Leveraging AI into your QA practice

For software development to be successful, it is crucial to perform robust software testing along the development cycle. In the era where the market changes within the blink of an eye, and enhancing customer experience is the first thought, finding a way to shorten development cycles and reach market faster has been exorbitantly challenging for businesses.

Quality assurance or QA is a way to save you from the hustle of launching inefficient software. It helps you to continuously run tests during the software development process and rectify any signs of software bottlenecks like fixing security patches if any, reducing software downtime, and much more. However, performing robust testing could be a time-consuming procedure, and reaching the market faster could seem challenging. Therefore, AI or artificial intelligence testing could be your groundbreaking solution.

Unlike traditional QA practices, leveraging AI for testing can help you enhance your QA strategy. It is a collaboration of test automation and intelligence that can help you achieve agility and deliver an improvised digital customer experience through flawless software.

AI encompasses a broad range of features and capabilities like Predictive analysis, Continuous Learning, Computer Vision, NLP, Pattern recognition, Generative models, and automated planning, all of which are used across different AI testing tools in the market today to enhance QA practices.

Let's explore in the blog the features that AI has integrated into QA testing.

Four features of artificial intelligence testing for improvised test results

1. Adaptive test scripting

Adaptive test scripting refers to the ability of a testing framework or tool to dynamically adjust test scripts based on changes in the application's user interface or functionality. The goal is to create more robust and flexible test scripts that can adapt to modifications in the application without requiring extensive manual updates.

Some adaptive testing tools leverage machine learning and artificial intelligence to understand the relationships between different elements on the page. These testing tools don't just follow the steps; they learn the rhythm of your application, autonomously adapting and improvising like seasoned performers on a digital stage.

• Dynamic element identification: Adaptive test scripting often involves using techniques that dynamically identify UI elements during runtime. Instead of relying on fixed locators (e.g., XPath or CSS selectors), the testing tool can employ algorithms to recognize and locate elements based on their visual properties or other dynamic attributes.
• Smart wait mechanisms: Adaptive testing tools use smart wait mechanisms to handle dynamic loading of elements. These mechanisms ensure that the test script waits for an element to be ready before interacting with it, reducing the likelihood of test failures due to element unavailability.
• Self-healing tests: Self-healing tests are a key aspect of adaptive test scripting. When a test encounters a failure due to a changed element or UI modification, the testing tool attempts to automatically correct the script by adjusting the locator or adapting the interaction steps.
• Parameterization and data-driven testing: Adaptive test scripts often utilize parameterization and data-driven testing techniques. By separating test data from the test script, these approaches make it easier to update input values without modifying the core test logic, promoting adaptability.

2. Visualized AI testing

Visualized AI testing is another excellent addition to the capabilities of AI when included in your testing practices. AI Visualized testing can analyze the visual appearance of application interfaces, identify graphical elements, and detect any visual discrepancies that may indicate defects.

• Element identification: Visual AI tools begin by identifying and understanding the graphical elements on a web page or application interface. These elements include buttons, text fields, images, and other UI components.
• DOM (Document Object Model) analysis: Visual AI tools often analyze the DOM, which represents the structure of a web page in a tree-like format. By examining the entire DOM, Visual AI gains insights into the hierarchy and relationships between different elements.
• Automated test execution: Test scripts are executed, and as the application evolves, Visual AI dynamically adjusts to changes in the visual representation of elements. This adaptability is crucial for handling dynamic content and responsive design.
• Visual comparison: During the initial test run, a baseline image of the application's visual state is captured. This baseline serves as the expected reference point for future comparisons. During subsequent test runs, Visual AI captures new images of the application screens. These images are then compared pixel by pixel with the baseline image to identify any differences.

Visual AI uses intelligent algorithms for visual differencing. It can filter out expected changes, such as those caused by intentional design updates, and focus on potential defects or unintended alterations in the visual appearance.

• Adaptation to dynamic content: Visual AI is designed to adapt to dynamic content by recognizing and learning from changes over time. This adaptability reduces false positives and ensures that the testing process remains effective in dynamic application environments.

3. Test case and test steps generation

Gone are the days where a QA had to spend hours to create columns and rows and describe each step and test case to be implemented. Natural Language Processing (NLP) is a branch within artificial intelligence that capitalizes on its capabilities to automatically generate test cases and test steps. This is achieved by inputting the tool with project requirements and acceptance criteria, leveraging NLP's language understanding and generation capabilities to produce detailed and contextually relevant testing scenarios.

• Natural Language Understanding (NLU): NLU involves the development and application of algorithms and models to extract semantic meaning, context, and intent from unstructured natural language data. This process encompasses syntactic and semantic analysis, entity recognition, sentiment analysis, and the creation of representations that capture the relationships and nuances within the language.

NLU algorithms leverage statistical methods, machine learning models, and neural network architectures to discern the subtleties of human expression, enabling machines to derive actionable insights, respond appropriately, and perform tasks based on the parsed and understood linguistic input.

• Pattern recognition: Pattern recognition in AI refers to the automated discovery of regularities, structures, or features within datasets. This involves the use of algorithms, statistical methods, and machine learning techniques to identify patterns in data, enabling systems to make predictions, classifications, or anomaly detections. This helps in keeping the consistency and context of the output based on the input.
• Context retention: ChatGPT uses its Transformer architecture to retain context over a conversation or prompt. This allows it to refer to the original description and acceptance criteria while generating test cases and steps.

4. AI-powered reporting

AI-powered reporting in AI-based quality testing refers to the use of artificial intelligence (AI) to analyze and present test results in a more informative and actionable way. It goes beyond simply reporting pass/fail results, but instead provides insights into the why and how of test failures, trends in test results, and recommendations for improvement.

• Defect prediction: Based on the data reported, machine learning algorithms can analyze historical data to predict potential defects or areas of concern in the application. This proactive approach allows QA teams to focus their efforts on critical areas.
• Test case prioritization: With the data processed during reporting, AI can assist in prioritizing test cases based on their likelihood to uncover critical issues. This helps QA teams optimize their testing efforts by focusing on high-impact areas first.
• Continuous monitoring: AI can continuously monitor the application's performance and behavior in real-time. Any deviations from expected norms trigger alerts, enabling quick responses to potential issues.
• Predictive analytics: AI can leverage historical data to provide predictive analytics, estimating future defects or areas that might require additional testing attention.
• Enhanced decision support: AI-powered reporting provides actionable insights, aiding QA teams and decision-makers in making informed choices about the application's quality and release readiness.

Why choose Altudo as your QA testing partner?

A QA service provider like Altudo helps you with expertise in streamlining testing processes and delivering tailored solutions for enhanced software quality. This strategic collaboration enables businesses to focus on core operations while ensuring their software meets high standards and user expectations.

Here’s how Altudo can help you in your QA testing journey:

• Accelerated testing cycles – We expedite the software testing process, enabling quicker product releases.
• Cost-efficiency – Reduce expenses while elevating software quality. Our services minimize testing costs by automating repetitive tasks and reducing reliance on manual testing.
• Automated cross-browser testing – Our QA engineers swiftly conduct tests across various browsers, operating systems, and devices, ensuring seamless cross-browser compatibility. This translates to fewer customer support tickets and an enhanced user experience.
• Enhanced test coverage – Leveraging strategic and customized test automation frameworks, we achieve extensive test coverage. This not only boosts software efficiency but also mitigates errors.
• Effortless test case execution – With extensive experience in software testing, we provide tailored automation testing services aligned with your business's unique requirements, ultimately enhancing software quality.

Conclusion

The integration of AI into QA is not just a technological trend, it's a paradigm shift. By embracing AI-powered tools and techniques, QA teams can unlock unprecedented levels of efficiency, accuracy, and insight, ultimately delivering software that consistently exceeds user expectations. It empowers you to move beyond the regular automation testing techniques and embrace enhanced results and focus on strategic problem solving.

To avoid the roadblocks in this transformative journey, Altudo stands out as a reliable QA service partner, from offering accelerated testing cycles to effortless test case execution. Partnering with Altudo ensures that businesses can navigate the complexities of software testing with confidence, delivering high-quality products that meet the expectations of both users and the market.

To learn more about our QA services, write to us at marketing@altudo.co and schedule a 1:1 no-obligation session with our in-house QA experts.