How do you leverage AI or machine learning to enhance test automation in agile web testing?

A test plan should be an ever evolving living document. Iterated upon as new things are discovered about the product under test. It should serve as a reference guide to ensure that the objectives identified from the outset and discovered throughput development have been met and considered appropriately. Any risks identified should be considered with an appropriate mitigation.

The primary test approach that we can focus on is using AI in testing. 1) Test Data Generation: There were multiple phases involved in the creation of test data. A) Remove null data from the dataset. B) Divided the data into test and sample forms (20:80). 2) Choose the Appropriate ML Method: Depending on the type of data, use either an unsupervised or supervised machine learning algorithm. 3) Provide the required resources and a data-driven strategy: Collect, clean, and reuse it. 4) To combine AI/ML with test automation, use Cypress, API Mockup, and scriptless automation. 5) Tracking results and improving skills: Keep an eye on the cloud at all times, collect data, and deal with issues.

They can enhance by automating test case generation, predicting areas prone to defects, optimizing test suite execution, and improving test coverage through adaptive test strategies. These technologies can also analyze test results to identify patterns, anomalies, and areas for improvement, enabling teams to focus on high-impact testing activities. Additionally, AI can help in reducing maintenance efforts by identifying redundant or obsolete test cases, and in enhancing the robustness of tests by learning from past test outcomes. Furthermore, AI-powered tools can provide intelligent recommendations for test scenarios, configurations, and data sets, improving overall testing efficiency and effectiveness.

To enhance test automation in agile web testing with AI and machine learning, leverage AI-driven tools for test case generation, maintenance, and execution. AI can analyze historical test data to identify patterns and generate new test cases, reducing manual effort. Machine learning algorithms can predict high-risk areas of the application, optimizing test coverage. Use AI for visual testing to detect UI changes and anomalies that traditional methods might miss. Additionally, AI can help with test data generation by creating realistic data sets and handling dynamic content. Integrate these AI-driven insights into your CI/CD pipelines to continuously adapt and improve test strategies.

Automation effectiveness is what I evaluated in my experience. We can create N number of user scenarios, but in terms of ROI, if we need to make sure to cover effectiveness %? Automation Script Effectiveness = (no. of defects found by automation / no. of defects opened) * 100 Second, we need to manage past production bugs in our automation suite so that they won't miss our test strategy. Third, improve execution time by avoiding unnecessary waits and using a better way to handle Try Catch, Prefer Using Primitive Data Type to make your suite faster, follow Performance Scalable and Reliable methods Use effective libraries, i.e., FileInput Read/Write Owner Library, Lombok for Data, etc.

Automating repeated tasks is crucial and benefial in the long run.. Planning to focus on complex scearios with manual testing teams ia essential. Optimize the percentage mix of manual and automation efforts. Regression for past changes which are not impacted by new scope can be automated. Automation in sprint is very tough given the frequency of scope changes.

It is important to assess what value your automation brings. Blindly automating everything is is inefficient and uneconomical.

AI-driven automation can intelligently identify which test cases to automate based on frequency, criticality, and impact. By leveraging machine learning algorithms, you can optimize your automation suite, focusing on high-value test scenarios while reducing maintenance efforts and costs associated with unnecessary automation.

AI transforms agile web testing by generating user-driven test cases, swiftly analyzing results for defect resolution, and optimizing coverage based on risk. Its adaptability eases script maintenance in changing environments, and its learning capability enhances efficiency over time. In agile settings, AI automates tasks, allowing focus on strategic testing. Its role in load testing provides insights into diverse performance conditions, making it indispensable for ensuring web application reliability and quality."

Numerous tools will be available to increase team productivity, allowing us to optimize our regression testing time investment. To start, we can create edge situations using a test case generator. As a second option, we can alter the data and generate sample data and test data. Use regression and Classification methods for Supervised learning and Clustering and Dimension reduction for Unsupervised learning. Third option - AI-powered computer vision and natural language processing detect UI problems and classify their severity and impact on customers Fourth: AI can predict the outcome of test automation, the density of defects, and the duration of test case execution. Both maintenance costs and time will decrease as a result.

AI can analyze the user behavior and feedback to automatically generate relevant test datasets, ensuring comprehensive test coverage. It is multi-folded and AI can: >Test scripts and scenarios to accommodate changing requirements and environments, ensuring tests remain relevant and effective. >Quickly analyze test results to identify defects, inconsistencies, and RC's(root causes), speeding up the debugging process. >Adapt test scripts & scenarios to accommodate changing requirements(fragileness - fast moving targets) and env's, ensuring tests remain most applicable and effective. >Learn from previous test runs, enhancing test efficiency and effectiveness over time by identifying patterns and improving future test cycles.

For Agile projects, changing Business scope and requirements is really challenging for testing teams. This would cause impact efforts and timelines, overall quality. Sprint testing and Regression execution is difficult to maintain.

Testing 'early n often' in agile env ensures higher quality builds, better alignment with user needs, and more efficient resource use, leading to a more successful product. Starting testing as soon as development begins helps catch defects early, making them easier and less costly to fix. Continuous testing throughout the cycle identifies & addresses issues promptly, providing regular feedback and iterative improvements. This enhances collaboration between engg teams, QA, & Business users/stakeholders. Additionally, incorporating AI/ML fosters in test automation, there by increasing testing speed & accuracy, allowing teams to focus on complex/edge cases and achieve faster delivery times.

Integrating AI into early testing phases enables proactive defect detection and resolution. Machine learning algorithms can analyze code changes, identify areas of potential risk, and prioritize testing efforts accordingly. By fostering a culture of continuous testing and feedback, AI-driven solutions empower teams to detect and address issues promptly, ensuring alignment with agile principles and delivering high-quality software.

Collaboration is critical in Agile web testing for assuring product quality and value. I understand the value of collaborating closely with the team and stakeholders to achieve common objectives. 1)Integrate with Agile tools: I can smoothly use tools like Jira and Trello, allowing the team to access and update. 2)Real-time defect reporting by filtering customer feedback responses on app utilization. 3)Enable collaborative test data management by allowing team members to share and reuse test data, reducing duplication of effort and increasing test coverage. 4) Facilitate cross-functional communication between developers, product owners, business analysts, and testers to ensure alignment on testing goals, objectives, and schedules.