HerTechQuest 3.0

[Virtual Presenter] HerTechQuest 3.0 Digital Dreamers – (A-I ) automates testing through detailed user descriptions Team Members: Priyanka Mishra Birju Patwa Yagnesh Patel Abhishek Agrawal Gaurav Sharma 12,13-June-2-0-2-5.

[Audio] Problem Statement Testing plays a crucial role in the software development industry. Traditionally, testers dedicate significant time to manually crafting test cases and transforming them into automated test plans. Ensuring comprehensive coverage of all intricate details in both manual and automated test cases poses a challenge, potentially allowing bugs to slip through in releases.Covering all minute details in the manually written test cases and in its automation is a challenge and may leave bugs in the release. By harnessing the power of (A-I ), we can efficiently generate both test cases and their automation code by utilizing user story descriptions and acceptance criteria, enhancing accuracy and efficiency. The intelligent agent would leverage advanced algorithms and machine learning techniques to process and analyze textbook material, distilling the essential information into easily digestible summaries. This would not only save time for students and educators alike but also ensure that key concepts are clearly understood. Furthermore, the agent would evaluate different teaching methodologies and recommend the best approach for each topic. This personalized teaching strategy would cater to diverse learning styles and maximize the effectiveness of our educational programs. Lastly, the agent would generate assessment modules aligned with the content, providing comprehensive evaluations to measure student understanding and progress. These assessments would be designed to reinforce learning and identify areas that may require additional focus..

[Audio] Approach, Solution & Outcome Approach Generative (A-I ) to draft comprehensive test plans and to Ensure coverage of even the most minute details and functionalities within test cases. Solution The Tax Law Change Summarizer is a Python based web application. Build a tool that takes recent tax law changes and generates plain language summaries of how they might impact different taxpayer profiles. Outcome Python based web application demonstrates how (A-I ) can democratize access to specialized knowledge, making complex regulatory information accessible and actionable for everyday users. Benefits The time taken in manual and automation test plans is reduced by 50%. Because of reduced human intervention, the test plans are clearer and understandable. The efficiency and coverage of features in automation test cases increased by 30%.

[Audio] Demos. Demos. 4.

[Audio] Challenges Faced Getting a complete and detailed description and acceptance criteria mentioned in the user stories. Mastering the prompt to get the desired results will take time and experience. Experiment & Metrics.

[Audio] Further Development The prompts can be used to generate unit test cases also by the developers and testers Which will eventually result in better code coverage and will help in building CI/CD pipelines. If the test plan generation will be easy and quicker then various categories of testing can be covered like Functional testing Non functional testing Integration testing Regression testing Performance testing Load testing Experiment & Metrics.

[Audio] Conclusion These days advancements in (A-I ) technologies and tools are making things easy and quick to complete. The test cases can be created and converted to automation efficiently. The tester just need to verify and update for any missing step. This makes the testing rounds quick so that testers will be able to spend time to cover different types of testings like functional, regression, performance, load testing, integration testing, unit testing. Experiment & Metrics.

[Audio] Thank You. Thank You.