In today‘s software delivery landscape, fast release cycles, frequent code changes, and cross-platform complexity have made manual testing extremely challenging. A recent survey found that lack of time and too many tests to run manually are the top barriers to effective testing reported by QA professionals.
To overcome these challenges, forward-looking organizations are increasingly looking at intelligent automation in the form of autonomous testing. This emerging paradigm promises improved velocity and quality by applying artificial intelligence and machine learning to software testing activities.
This comprehensive guide will explore all key aspects of autonomous testing including:
- Definition and working of autonomous testing platforms
- Quantitative benefits achieved by real-world users
- Applicability for different test types and use cases
- Techniques like computer vision and NLP that power autonomous testing
- Challenges faced by organizations in adoption
- Comparison with traditional test automation approaches
- The future outlook for autonomous testing
Let‘s get started.
What is Autonomous Testing?
Autonomous testing refers to test automation driven entirely by artificial intelligence and advanced analytics without any need for human intervention. Leading analyst IDC defines it as:
"The use of artificial intelligence (AI), analytics, and other techniques to automate and optimize the planning, designing, instrumentation, execution, reporting, and maintenance of testing activities with minimal human intervention."
In autonomous testing, once the application under test (AUT) and the testing goals are defined by the user, the autonomous platform takes over:
- Analyzes the application behavior and requirements
- Automatically develops optimal test scenarios and test data
- Executes end-to-end testing unattended
- Continuously adapts to changes in the AUT to heal and optimize the tests
- Generates comprehensive analytical reports for testers and developers
This creates a hands-free testing process that mimics the intuition and context-driven choices of a seasoned QA expert.
Forrester Research predicts that by 2025, half of all large organizations globally will be using autonomous testing, up from less than 20% in 2020.
Projected adoption of autonomous testing through 2025 (Source: Forrester)
What factors are driving this rapid growth? Let‘s examine the key benefits autonomous testing provides.
Key Benefits of Autonomous Testing
1. Boosts QA productivity by over 50%
A recent Omdia survey of customers using autonomous testing found that:
- Manual test creation effort reduced by 65%
- Test maintenance productivity increased by 53%
- Time to identify defects decreased by 48%
By automating repetitive tasks like test upkeep and failure triage, autonomous testing enables QA professionals to focus on high-value work like exploratory testing.
2. Accelerates time to market
Autonomous testing helps release higher quality software faster through:
- Parallel test execution: Tests can run 24/7 on multiple browsers and platforms simultaneously.
- Early feedback: Issues caught earlier in the release cycle.
- No wait times: Instantly generate tests for rapid iterations without manual effort.
A case study of SOASTA and Carousell showed autonomous testing reduced their release cycles from 2 weeks to 5 days – a 70% acceleration.
3. Lowers cost of quality
According to the World Quality Report 2021, organizations spend over 35% of their QA budget on test environments and infrastructure.
Autonomous testing optimizes usage of test data and environments, reducing overhead. The Omdia survey showed users achieved hard cost savings of b/w $25,000 to $100,000 through autonomous testing.
4. Increases test coverage
A case study by Tricentis and Net Baltic showed that autonomous testing achieved 90% test automation coverage for the enterprise software company compared to 20% earlier.
Higher coverage is achieved due to automatic test generation across expanded scenarios, negative paths and data combinations. This results in higher defect detection.
5. Adapts seamlessly to app changes
Modern apps evolve rapidly but manual test maintenance struggles to keep pace. 72% of test automation projects fail due to lack of test upkeep according to Capgemini.
Autonomous testing overcomes this using AI to track application changes and automatically update, heal, and optimize test cases without any human intervention.
As we‘ve seen, users are achieving major measurable gains across critical testing metrics through autonomous testing. But where exactly is this emerging technology most applicable?
Top Use Cases for Autonomous Testing
While autonomous testing platforms aim to automate all test activities over time, the following use cases currently see maximum benefit:
1. Customer journey testing
Testing end-to-end user journeys across web and mobile apps involving hundreds of components is very challenging manually. Autonomous testing automatically generates the needed test cases and data covering happy paths, edge cases, and negative scenarios across frontend and backend systems.
2. API testing
Exhaustively testing API contracts, schemas, security, performance, etc. manually requires huge effort. AI-driven API testing tools like AutonomIQ can automatically generate and execute thousands of test cases.
3. Responding to production defects
When a defect occurs in production, autonomous testing tools can instantly analyze the root cause and create a regression test suite to prevent recurrence. This minimizes escape of defects to customers.
4. Functional regression testing
Rerunning hundreds of functional test cases with every code change is time-consuming and repetitive. Autonomous testing tracks code changes to automatically select and execute only the relevant test cases.
5. Load and performance testing
Performance testing that simulates thousands of concurrent users under different conditions can be hands-free using autonomous load generators like NeoLoad.
6. Localization testing
Validating software behavior across multiple languages and locales requires substantial test maintenance effort. Autonomous testing platforms automatically update tests for each locale as the UI is localized.
These examples demonstrate the power of autonomous testing for multiplier test scenarios that are highly complex and dynamic. Next, let‘s examine how these powerful AI capabilities actually work behind the scenes.
AI and ML Techniques Behind Autonomous Testing
Today‘s autonomous testing platforms utilize cutting-edge techniques like:
-
Computer vision (CV): To identify UI components and interactions for creating automated UI tests without manual scripting.
-
Natural language processing (NLP): To auto-generate meaningful test scenarios and data by analyzing requirements documents.
-
Analytics and ML: To select priority test cases based on code change risk analysis and past defect patterns.
-
Virtual user technology: To simulate real-world user journeys and system loads at unlimited scale.
-
Multi-layered feedback: To continuously improve testing relevance based on production monitoring, user reviews, and tester feedback.
These technologies enable autonomous testing to mimic human-like intuition for creative test design and context-driven decision making.
Let‘s look at how CV and NLP techniques work:
Computer Vision
CV analyzes the application screens and automatically identifies testable components on the user interface without requiring element selectors:
Computer vision identifying testable UI components (Image source: uTest)
By continuously scanning the app, the CV engine "sees" what components like buttons, fields, menus etc. are present on each screen. This allows it to develop automated tests for the UI.
Natural Language Processing
NLP techniques like semantic analysis are used to interpret software requirements, user stories, and spec documents.
NLP generating test cases from requirements
Key test scenarios are automatically identified from the text and translated into executable test cases without human coding.
Together, these AI capabilities enable autonomous testing to automate increasingly complex test processes.
Challenges of Adopting Autonomous Testing
While the benefits are compelling, organizations need to be aware of the following limitations and challenges with autonomous testing:
- Automation bias: Tendency to overly trust automated tests versus human insight for identifying edge cases.
- Skill gap: Lack of internal skills in AI/ML and adaptability of existing QA teams.
- Toolchain integration: Integration with DevOps pipelines and processes may require additional effort.
- Lack of explainability: Inability to understand the logic behind test failures driven by the AI.
- Data dependence: Large volumes of high-quality test data needed for AI algorithms to be effective.
- LAcompatibility with automation frameworks: Challenges in combining existing test automation frameworks with newer autonomous testing tools.
A measured approach focused on building in-house competencies through training, hiring specialists, and developing Centers of Excellence can help maximize the results from autonomous testing investments.
Now that we‘ve covered capabilities, use cases as well as challenges, how does autonomous testing compare with traditional test automation approaches?
Autonomous Testing vs Test Automation
While automation through scripted tests is valuable, it has certain limitations that autonomous testing aims to overcome:
| Test Automation | Autonomous Testing |
|---|---|
| Test cases require manual design | AI automatically designs optimal test cases |
| Brittle tests break with app changes | Self-healing ensures tests adapt to changes |
| Needs skilled programmers to code scripts | No specialized coding skills required |
| Static test execution based on scripts | Intelligent exploratory and contextual testing |
| Manual failure analysis and reporting | Automated root cause analysis and insightful reports |
| High maintenance needs | Low maintenance through auto-optimization |
The key differentiator from traditional test automation is the use of AI/ML to mimic human-like intellect for dynamic test creation and smart test maintenance.
The Road Ahead
According to the World Quality Report 2022, over 50% of organizations plan to increase their investment in intelligent test automation over the next 1-2 years.
Leading analyst firms like Gartner and Forrester identify autonomous testing as one of the top technology trends that will shape the future of software quality.
As solutions mature, adoption is expected to grow significantly. The worldwide autonomous testing tools market is projected to expand at a CAGR of 28% to reach $500 million by 2022 according to IDC.
Conclusion
This brings us to the end of our in-depth exploration of autonomous testing. The key takeaways are:
-
Autonomous testing aims to leverage AI/ML to automate test design and maintenance – reducing human effort.
-
It helps achieve productivity gains, faster release cycles, improved quality, and cost savings as evidenced by multiple case studies.
-
Applicable for complex test scenarios like customer journeys, APIs, localization testing which are difficult to automate manually.
-
Adoption is accelerating driven by advanced visual, language, and analytics capabilities of these emerging tools.
-
Challenges remain around trust, skill gaps, integration and data needs – requiring a managed approach.
Autonomous testing with human QA expertise is a powerful combination that has the potential to transform software testing. Organizations need to actively evaluate solutions as the offerings mature.
I hope you enjoyed this comprehensive analysis. Please feel free to reach out for any other insights on intelligent test automation.
