As organizations accelerate their digital transformation, software testing is evolving beyond traditional automation. While test automation tools remain relevant, autonomous testing, powered by AI and machine learning, is emerging as a game-changer. Hexaware’s testing services exemplify this shift, enabling self-healing tests, predictive analytics, and scriptless test generation. This blog explores how autonomous testing bridges gaps in failure analysis, accelerates releases, and prepares QA teams for the next wave of agile development and the future of software testing.
Transformation: Key Phases to Autonomous Testing
Transformation typically progresses from manual to assisted and, in mature cases, to unattended or autonomous modes. Most organizations integrate some automation at each step and maintain human oversight for governance and exceptions.
Manual Testing
Test cases are authored and executed by humans (often in spreadsheets or dedicated tools) based on a clear understanding of the application and objectives. The tester’s knowledge seeds scenarios that later inform automation and AI training and helps define the scope and requirements for subsequent stages.
Assistance-supported Testing
Teams identify which portions of execution can be automated while retaining human involvement for test management, quality checks, and defining objects and requirements (including cross-platform considerations). Automation supports execution; humans guide correctness and coverage.
Semi-automated Testing
Larger automated tasks run with well-defined logic and pass/fail criteria and can execute independently. Continuous feedback/loopback improves agility and accessibility for multiple scenarios. Human intervention remains important for oversight, tuning, and addressing failures or performance issues.
Automated Testing
Automation tools orchestrate most of the pipeline, including distributed execution, with source/version control improving manageability. Teams still maintain environments and handle defects, performance checks, and other practical details that require judgment.
Unattended Testing
The goal is to minimize day-to-day human involvement. ML assists with routine monitoring, minor test adjustments as scenarios change, and anomaly detection. Teams begin to adopt AI-enabled script creation and self-healing capabilities while retaining oversight for exceptions and policy compliance.
Autonomous Testing
The system leverages assets and artifacts from prior stages to learn from past failures, adapt tests to changing inputs, and manage end-to-end execution with minimal human input. QA focuses on governance and higher-value work rather than routine maintenance or execution.
What is Autonomous Testing?
Autonomous testing is the practice of generating, maintaining, and analyzing software tests with minimal human intervention. It often uses scriptless approaches that let teams create tests without hand-coding, reducing the amount of custom scripting required. This approach is typically powered by AI in software testing and machine learning models trained on historical test executions and past interactions, enabling systems to manage tests automatically, adapt to changes, and, in some cases, propose or generate new scenarios from limited inputs.
By lowering manual maintenance and accelerating scenario selection and analysis, autonomous testing can help QA teams speed up releases and shorten time-to-market, though actual impact varies by context.
Test Automation vs. Autonomous Testing
Over the past two decades, many organizations have shifted from the traditional waterfall model to more iterative agile approaches as software systems have grown in scale and complexity. That shift, along with expanding application scope, drove demand for tools that could accelerate testing, spurring the evolution of test automation to better manage test data, execute tests, and analyze results.
Automation tools programmatically simulate user actions (for example, clicks and keystrokes) using scripts or code to remove repetitive effort; Selenium is a widely used example of this approach.
A typical test automation framework combines tools with conventions, rules, and guidelines across the development lifecycle. When applied well, these practices can accelerate test cycles, reduce costs, and improve overall efficiency. At the same time, teams have encountered practical limitations with traditional, script-centric automation:
- Specialized skills are required to author and maintain automation, creating hiring and continuity challenges when business knowledge and programming expertise must coincide.
- Maintenance overhead grows as applications change; even well-written scripts often require updates, and debugging can be time-consuming.
- Record/playback approaches can be brittle; small changes may force re-recording large portions of a flow.
- Multi-window or multi-screen scenarios complicate data and state management, increasing maintenance complexity.
- Data-driven testing typically demands additional coding effort to parameterize and manage datasets.
These constraints have motivated interest in approaches that reduce manual scripting and improve resilience to change, setting the stage for more assisted, unattended, and ultimately autonomous testing capabilities.
Benefits of Autonomous Testing
Autonomous testing offers several advantages over traditional, script-centric automation frameworks. While conventional approaches require QA engineers to write code to create and maintain tests, autonomous testing can generate a broader test suite—spanning test scenarios and executable assets—more quickly and with minimal human input. In many implementations, it also encompasses test case design and execution, accelerating the selection of scenarios across environments and conditions. Automated identification of high-value scenarios and fit-for-purpose test data can reduce effort and cost, improving productivity and efficiency, and helping compress development cycles and time‑to‑market.
Modern AI/ML techniques underpin these gains by assisting with test selection and prioritization to shorten execution time. They also support earlier defect discovery with pattern analysis and root‑cause insights that lower failure risk. As applications change, autonomous approaches can update or adapt scenarios automatically, often described as self-healing or auto‑alignment, so suites remain in step with evolving UIs and workflows without constant manual rework.
Unlike traditional test automation, which can depend heavily on engineers with specific programming expertise, autonomous testing reduces reliance on specialized coding skills through scriptless or low-code authoring, while still benefiting from domain knowledge and human oversight for governance, complex edge cases, and policy compliance. This shift enables teams to reallocate time from routine scripting and maintenance toward exploratory testing, risk-based coverage, and exception handling.
Introducing Autonomous Testing Services
To achieve the goal of Autonomous Testing, you need optimized automation testing services. Hexaware’s unified Tensai® for Autonomous Testing platform is a plug-and-play architecture that allows DevOps to operate at the highest level of test automation maturity by addressing all the possible use case scenarios per industry needs. It comprises 205 use case scenarios, covering a wide range of personas and types of testing. These scenarios can address all requirements in an automated fashion, eventually making the software testing process independent of human intervention, marking a shift from AI in quality assurance to AI in quality engineering.
Tensai® integrates with all effective application lifecycle management tools, application performance management tools, log aggregators, defect management tools, and app stores available in the market. Organizations can deploy these services in the following modes:
- Out of the box: Tensai® will be deployed with Hexaware’s automation, test data management, performance, and security testing solutions
- Custom: Tensai® is integrated with the existing solutions for automation, test data management, performance, and security testing, as necessary
See how enterprises accelerate releases, cut costs, and improve reliability with Hexaware’s AI-first approach and Tensai® for Autonomous Testing:
- 50% reduction in test cycle time at a leading US-based auto finance brand; 35% test tool optimization; 50% faster time-to-market
- 100% on-time flight departures after a passenger service system modernization at a North American airline; 50% faster staff onboarding; 80% reduction in execution time across browsers/devices
- 15% increase in test design and execution productivity; 75% ServiceNow regression automation coverage; 80% reduction in test data generation cycle time at a multinational professional services firm
- 100% automation coverage across 40+ agile factories for a third-party financial services provider
- 75% reduction in execution time when testing 100% of data and a 60% reduction in overall QA cycle time via full automation at a Big Four accounting firm
- 100% data coverage for migration testing; >85% regression automation coverage for critical apps; 78% reduction in test execution time; ~40% reduction in testing cost
Do you also want to replicate the success of our numerous clients with Tensai®? Check out our real-world impact and how our state-of-the-art solution streamlines your testing processes.
Conclusion: AI-first Approach to Quality Engineering
Enterprises moving at the pace of digital disruption need quality that’s built in, not bolted on. An AI-first assurance model does exactly that: embedding quality into the product lifecycle, accelerating test cycles with faster feedback loops, and surfacing predictive insights that help teams maintain operational excellence even as change accelerates. Here’s how:
- AI-first assurance integrates quality into products, accelerates test lifecycles, and provides predictive insights, which are key to maintaining operational excellence during rapid digital transformation.
- Tensai® enables autonomous functional and non-functional testing with GenAI for in-sprint and continuous testing, improving speed, efficiency, and agility.
- Advanced capabilities include self-healing, computer vision for dynamic app changes, next-gen MLOps, and extensive tool integrations.
- Coverage spans scriptless UI/API, data/ETL, performance, SAST/DAST, chaos/resiliency, and testing of GenAI-infused apps/models—with real-time QA dashboards for metrics-driven governance.
- Customer experience and accessibility testing aligned to WCAG 2.1, ADA Section 508, EAA, and AODA frameworks.
Download our AI-first approach for quality engineering for deeper insights. For more information, please visit Hexaware’s autonomous testing services.
