive coverage. Our customers need scalable solutions that adapt to evolving technology stacks, including additional integration and testing for open source solutions due to limited quality and compliance oversight.
“Much compliance testing still happens late in the cycle, resulting in delayed detection of vulnerabilities or defects.”
– Elizabeth Kolawa
We prioritise evolving with our customers’ testing needs. As microservices architectures become more prevalent, we have enhanced our platform to support microservices testing. Our recent updates include capabilities for code coverage across functional, API, end-to-end, and UI testing of Java or .NET-based microservices within distributed ecosystems. Additionally, we have improved our test impact analysis to integrate better with third-party test automation frameworks, ensuring thorough testing across diverse tech stacks.
Let’s dive a bit deeper into that. Are streamlining test automation and ensuring continuous compliance increasingly a priority for QA teams?
Yes, streamlining test automation and ensuring continuous compliance are increasingly top priorities. As development cycles accelerate with Agile methodologies and as regulatory requirements become more stringent, QA teams are automating tests to enhance efficiency and maintain consistent compliance. However, they’re under pressure to ensure testing keeps pace with development, making the job more challenging.
Much compliance testing still happens late in the cycle, resulting in delayed detection of vulnerabilities or defects, increased remediation costs, and postponed releases. While test automation is essential, many QA teams are looking beyond only running tests in automation. They need solutions that help shift compliance testing left and increase the speed of testing at the creation, maintenance, and execution levels.
Financial businesses are facing enormous challenges with traditional QA and testing methodologies. Increasing the speed of application development and deployment, without compromising on quality, has become a critical factor for success. How does your company play into this?
With Agile methodologies quickly increasingly dominating software development, QA teams are consistently under pressure to keep up with the rapid pace of Agile sprints. This demand for high quality testing within shortened times has heightened the need for test automation optimizations.
QA teams must find solutions that go beyond automating test execution in CI/CD pipelines. They need tools for better test creation, easier and faster test maintenance, and increased test execution efficiency. Our platform provides test automation solutions across the entire SDLC enabling QA teams to start testing earlier, use AI to accelerate test creation, eliminate known testing bottlenecks, and focus, with efforts on new or modified parts of the application to prevent untested code from reaching production.
For example, our SOAtest solution allows teams to shift left functional testing to the API layers and quickly generate test cases through AI analysis of service definition files. Many customers using our ML-based test impact analysis capabilities report a 90% acceleration in their test cycles.
Am glad you brought up AI, it is undoubtedly the number one buzzword within the QA space at the moment. How much of a game changer is GenAI?
Generative AI is transforming software development and testing. We see significant potential in using generative AI to simplify and accelerate test creation, and we’ve already incorporated this into our Continuous Quality Testing Platform. One major benefit of generative AI in testing is the ability to make testing more accessible for less technical testers, which is crucial for organizations looking to scale their teams without needing specialized skill sets.
“AI should enhance the tester’s role, rather than replace it.”
– Elizabeth Kolawa
For instance, our SOAtest can integrate with OpenAI and Azure OpenAI, allowing testers to generate parameterized API scenario tests without coding. By leveraging AI analysis of service definitions and utilizing either custom or preconfigured command prompts, our solution makes it easy for testers with limited technical knowledge to create API test scenarios.
Regarding code development, generative AI boosts productivity, but more code often means more testing. Tools like Copilot are improving in quality assurance, yet the increased volume of code heightens the demand on QA teams. To keep up with the fast pace of development, test automation, continuous testing workflows, and optimizing CI/CD pipelines are essential.
So if we dive a bit deeper. How can these technologies best be exploited in QA testing? And what do you see as the pitfalls businesses should look out for?
We strategically integrate generative AI into our solutions to enhance the tester’s role rather than replace it. We use GenAI to automate tedious and time consuming testing tasks, while ensuring that human oversight is maintained. A key challenge with GenAI is its tendency to generate incorrect or irrelevant test cases, so reviews are essential.
Another risk is the potential drop in test quality when focusing on quantity. Generative AI might produce tests that instrument the application but don’t effectively validate functionality or lead to repetitive tests. Maintaining large, ineffective test suites can slow down testing and add on to the QA team’s workload. This is why Parasoft ensures that AI assists testers in improving efficiency rather than replacing them.
Our approach to integrating generative AI surpasses what many startup solutions provide. We combine our extensive testing expertise with large language models to produce high-quality generated test cases. Generative AI in software testing should enhance and accelerate the tester’s work, not replace it!
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