Is GenAI ready to support testing in software modernization projects?
AI Summary
Summary Note for Webinar on Modernizing Software Development with Gen
Webinar Details
- Title: Modernizing Software Development with Gen
- Presenters: Mi (Operations Manager, wfir) and Virgil (Tech Enthusiast)
- Duration: Session recorded for later viewing.
- Purpose: To explore how Gen assists in software modernization and testing.
Summary of Key Topics
- Introduction:
- Focus on utilizing Gen to enhance developer activities.
- Review of previous webinar findings on code generation for various development layers.
- Software Modernization Approach:
- Emphasizes improving performance, usability, security, and maintainability of legacy systems.
- Involvement of QA/QC practices at every stage of the software development lifecycle (SDLC).
- Quality Assurance (QA) vs. Quality Control (QC):
- QA ensures the process used in software development promotes excellence.
- QC evaluates the actual software for defects.
- Importance of Testing:
- Discusses role and types of software testing: functional, non-functional, regression, and automated testing.
- Best practices include early testing, involving user feedback, and utilizing reports.
- Testing Frameworks:
- Various frameworks are outlined, such as JUnit and integration testing frameworks.
- Framework selection based on the backend (e.g., ABL Unit) versus frontend (e.g., Cypress).
- Generative AI in Testing:
- Discussed the advantages of leveraging AI for automatic test generation, data generation, and code analysis capabilities.
- Highlights of AI enhancing test efficiency, test coverage, and error detection.
- Challenges with AI Integration:
- Issues like setup complexity, data quality dependency, and potential biases in AI outputs.
Demonstration**
- Live Demo:
- Conducted using Gen tools for creating unit tests and end-to-end testing with Cypress for frontend applications.
- Demonstrated setup, execution, and QA process improvements using generative AI.
Conclusions**
- Key Takeaways:
- Gen can significantly streamline unit test creation, though human review remains essential.
- Successful outcomes depend on well-structured prompts and understanding existing codebases.
- Collaboration between developers is crucial in establishing coding standards that support AI-driven development and testing.
- Next Steps: Encouragement for participants to explore the use of Gen in their projects.