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Last updated on 2024年6月26日

How can AI and ML help you design better exploratory testing scenarios?

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Exploratory testing is a creative and dynamic approach to software testing that involves learning, designing, and executing tests based on the tester's observations and insights. It can help uncover unexpected bugs, usability issues, and user feedback that might not be captured by scripted or automated tests. However, exploratory testing also has some challenges, such as time constraints, documentation, coverage, and consistency. How can AI and ML help you design better exploratory testing scenarios? In this article, we will explore some of the ways that AI and ML can enhance your exploratory testing skills and outcomes.

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Dev Narayan

Sr.Manager - Engineering at testRigor (Helping companies empower manual QA to build automation with AI)

1 AI and ML for test generation

One of the ways that AI and ML can help you design better exploratory testing scenarios is by generating test cases based on the application's features, functionality, and behavior. AI and ML can analyze the application's code, user interface, data, and user actions to create test scenarios that cover different paths, inputs, outputs, and edge cases. These test scenarios can serve as a starting point for your exploratory testing sessions, or as a supplement to your existing test cases. You can also use AI and ML to generate test data, such as realistic user profiles, transactions, or documents, that can help you simulate different scenarios and conditions.

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2 AI and ML for test prioritization

Another way that AI and ML can help you design better exploratory testing scenarios is by prioritizing the most important and relevant tests based on the application's context, risks, and changes. AI and ML can use historical data, user feedback, bug reports, code analysis, and other sources to rank the test scenarios according to their impact, likelihood, severity, and urgency. This can help you focus your exploratory testing efforts on the areas that matter the most, and avoid wasting time on low-value or redundant tests. You can also use AI and ML to monitor the application's performance, quality, and user behavior, and adjust your test priorities accordingly.

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Ramdinesh Boopalan

SAFe | DevOps | Secure Development | Cloud Services | Cyber Security | Infra Management | Technical Writer | AI/ML|
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AI and ML help design better exploratory testing scenarios by prioritizing tests based on risk and impact. By analyzing historical testing data, ML algorithms can identify patterns of defects and prioritize test cases accordingly. For instance, ML can assess factors like code complexity, frequency of changes, and past defect occurrences to determine which areas of the application are most critical. This prioritization ensures that exploratory testing focuses on high-risk areas, maximizing test coverage and effectiveness while optimizing resource utilization.

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3 AI and ML for test execution

A third way that AI and ML can help you design better exploratory testing scenarios is by executing the tests for you, or assisting you during the test execution. AI and ML can use natural language processing, computer vision, speech recognition, and other techniques to interact with the application as a human user would, and verify the expected outcomes. AI and ML can also provide you with real-time feedback, suggestions, and alerts during your exploratory testing sessions, such as highlighting potential defects, anomalies, or improvements. You can use AI and ML to automate some of the repetitive or tedious tasks, such as setting up the test environment, logging the test results, or reporting the issues.

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Dev Narayan

Sr.Manager - Engineering at testRigor (Helping companies empower manual QA to build automation with AI)
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You should choose powerful test automation tools that can leverage AI and ML to execute exploratory tests. There are some fantastic tools available that make documenting exploratory tests easy (in plain English language) and run them for you. A good tool will be able to do your bidding when it comes to UI testing and executing other common scenarios that are seen in apps these days, like 2FA or manipulating table data. Try demos of such tools before settling on one.

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Ramdinesh Boopalan

SAFe | DevOps | Secure Development | Cloud Services | Cyber Security | Infra Management | Technical Writer | AI/ML|
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AI and ML enhance exploratory testing by automating test execution. ML algorithms can learn from historical testing data and user interactions to generate and execute test scenarios autonomously. For instance, AI-powered bots can explore the application dynamically, mimicking user behavior and identifying potential issues. ML algorithms can also predict areas prone to defects, guiding testers to focus on critical functionalities. This accelerates testing cycles, improves coverage, and more efficiently identifies defects, enhancing the software's overall quality.

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4 AI and ML for test learning

A fourth way that AI and ML can help you design better exploratory testing scenarios is by helping you learn from the test results, and improve your testing skills and knowledge. AI and ML can use data mining, machine learning, and deep learning to analyze the test results, and identify patterns, trends, correlations, and root causes. AI and ML can also use natural language generation, data visualization, and recommender systems to present the test findings in a clear, concise, and actionable way. You can use AI and ML to learn from the test feedback, and refine your test strategies, techniques, and heuristics.

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Ramdinesh Boopalan

SAFe | DevOps | Secure Development | Cloud Services | Cyber Security | Infra Management | Technical Writer | AI/ML|
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AI and ML facilitate test learning and assist in designing improved exploratory testing scenarios. Through ML algorithms, testing systems can analyze past testing outcomes, user interactions, and application behavior to identify patterns and learn from them. For instance, AI can recognize common user paths or features prone to defects, suggesting areas for testers to focus on during exploration. By continuously learning from testing experiences, AI optimizes the selection of test scenarios, enhancing the effectiveness and efficiency of exploratory testing efforts.

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5 AI and ML for test collaboration

A fifth way that AI and ML can help you design better exploratory testing scenarios is by facilitating your collaboration with other testers, developers, and stakeholders. AI and ML can use chatbots, voice assistants, and social media platforms to communicate with you and your team members, and provide you with relevant information, guidance, and support. AI and ML can also use cloud computing, blockchain, and smart contracts to enable you to share your test scenarios, data, results, and feedback with others, and ensure the security, integrity, and transparency of your testing process. You can use AI and ML to collaborate with your peers, and leverage their expertise, experience, and perspectives.

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Ramdinesh Boopalan

SAFe | DevOps | Secure Development | Cloud Services | Cyber Security | Infra Management | Technical Writer | AI/ML|
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AI and ML aid in designing enhanced exploratory testing scenarios by facilitating test collaboration. AI can analyze testers' past interactions, preferences, and expertise through collaborative filtering algorithms to recommend relevant test scenarios. For instance, AI-powered platforms can suggest test cases based on similarities with previous successful tests conducted by other team members. By promoting collaboration and knowledge sharing, AI optimizes test scenario design, ensuring comprehensive coverage and improved testing outcomes in exploratory testing efforts.

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6 AI and ML for test innovation

A sixth way that AI and ML can help you design better exploratory testing scenarios is by inspiring you to innovate and experiment with new testing ideas, methods, and tools. AI and ML can use generative adversarial networks, reinforcement learning, and evolutionary algorithms to create novel and challenging test scenarios that can push the boundaries of your application's functionality, usability, and reliability. AI and ML can also use gamification, virtual reality, and augmented reality to make your exploratory testing sessions more fun, engaging, and immersive. You can use AI and ML to innovate and experiment with your testing approach, and discover new possibilities and opportunities.

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Ramdinesh Boopalan

SAFe | DevOps | Secure Development | Cloud Services | Cyber Security | Infra Management | Technical Writer | AI/ML|
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AI and ML drive innovation in exploratory testing by leveraging advanced algorithms to discover new and creative test scenarios. Through techniques like genetic algorithms or reinforcement learning, AI can autonomously generate novel test cases based on evolving criteria and feedback. For example, AI can dynamically adjust test scenarios based on real-time application performance data, identifying unique paths or edge cases for exploration. This fosters innovation by continuously pushing testing boundaries, uncovering unforeseen issues, and improving overall test coverage and effectiveness.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can AI and ML help you design better exploratory testing scenarios?

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1 AI and ML for test generation

2 AI and ML for test prioritization

3 AI and ML for test execution

4 AI and ML for test learning

5 AI and ML for test collaboration

6 AI and ML for test innovation

7 Here’s what else to consider

Exploratory Testing

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