California-based software testing solution provider Parasoft has launched new Agentic AI capabilities within its entire testing and analysis tool suite, the firm shared.
The move allows QA teams to generate API test scenarios using natural language commands, the company pointed out, announcing it at the same time a host of updates to its Continuous Testing Platform (CTP), integrating Test Impact Analysis (TIA) and extended code coverage for manual testers, aiming to bridge gaps between development and quality assurance teams.
Agentic AI enables less technical testers to execute complex, multi-step API testing scenarios by simply inputting natural language commands, ideal for smaller or less experienced QA teams at smaller financial services firms our boutique firms.
The AI Assistant collaborates with testers to generate test data, parameterize scenarios, and handle dynamic data workflows, reducing the need for advanced coding skills.
“The Agentic AI capability reflects our focus on pragmatic automation that helps users achieve faster, smarter outcomes,” explained Igor Kirilenko, since August 2021 the company’s chief product officer.
“This enhanced test impact analysis and code coverage aggregation, spanning automated and manual testing, helps teams meet their coverage goals with greater speed and agility.”
The AI Assistant, initially introduced as a chat interface for real-time technical guidance, now extends to test data generation and data looping.
“In previous releases, the AI Assistant provided technical guidance,” Kirilenko noted. “Now, it actively participates in generating API test scenarios using service definition files.”
Manual and automated testing
Parasoft’s CTP now includes code coverage collection for manual testing workflows, allowing teams to capture test coverage data during manual test runs and publish it to Parasoft DTP for further analysis.
This consolidated view helps identify coverage gaps and refine testing strategies, stressed Daniel Garay, director of Software Quality Assurance at Parasoft.
“Enhanced code coverage across both automated and remaining manual tests is vital to keeping pace with development and getting quicker feedback,” Garay noted.
The CTP update also extends Test Impact Analysis capabilities to manual testing, enabling testers to isolate critical regression tests based on recent code changes. By identifying which tests to rerun, QA teams can streamline testing cycles and reduce retesting time, he explained.
“This new capability also makes it easier to adapt manual regression testing for agile sprints,” Garay added. “With faster test cycles, QA teams can quickly validate changes and shorten feedback loops.”
AI and ML merged
The new launches come only months after Parasoft said its engineers had come up with a new solution that integrates artificial intelligence tools and machine learning capabilities in order to help banks and other financial services firms to bring down their testing failure rates.
By combining AI and ML, together with the integration of its latest large language models (LLMs) using natural language for troubleshooting, or checking code in real time, firms should be able to detect flaws, bugs and failures much faster, the firm claimed.
Kirilenko explained that the latest product investment in AI serves “a singular purpose,” namely to address “the quality risk developers and testers face as they’re pressured throughout product release cycles.”
He elaborated that, “as more development teams turn to AI, the foresight of this approach underscores their commitment to helping users augment quality.”
Moreover, Kirilenko stressed it helps financial organisations to integrate any large language models they already use into advanced testing workflows.
“These latest enhancements are backed by dozens of our technology patents, designed to ease the journey to fully autonomous software testing,” Kirilenko noted.
He added that, in the latest product releases, developers gain greater control and feedback in strategic stages of the software development lifecycle.
Moreover, live unit testing has been added for real-time code verification, which lets lets developers continuously execute unit tests impacted by code changes in their Integrated Development Environment (IDE).
The ability to validate code changes before checking them into source control is “a major time saver,” Kirilenko claimed, which should result in fewer build and regression failures.
Meanwhile, he stressed that Parasoft’s machine-learning engine operates “in the background,” correlating recent code changes to impacted unit tests while autonomously executing tests in the IDE, giving the engineer continuous feedback.
Busy period
The last twelve months have been a busy period for Parasoft. 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 July 2024 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.
Elizabeth Kolawa, since 2011 the CEO of Parasoft, recently told QA Financial that “the challenges we face reflect those of our customers: automating and optimising testing in increasingly complex and diverse software ecosystems.”
She stressed that “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.
Compliance
One of the main items on most financial services firms’ agendas is currently compliance, and therefore also on the priority list of Parasoft and other major testing firms.
Kolawa acknowledged that as she said that “yes, streamlining test automation and ensuring continuous compliance are increasingly top priorities.”
She explained that “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 are 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, Kolawa added.
“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.”
When zooming in on GenAI, undoubtedly the buzzword in the QA space at the moment, Kolawa said that a major benefit of generative AI in testing is the ability to make testing more accessible for less technical testers.
She called this “crucial for organizations looking to scale their teams without needing specialized skill sets.”
Moreover, with regards to 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,” Kolawa continued.
To keep up with the fast pace of development, she called test automation, continuous testing workflows, and optimizing CI/CD pipelines “essential.”
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