Practical Applications of ML in Software Testing

Explore practical applications of machine learning in software testing through this comprehensive NDC Sydney 2020 conference talk. Delve into a detailed case study examining how ML algorithms can be adapted to various testing processes, aiming to reduce manual effort and enhance quality. Begin with an overview of machine learning types before exploring AI applications across different testing perspectives, including test definition, implementation, execution, maintenance, grouping, and bug handling. Discover both existing AI applications and potential future developments in the field. Gain insights into application areas with specific algorithms, and critically evaluate the advantages and potential risks associated with integrating AI into software testing practices. Learn how to leverage machine learning to overcome testing challenges and improve overall software quality.

Intro
AGENDA
INTRODUCTION
Me vs. Challenges
Can They Help?
Already being used widely
Learning: Meaning
Background
Algorithms
Applications
Software Testing Stages
Definition / Case Generation
Implementation / Test Generation
Implementation / Code Generation
Implementation / Execution
Maintenance / Refactoring
Maintenance / Self Healing
Maintenance / Prioritization
Maintenance / Bug Management
Bug Severity Estimation
Who is right?
Clustering
Summary