AI in Quality Assurance: Shifting From Manual to Autonomous Testing
Artificial intelligence (AI) has revolutionized the way we do quality assurance (QA). The immense value that AI brings to optimizing testing processes and enhancing efficiency simply can’t be ignored. Organizations that know how to incorporate AI into their testing will gain a strong competitive edge against their competitors. This article explores in-depth the power of AI in quality assurance and how QA teams can shift from unscalable manual testing to cutting-edge autonomous testing technologies.
Introduction to AI in Quality Assurance
AI can perform much more advanced tasks that previously required human cognitive powers, most notably:
- Natural language processing (NLP) – It can understand, interpret, and even respond to human language while taking linguistic nuances into account. For QA, this means AI can read and understand user requirements written in plain language and transform them into test cases or even automation scripts.
- Learn and improve – Machine learning (ML, a subset of AI) enables the AI system to independently learn from its experience without having to be explicitly programmed. QA teams can train the AI through their testing sessions and over time it’ll pick up their testing patterns and fine-tune its recommendations to be more organization-specific.
- Computer vision – It can also analyze and interpret visual data, detecting anomalies in the user interface (UI). For QA teams, this means more accurate visual regression testing.
Incorporating AI into the QA process takes us one step closer toward the autonomous testing future.
The software testing industry has gradually shifted from Test Automation to Autonomous Software Testing (AUST). This shift was accelerated by the introduction of many groundbreaking AI technologies like ChatGPT. By “autonomous”, we envision a future where tests are completely created, driven, and managed by AI/ML, eliminating the need for human intervention.
Read More: Autonomous Testing: A Complete Guide
Taking a look at the Autonomous Software Testing Model (ASTM) above, you will see that Manual Testing ranks at the lowest position in terms of autonomy, simply because it is 100% driven by humans. Further up the model, we see Automation Testing slowly offloading the repetitive tasks, freeing up the bandwidth of testers so that they can focus on more strategic tasks and high-level planning. Autonomous Testing is the final stage, where computers have full control over the testing process.
Shifting from manual testing to automated testing, then from automated testing to autonomous testing bring a wide array of benefits:
- Drastically improves efficiency and speed
- Cost-saving while increasing ROI
- Ability to handle large volume of test cases, leading to increased test coverage
- High scalability
- Resource optimization
- Opportunity to collect more data to perform predictive analytics
Benefits of AI-Led Quality Assurance
More than just the implementation of AI into the QA process, AI-led QA is when you leverage AI to automate, optimize, and even guide the testing activities. AI-enabled test techniques can be incorporated into any stage of the Software Testing Life Cycle (STLC), from test case creation to defect detection and analysis. This approach is also known as AI testing.
1. Effortless Test Authoring
ChatGPT, a groundbreaking AI recently released by OpenAI, possesses remarkable language capabilities, and has an impressive IQ of 155. QA teams have leveraged its power to achieve “effortless test authoring,” where they simply give the chatbot a detailed instruction on the test case they want to generate and it gives them exactly that, in the programming language they choose. Although there is still room for improvement, chatbots can boost the speed of test authoring in many ways, such as:
- Interpret natural language instructions and turn them into test scripts
- Create test templates for common testing scenarios which testers can quickly adapt into specific testing requirements
- Generate test data based on specific inputs
Read More: The Potential of AI in Regression Testing
2. Improved Consistency
With human testers, there is always the risk of human error. Either due to fatigue, distraction, perception errors, or even cognitive bias, testers may venture off the standardized path at some point. This is a particularly common issue in manual testing where testers execute test cases and interpret test results in their own way, leading to inconsistency in the process.
With AI in quality assurance, teams can establish a more standardized process. For example, you can have an AI system that automatically scans through the code to identify inconsistencies in your design. AI can also be trained to follow standardized formats and coding best practices so that test script generation becomes more rule-based.
Another interesting application of AI in quality assurance is documentation. You can instruct AI to examine the code, then extract essential details like function and variable names, descriptions, and usage examples. With this information, it can produce comprehensive reports that are user-friendly, saving valuable time and effort that would otherwise be spent on manually constructing the required documentation.
3. AI as a Helpful Assistant
Instead of spending hours scrolling through resources on the internet, testers can now jump straight to ChatGPT to find the answers for any questions that they have. Although concerns have been raised about the validity of these answers, they do offer a fairly decent starting point to further understand the topic at hand. It can save testers a lot of time spent scouring Stack Overflow.
The Limitations of Manual Testing and How AI Can Change That
The goal of implementing AI in quality assurance is to overcome the bottlenecks in manual testing. Manual testing is a time-consuming process that is prone to human error. It requires significant effort to write, manage, execute test cases, document the outcomes, and verify results. As software complexity grows, scaling manual testing becomes much more challenging, leading to prolonged testing periods.
Moreover, manual testing can cause delays in identifying and resolving bugs, especially during major software updates or releases. When performed manually, regression testing, which is essential for software maintenance, becomes tedious and counterproductive. The hidden costs associated with training new testers and managing testing environments are also often overlooked.
Automation testing accelerates the process by streamlining repetitive and resource-intensive tasks. As we move closer to autonomous testing, even the common challenges of automation testing can also be addressed.
Understanding Autonomous Testing with AI
AI is a crucial part of autonomous testing because we want the system to make decisions and evolve on its own, with minimal (and ideally zero) instructions from human testers. A truly autonomous system is a self-governing system that can test other systems intelligently. This intelligence is what separates autonomous testing from automation testing.
Let’s explore the concept of “intelligence” more in-depth. Humans are considered “intelligent” because we can make non-deterministic decisions based on existing information and experience, taking into account a large number of complex underlying factors. Similarly, AI can recognize patterns from billions of data points that they have been trained on to arrive at a final decision.
Without the power of AI, we can only perform automation testing at best. Automation testing generates deterministic results (i.e., the outcome is entirely predictable with the same initial inputs). When we execute a test script for the scenario “Typing valid credentials in the Login feature,” we know exactly the possible results this script will return. In this scenario, we don’t really test, we can only check the system.
With the power of AI, we can expect the system to perform more non-deterministic activities (i.e., the outcome is not entirely predictable with the same initial inputs). For example, we can ask the AI to generate a CSV file consisting of 100 data points with username, password, and email fields that we can use to test the Login feature. The system is now capable of doing much more complex tasks to arrive at results not entirely under our control.
Currently, QA teams have already leveraged AI in their quality assurance process to achieve many exciting feats:
- Analyze application behavior, requirements, and historical data to automatically generate test cases, covering various scenarios and edge cases.
- Prioritize test cases based on factors like code changes, risk analysis, and historical defect data.
- Dynamically update test suites based on changing requirements and application updates.
- Detect and correct test script failures (self-healing mechanisms).
- Suggest ideas of manual test cases based on existing test plan.
- Continuously learn from and adapt to the system’s testing patterns.
The Future of Autonomous Testing and AI in QA
The software testing industry is heading towards autonomy, gradually pushing automation testing into the past. Automation testing used to be the innovative approach, but autonomous testing is what we should truly look towards.
Currently, AI-driven autonomous testing is still only in its infancy, but we can expect its development to follow a continuous upward trend in the near future. AI technology requires time to be fully trained and integrated into an organization’s existing system, so it will be a while before companies can fully enjoy the benefits of AI-driven quality assurance. Several interesting potential developments include:
- AI-driven test case suggestion and authoring: As the AI learns about the specific testing needs of the organization, it can make test case suggestions based on those requirements, and if needed, generate the test script by themselves. Of course, to achieve this, the team must continuously feed the AI with huge numbers of testing sessions for it to learn.
- Autonomous test orchestration: If the AI is granted access to real-time data on the organization’s testing resources, it will be able to manage end-to-end testing processes, automate test scheduling based on current available resources, and make decisions for test execution in complex, distributed environments.
- Test environment setup: Based on data from system performance, configuration requirements, and the test case suggestions that it generates itself, the AI will take over the provisioning of the test environment, streamlining the setup process.
- Cognitive test exploration: Previously handled by humans due to its highly spontaneous nature, AI in the future may be able to perform intelligent exploratory testing and identify areas that need attention. It can make these decisions based on usage patterns, business priorities, and user behavior.
- AI-powered visual testing: As automation test scripts may run into the problem of reporting false positives, AI-powered visual testing will do a better job at detecting true visual bugs that affect the user.
The rapidly evolving landscape of the QA industry demands a learning mindset. The methods and tools we rely on today may swiftly become outdated, rendering our expertise obsolete. Testers must do more than just testing — now they must know how to leverage cutting-edge technologies to transform their entire testing process. The result would be a significant competitive advantage in an increasingly crowded marketplace.
Start Your AI Testing Journey with Katalon
Katalon Platform is a modern, AI-augmented test automation and quality management platform for web, mobile, API, and desktop applications. It provides a unified platform for teams to plan, design, execute, and manage automated testing efforts. Most importantly, Katalon is the pioneer in the AI testing space with a lot of exciting features that transform the way we imagine testing:
- ATG (Katalon Autonomous Test Generation): Automatically creates and updates test cases, reducing the time and effort required by manual creation and maintenance (in Beta).
- StudioAssist: Autonomously generate test scripts from a plain language input. You can also select a snippet of code and choose Explain Code to quickly comment on it for all stakeholders and team members to understand.
- Katalon GPT-powered manual test case generator: Integrates with JIRA, reads the ticket’s description, extracts relevant information about software testing requirements, and outputs a set of comprehensive manual test cases tailored to the described test scenario.
- SmartWait: Automatically waits until all necessary elements are present on screen before continuing with the test steps.
- Self-healing: Automatically fixes broken element locators and uses the new locators in subsequent test runs, reducing maintenance overhead.
- Visual testing: Indicates if a screenshot is taken during test execution using Katalon Studio, then assesses the outcomes using Katalon TestOps. AI is used to identify significant alterations in UI layout and text content, minimizing false positive results and focusing on meaningful changes for human users.
- AI-powered test failure analysis: Automatically classifies failed test cases based on the underlying cause and suggests appropriate actions.
- Test flakiness: Understands the pattern of status changes from a test execution history and calculates the test's flakiness.
- Image locator for web and mobile app tests (Katalon Studio): Finds UI elements based on their visual appearance instead of relying on object attributes.
- Web service anomalies detection (TestOps): Identifies APIs with abnormal performance.
As one of the pioneers in the use of AI in software testing, Katalon continues to add more exciting AI-powered features to their product portfolio, empowering QA teams around the world to test with unparalleled accuracy and efficiency.