Hyperautomation (using AI to drive decision-making) is becoming a leading strategic technology trend that integrates RPA, AI/ML, intelligent business management software, and other emerging technologies to increase automation in enterprises. This trend is influencing software test automation evolution, as different tools, frameworks, and custom-developed solutions continue to enhance automation penetration and efficiency.
The test automation evolution began with the automation of user interface, API, and database layers using open-source tools such as Selenium and Appium. With accelerated agile culture, a digital business, and DevOps technologies such as AI/ML capabilities, zero-touch automation pipelines, and self-healing automation scripts, have made testing smarter. As a result, teams have optimized their automation strategies to adapt faster and operate more effectively.
In the test automation domain, enterprises use hyperautomation to improve cycle time and the process. Hyperautomation technologies are raising test automation’s maturity level through RPA tools like UiPath, Appian, and Automation Anywhere; LC/NC tools like Tricentis and Katalon; and AI, ML, and NLP advances and autonomous testing tools like AutonomIQ.
A leading health care firm wanted to adopt an AI-led cognitive automation solution, which combines the best automation approaches with AI to deliver superior results. The company, in partnership with Infosys, developed a solution with three key focus areas: eliminate test coverage overlaps, optimize efforts with more predictable testing, and move from defect detection to defect prevention.
DevSecOps introduced security earlier in the SDLC, expanding collaboration between development and operations teams in DevOps to include security teams. Security testing tools were introduced but were not integrated with continuous testing pipelines. This evolved into a shared responsibility, with everyone playing a role in building security into the DevOps CI/CD workflow. Later, DevSecOps integrated application security testing (AST) tools into the CI/CD process. SAST tools (e.g., Micro Focus Fortify) were used to identify coding errors and design flaws, leading to exploitable weaknesses. DAST tools (e.g., Micro Focus WebInspect) helped automate black box security testing to mimic how a hacker interacts with a web application or an API. And finally, SCA tools (e.g., Black Duck) were implemented to identify known vulnerabilities in open-source and third-party components. These integrations within the CI/CD pipeline accelerated the identification and remediation of security vulnerabilities earlier in the cycle.
The trend has matured further with the implementation of AI/ML for security defense and risk prediction, and with automated vulnerability assessment and management. AST now includes two additional tools: IAST to detect runtime vulnerabilities and provide detailed insights to developers and RASP to identify threats and support self-protection.
A high-tech U.S. company, in collaboration with Infosys, built a threat intelligence database to suppress false positives. The client achieved 25% faster time to market, 100% code coverage and OSS components, and a 50% reduction in common vulnerabilities.
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