30 Days of AI in Testing Challenge: Day 31 Bonus: Visualise the future of AI in testing
nao.deng ·
30 Days of AI in Testing Challenge
30 Days of AI in Testing Challenge: Day 30: Consider what your AI Test Buddy would do for you
30 Days of AI in Testing Challenge: Day 29: Share whose work is influencing your AI in testing approach
30 Days of AI in Testing Challenge: Day 28: Build your own AI Tools
30 Days of AI in Testing Challenge: Day 27: Assess your team’s readiness to adopt AI-assisted testing
30 Days of AI in Testing Challenge: Day 26: Investigate strategies to minimise the carbon footprint of AI in testing
The blog post discusses strategies for minimizing carbon footprint in AI testing, as part of the 30-day AI Testing Challenge. It may cover aspects such as using energy-efficient hardware devices, optimizing testing processes to reduce resource consumption, and adopting renewable energy sources for power. The author may share practical experiences in reducing carbon footprint, as well as reflections and evaluations on environmentally friendly testing strategies. By sharing strategies for reducing carbon footprint, readers will gain insights into the author's focus on sustainability in AI testing and how to balance testing needs with environmental responsibilities in practice. This series of activities aims to provide testing professionals with an opportunity to understand and explore sustainable development strategies in AI testing, and to promote industry attention and advocacy for environmentally friendly testing practices.
30 Days of AI in Testing Challenge: Day 25: Explore AI-driven security testing and share potential use cases
This blog post is about the twenty-fifth day of the 30-Day AI Testing Challenge, exploring AI-driven security testing and sharing potential use cases. The article may introduce the application of artificial intelligence in the field of security testing, such as vulnerability scanning, malware detection, behavior analysis, etc., and discuss potential use cases in different scenarios. The author may share their understanding and insights into AI-driven security testing techniques, as well as thoughts on its potential value in improving security and reducing risks. By sharing potential use cases, readers will gain insights into the practical application scenarios and potential effects of AI in security testing, as well as prospects for the future development of security testing. This series aims to provide a platform for testing professionals to understand and explore the application of AI in security testing, and to promote wider application and deeper research of AI in testing within the industry.
30 Days of AI in Testing Challenge: Day 24: Investigate code explanation techniques and share your insights
This blog post is about the 24th day of the 30-day AI Testing Challenge, exploring code interpretation techniques and sharing insights. The article may introduce different code interpretation techniques, such as explainable AI, model interpretation, and interpretable machine learning, and discuss their applications in the testing domain. The author may share their understanding and experience in using these techniques, as well as insights into their advantages, challenges, and potential application areas. By sharing insights into code interpretation techniques, readers will gain an understanding of the author's exploration and thoughts on new technologies and methods in AI testing, as well as expectations and prospects for future developments. This series aims to provide a platform for testing professionals to understand and explore new technologies and methods in AI testing and to promote further research and application of AI in the testing domain.
30 Days of AI in Testing Challenge: Day 23: Assess AI effectiveness in visual testing and discuss the advantages
This blog post is about Day 23 of the 30-Day AI Testing Challenge, focusing on assessing the effectiveness of Artificial Intelligence in visual testing and discussing its advantages. The article may include the author's practical application experience of using AI for visual testing, as well as thoughts and evaluations on the advantages and challenges AI brings to visual testing. By sharing evaluations of the application effects and advantages of AI in visual testing, readers will gain insight into the author's understanding and perspective on this emerging testing method, as well as a forecast for the application prospects in the visual testing field. This series of activities hopes to provide testing professionals with an opportunity to understand and explore the application effects and advantages of AI in visual testing, and to promote deeper research and application of AI in the testing field within the industry.
30 Days of AI in Testing Challenge: Day 22: Reflect on what skills a team needs to succeed with AI-assisted testing
This blog post is about Day 22 of the 30-Day AI Testing Challenge, discussing the skills necessary for a team to succeed in AI-assisted testing. The article might include the author's reflections on the skills and qualities team members need to possess, as well as the key factors and challenges to success in AI testing. By sharing the required skills and qualities for teams in AI testing, readers will gain insights into the author's views on building an efficient AI testing team and advice on how to develop and enhance team members' professional abilities in the AI testing field. This series of activities hopes to provide testing professionals with an opportunity to understand and explore the skills needed for teams in AI-assisted testing and to offer guidance and references for team building.
30 Days of AI in Testing Challenge: Day 21: Develop your AI in testing manifesto
This blog post is about Day 21 of the 30-Day AI Testing Challenge, encouraging participants to create their own AI Testing Manifesto. The article may include the author's elaboration on the core values, vision, and commitments related to AI testing, as well as reflections on the principles and guidelines for the application of AI in testing. By sharing a personal AI Testing Manifesto, readers will gain a profound understanding of the author's views on the significance and application value of AI in the testing field, along with a vision and expectations for the future development of AI testing. This series of activities hopes to provide a platform for testing professionals to express their personal opinions and values, and to promote in-depth discussions within the industry regarding the development and application of AI in testing.
30 Days of AI in Testing Challenge: Day 20: Learn about AI self-healing tests and evaluate how effective they are
This blog post is about Day 20 of the 30-Day AI Testing Challenge,exploring the effectiveness of AI self-healing tests. The article may include the author's definition, purpose, and methods of AI self-healing testing, as well as an evaluation of its effectiveness and practical experience. By sharing explorations and evaluations of AI self-healing testing, readers will understand the author's views and insights on this emerging testing method, as well as its application effects in real testing environments. This series of activities hopes to provide testing professionals with an opportunity to understand and explore the potential and limitations of AI in self-healing testing, and to promote further research and application of AI testing in the industry.
30 Days of AI in Testing Challenge: Day 19: Experiment with AI for test prioritisation and evaluate the benefits and risks
This blog post is about Day 19 of the 30-Day AI Testing Challenge, focusing on exploring the role of AI in test priority sorting and evaluating its pros and cons. The article may include the author's practical application cases of AI in test priority sorting, as well as the benefits and challenges brought by using AI. By sharing experiences and evaluations of applying AI in test priority sorting, readers will gain insights into the author's views on the actual effects and impacts of AI in the testing process. This series of activities hopes to provide testing professionals with an opportunity to understand and explore the role of AI in test priority sorting, and to promote further discussions about the application of AI in testing.
30 Days of AI in Testing Challenge: Day 18: Share your greatest frustration with AI in Testing
This blog post is about Day 18 of the 30-Day AI Testing Challenge, aimed at sharing the biggest challenges participants face in AI testing. The article may include difficulties, challenges, and obstacles encountered by the author in practice, as well as corresponding solutions or coping strategies. By sharing the difficulties and challenges encountered, readers can understand the problems others may face in AI testing and gain inspiration and assistance from them. This series of activities hopes to provide a platform for testing professionals to exchange, learn, and solve problems together, promoting progress and development in the field of AI testing.
30 Days of AI in Testing Challenge: Day 17: Automate bug reporting with AI and share your process and evaluation
This blog post is about Day 17 of the 30-Day AI Testing Challenge, looking at automating bug reporting with AI and sharing your personal process and evaluation results. The article may cover the author's process for automating defect reporting using AI technology, including tool selection, implementation methodology, benefits of automating the process, and evaluation results. By sharing the process and evaluation results of automated defect reporting, readers will learn about the authors' experiences and lessons learned in practice, as well as the potential of AI technologies to improve the efficiency of defect management. This series promises to provide an opportunity for testing professionals to understand and explore the use of AI to automate defect reporting and to promote technological advancement and innovation in the industry.
30 Days of AI in Testing Challenge: Day 16: Evaluate adopting AI for accessibility testing and share your findings
This blog post is about Day 16 of the 30-Day AI Testing Challenge to evaluate the adoption of AI for accessibility testing and share personal findings. The article may cover the author's experience with the practical application of accessibility testing with AI, including the selection of AI tools, improvement of testing methods, and validity of test results. By sharing evaluations and findings on accessibility testing with AI, readers will learn about the authors' applications in real-world testing scenarios and learn from their experiences and lessons learned. This series promises to provide an opportunity for testing professionals to learn about and explore the use of AI in the field of accessibility testing, and to foster industry dialog and technological innovation.
30 Days of AI in Testing Challenge: Day 15: Gauge your short-term AI in testing plans
This blog post is Day 15 of the 30 Day AI Testing Challenge and looks at measuring short-term AI in test programs. The article may include criteria for evaluating short-term AI applications in a test program, as well as methods on how to determine their success. By sharing methods and hands-on experience in measuring short-term AI applications, readers will gain insights and guidance from the authors on practical applications of using AI in test programs. This series promises to provide a platform for testing professionals to understand how to measure and evaluate short-term AI applications and to foster broader industry discussions.
30 Days of AI in Testing Challenge: Day 14: Generate AI test code and share your experience
This blog post is about the 14th day of the 30-Day AI Testing Challenge event, aimed at generating AI test code and sharing experiences. The post may include the author's process of using AI tools to generate test code, the choice of tools, the quality assessment of the generated code, and the application experience in actual testing. By sharing the process and experience of generating AI test code, readers will learn about examples of AI application in the field of testing, as well as the author's views on the effectiveness and reliability of AI-generated code. This series of events is expected to provide an opportunity for testing professionals to understand and try using AI testing tools and to share their experiences and insights.
30 Days of AI in Testing Challenge: Day 13: Develop a testing approach and become an AI in testing champion!
This blog post is about the thirteenth day of the 30-Day AI Testing Challenge event, where participants are required to develop their own testing methods and become pioneers in AI testing. The post may include the author's thoughts and methodologies on developing new AI testing methods, as well as the experiences and outcomes of applying these methods in practice. By sharing their own process of developing testing methods and the results, readers will learn about the author's innovative practices and leading position in the field of AI testing, inspiring more people to try and explore the application of AI in testing. This series of events is expected to provide testing professionals with an opportunity to deeply understand and practice the development of AI testing methods and encourage them to become pioneers in the field of AI testing.
30 Days of AI in Testing Challenge: Day 12: Evaluate whether you trust AI to support testing and share your thoughts
This blog post is day eleven of the 30 Days of AI Testing Challenge, focusing on the use of AI to generate test data and evaluating its effectiveness. The post may include the author's real-world application of AI-generated test data and an assessment of its effectiveness and applicability. By sharing the application and evaluation of AI-generated test data, readers will understand how the author leverages AI technology to generate valid test data and enhance the efficiency of the testing process in real testing environments. This series of events is expected to provide testing professionals with cases of practical application of AI-generated test data and encourage them to experiment with this emerging technology.
30 Days of AI in Testing Challenge: Day 11: Generate test data using AI and evaluate its efficacy
This blog post is day eleven of the 30 Days of AI Testing Challenge, focusing on the use of AI to generate test data and evaluating its effectiveness. The post may include the author's real-world application of AI-generated test data and an assessment of its effectiveness and applicability. By sharing the application and evaluation of AI-generated test data, readers will understand how the author leverages AI technology to generate valid test data and enhance the efficiency of the testing process in real testing environments. This series of events is expected to provide testing professionals with cases of practical application of AI-generated test data and encourage them to experiment with this emerging technology.
30 Days of AI in Testing Challenge: Day 10: Critically Analyse AI-Generated Tests
This blog post is day ten of the 30 Days of AI Testing Challenge,which calls for participants to critically analyze tests generated by artificial intelligence. The blog post may include the author's evaluation of AI-generated tests, covering aspects like accuracy, completeness, and coverage. By sharing the results of the critical analysis, readers will gain insight into the author's deep understanding and views on AI-generated testing. This series of events aims to provide testing professionals with practical cases to deepen their knowledge of AI test generation outcomes and to stimulate further discussions on improving the quality of AI-generated tests.
30 Days of AI in Testing Challenge: Day 9: Evaluate prompt quality and try to improve it
This blog post is the eighth day of the 30-day AI Testing Challenge, focusing on creating detailed prompts to support the testing activities. The post may include the author's reflections on how to design and build prompts necessary for the testing activities, as well as insights gained during this process. By sharing detailed prompt designs, readers will be able to understand how the author uses prompts in testing activities and effectively guides AI in tasks related to testing. This series of activities is expected to provide practical examples and experiences for testing professionals applying AI testing.
30 Days of AI in Testing Challenge: Day 8: Craft a detailed prompt to support test activities
This blog post is the eighth day of the 30-day AI Testing Challenge, focusing on creating detailed prompts to support the testing activities. The post may include the author's reflections on how to design and build prompts necessary for the testing activities, as well as insights gained during this process. By sharing detailed prompt designs, readers will be able to understand how the author uses prompts in testing activities and effectively guides AI in tasks related to testing. This series of activities is expected to provide practical examples and experiences for testing professionals applying AI testing.
30 Days of AI in Testing Challenge: Day 7: Research and share prompt engineering techniques
This blog post is the seventh day of the 30-Days AI Testing Challenge, which requires participants to research and share real-time engineering technology. The post may include a definition of real-time engineering technology, its applications in the testing domain, introductions to relevant tools and technologies, and the author's perspective on real-time engineering technology. By sharing research on real-time engineering technology, readers will gain insights into its potential value in testing and how to effectively apply this technology. This series of activities aims to provide a platform for testing professionals to deeply understand and discuss emerging technologies.
30 Days of AI in Testing Challenge: Day 6:Explore and share insights on AI testing tools
This blog post is day six of the 30 Days of AI Testing Challenge, encouraging participants to explore and share insights about artificial intelligence testing tools. The blog post may include an introduction to different AI testing tools, an assessment of their features and applicable scenarios, and sharing the author's experiences and opinions on these tools. Through such sharing, readers can better understand the AI testing tools available on the market and their roles in the testing process. This series of events hopes to provide testing professionals with a comprehensive understanding of AI testing tools and prompt them to more flexibly choose the tools that are suitable for their projects.
30 Days of AI in Testing Challenge: Day 5:Identify a case study on AI in testing and share your findings
This blog post is for the fifth day of the 30-day AI testing challenge event, which requires participants to identify a case study of artificial intelligence in testing and share their findings. The blog post may include the background, objectives, and methods of the case study, as well as key insights discovered during the research process. By sharing the case study, the author is able to demonstrate the application of AI in real-world testing scenarios to the readers, promoting the exchange of knowledge and learning. This series of events is expected to provide testing professionals with an in-depth understanding of AI testing and encourage them to actively participate in the research of actual cases.
30 Days of AI in Testing Challenge: Day 4: Watch the AMA on Artificial Intelligence in Testing and share your key takeaway
This blog post is the fourth day of the 30-Day AI in Testing Challenge, in which participants are asked to watch a video or presentation on artificial intelligence in testing and share their key takeaways. The post may include a summary of what the author watched, mentioning new insights into the understanding and application of AI in testing. Through this series, readers can continue to expand their knowledge of the field of AI in testing by watching videos and other formats, while sharing this knowledge and facilitating interaction among participants.
30 Days of AI in Testing Challenge: Day 3: List ways in which AI is used in testing
This blog post is the third day of the 30-Day AI Testing Challenge and focuses on the many ways AI can be used in testing. The post may include an introduction to the various uses of AI in testing, such as automated testing, defect analysis, performance test optimization, and more. Readers will learn how AI can improve the testing process and increase testing efficiency, as well as the potential benefits of applying AI in testing. This series promises to provide a platform for testing professionals to comprehensively understand and discuss the use of AI in testing.
30 Days of AI in Testing Challenge: Day 2: Read an introductory article on AI in testing and share it
This blog post is the second day of the 30-Day AI in Testing Challenge and focuses on a session where participants read and share introductory articles related to AI in testing. The post may contain the author's summary and personal opinion of the article read, sharing the potential benefits and challenges of applying AI in testing. Through such sharing, readers are able to better understand the application of AI in testing and prompt other participants to share their insights and promote interactivity of the blog posts. This series promises to provide a platform for testing professionals to gain insights into AI testing.
30 Days of AI in Testing Challenge: Day 1: Introduce yourself and your interest in AI
This blog post is about the first day of the 30 Day AI Testing Challenge and introduces the start of the program. The blog post begins on the first day of the challenge and explores participants introducing themselves and their interest in AI. The post may include the author's background, work experience, and expectations for AI testing. This series of challenges promises to provide readers with an opportunity to dive deeper into AI testing and continue to learn, and may also contain some encouragement and motivation to actively participate throughout the challenge.