Leslie has extensive experience with QA and has automated testing in more languages than he can remember. He has constantly searched for more efficient methodologies and tools for automation and has trained and led teams in new and innovative automation technologies. His favorite anecdote is about the test that he reduced from 30 minutes (automated by someone else) to 90 seconds (automated better).
In his constant drive to test better, faster, and cheaper tomorrow than today, Leslie was an early adopter of Specification by Example/BDD, and has used it to transform three different organizations. He has trained more than 300 Business Analysts, Developers, and Testers in the correct use of SBE, including using it to deliver automated tests while the developers are still writing the code. In his current job he is applying SBE to data science applications, and loving the challenge and the fast pace of learning.
Leslie currently works for a top 3 US Defense Contractor. He has a Bachelor's in Math/Computer Science, MBA and holds a couple of patents for in-circuit hardware testing. Leslie is a long-time member of AQAA, always active in the testing community, and currently serves as a board member.
BDD/SBE applied to Data Science & Machine Learning
Data Science and Machine Learning applications are very different from traditional applications, and the differences make it difficult to apply traditional techniques for writing requirements and performing QA. How do you write requirements and test an application that inherently has an element of randomness built into it? How do you test a neural network when you can't explain why one set of neural weights performs better than another set? Because of this, Data Science and Machine Learning have been resistant to the application of both traditional requirements and traditional QA.
In this presentation you will learn:
Why DS/ML haven't followed traditional requirements and QA practices
Which aspects of DS/ML make it difficult to apply traditional requirements and QA practices
How to apply SBE/BDD to Data Science and Machine Learning applications
How to write the requirements
How to automate the testing and deliver it early
Problems and pitfalls to avoid