Enhancing Agile Software Testing with Generative AI
Explore how Generative AI revolutionizes software testing in agile environments, leading to faster feedback loops, automated regression testing, and improved defect prediction. Uncover adoption challenges, propose solutions, and provide actionable recommendations for optimizing AI-driven testing processes. Drive innovation in agile testing to realize the full benefits of AI while addressing adoption hurdles.
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Presentation Transcript
Transforming Agile Software Testing with Generative AI: A Study of Evolving Work Practices Dr. Pavankumar Mulgund Management Information Systems, University of Memphis pmulgund@memphis.edu Dr. Mark Gillenson, PhD Management Information Systems, University of Memphis mgillnsn@memphis.edu Dr. Ankur Arora Management Information Systems, University of Memphis ankur.arora@memphis.edu
The Need for AI in Agile Software Testing The Challenge Traditional software testing struggles to keep pace with agile development. Manual processes slow down releases and impact efficiency. Growing complexity demands smarter, adaptive solutions. The AI Opportunity Gen AI automates testing, accelerates defect detection, and enhances CI/CD efficiency. AI-driven test cases improve coverage and catch issues earlier. Why This Matters Research is essential to unlock Gen AI s full potential in agile testing. Addressing adoption barriers ensures seamless AI integration. Future research will drive innovation in AI-powered software quality assurance.
Research Objectives & Approach Key Research Goals: Investigate how Gen AI enhances software testing in agile environments. Identify benefits such as faster feedback loops, automated regression testing, and improved defect prediction. Understand the challenges teams face in adopting Gen AI and propose solutions. Methodology: Conduct qualitative interviews with software testers and managers to analyze adoption patterns. Evaluate AI-driven testing strategies across different phases of the software test lifecycle. Compare pre- and post-AI integration testing performance. Expected Outcomes: Practical insights into AI adoption in agile testing. Best practices for overcoming challenges and optimizing AI-driven testing processes. Actionable recommendations for organizations to leverage AI in software testing.
Why This Research Matters? Potential Impact: Industry: Enables faster, more reliable software development, reducing testing costs and release times. Academia: Contributes to research on AI-driven quality assurance and agile methodologies. Workforce: Supports the upskilling of testing teams to effectively integrate AI tools. Call to Action: With research, we can drive innovation in agile software testing, ensuring AI s benefits are fully realized while addressing adoption challenges.
