New York City-based software testing company headquartered QA Mentor has introduced a new testing methodology named Human Intelligence Software Testing (HIST), developed by its founder and CEO Ruslan Desyatnikov.
His new discipline emphasises human judgment, intuition, ethics, and emotional awareness at the core of software testing, rather than relying predominantly on automation and artificial intelligence, according to Desyatnikov.
The industry insider says he developed HIST after observing persistent issues in software testing, despite the proliferation of automation tools.
“Automation scripts were running, dashboards were green, but critical issues were still making it into production,” Desyatnikov explained, emphasising that human oversight could have prevented many of these problems.
New role
Under the HIST approach, QA Mentor has created a new professional role called the Human Intelligence Software Tester (HISTer).
Unlike traditional testers focused on executing scripts or writing test cases, HISTers prioritise strategic thinking, risk assessment based on business impacts, and adaptability to project and domain-specific needs.
HISTers are also trained to leverage AI and automation tools responsibly without relying solely on their outputs. Their responsibilities include identifying biases in AI algorithms, addressing subtle user experience issues, and effectively interpreting user feedback into actionable insights.
The HIST methodology is already in practical use within QA Mentor’s consulting and training offerings across sectors including banking and finance, as well as e-commerce, and retail.
Desyatnikov claimed that early client feedback indicates HIST’s approach has significantly improved software quality assurance, often helping projects stay on track by identifying issues automation alone could miss.
‘Redefining QA’
Desyatnikov’s new methodology comes as the tech sector faces unprecedented upheaval and widespread layoffs, with the QA industry feeling the impact, grappling with new uncertainties and the rise of automation and new technologies, most prominently AI.
This shifting landscape presents significant challenges that demand attention, he argued. In fact, it’s time to confront how these changes are reshaping the world of quality assurance and what it means for professionals in the field.
Amid this industry upheaval, Desyatnikov is convinced the QA sector must seize the opportunity to leverage automation and AI to stay competitive.
“As AI testing solutions advance, QA professionals need AI tools to remain marketable,” he stressed.
By integrating AI, companies can optimize efficiency with AI. “AI-driven tools quickly spot patterns and anomalies often missed by traditional methods, streamlining testing, cutting manual work and boosting productivity,”
Desyatnikov said, adding that AI also prioritises test cases based on data, ensuring focus on critical areas.
Moreover, AI reduces manual labour by predicting defects early, minimising rework and errors. “AI-driven resource management enhances efficiency and keeps budgets lean,” he said.
“To effectively address the challenges facing the QA industry, firms must adopt an approach that includes targeted employee training and upskilling,” Desyatnikov went on to say.
Further, he calls on the industry to enhance human resource allocation. “With routine tasks automated, QA teams can tackle complex challenges and innovation. AI augments human expertise, allowing teams to focus on strategic initiatives and elevate service delivery,” Desyatnikov explained.
“As AI testing solutions advance, QA professionals need AI tools to remain marketable.”
– Ruslan Desyatnikov
In addition, the QA space should develop new AI-driven services, Desyatnikov argued.
“Adopting AI enables companies to create new testing services tailored to evolving market needs. AI offers the scalability and adaptability needed to stay ahead with innovative solutions,” he said.
Also, Desyatnikov thinks it’s vital workflows are modernised. “AI accelerates test execution by auto-generating cases and scripts, improving accuracy and ensuring consistent, reliable results across environments,” he noted.
Finally, firms should strengthen risk management. “AI-powered testing tools help identify potential risks early in the process, offering predictive insights that reduce the likelihood of critical failures,” Desyatnikov shared.
“This proactive approach helps ensure higher-quality releases and mitigates costly issues in production,” he added.
Challenges for QA teams
Addressing these challenges may not be an easy task, Desyatnikov did warn, as he believes the QA space still has a long way to go.
“To effectively address the challenges facing the QA industry, firms must adopt an approach that includes targeted employee training and upskilling,” he said.
To enhance QA teams’ capabilities, it is vital to implement continuous learning programs, by focusing on advanced testing frameworks such as Selenium, Appium and JUnit to ensure QA engineers are proficient in the latest tools and methodologies.
Also not unimportant, Desyatnikov argues that companies should provide training in areas like API testing, security testing and performance testing using tools like Postman, OWASP ZAP and Apache JMeter.
This all should lead to a culture in which expertise in test automation scripts is fostered, he stressed. “Encourage the use of languages like Python, Java and JavaScript to keep teams agile and adept at handling evolving testing requirements.”
Also a step that should not be skipped, Desyatnikov argues, is to invest in employee development. “Provide access to industry conferences and webinars to keep teams updated on the latest trends and best practices in QA.”
Traditional methods are over
In the context of present challenges, sticking to traditional methods is no longer sufficient, Desyatnikov continued.
“To stay competitive, companies must integrate data-driven insights into their quality assurance strategies,” he said.
“Begin by utilizing analytics tools to thoroughly analyse test results, track performance metrics and uncover patterns,” Desyatnikov added.
He believes this approach provides a clearer picture of where to direct testing efforts and how to refine processes.
Desyatnikov also thinks predictive analytics can help teams anticipate potential issues before they become critical, reducing the risk of defects in production.
“Optimising resource allocation becomes more precise with data.”
– Ruslan Desyatnikov
“By understanding workload and performance metrics, you can ensure that high-priority tasks receive the necessary focus and attention. Data insights also help refine test coverage strategies, revealing gaps and overlaps to ensure comprehensive and targeted testing.”
In other words, cultivating a data-driven culture is essential, he stressed, namely to “equip QA teams with the skills and tools to interpret data effectively, promoting a proactive approach to quality assurance and driving excellence in your testing processes.”
In summary, Desyatnikov said that, by optimising efficiency, controlling costs, modernizing workflows and upskilling employees, QA professionals can turn disruption into innovation.
“It’s time for the industry to shed its old skin, rise above the chaos and redefine what quality assurance means in a post-pandemic world,” he said.
“The future belongs to those who can navigate the turbulence with agility and foresight,” Desyatnikov concluded.
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