SQA skillset, AI and advancing medical research
Software professionals have a social responsibility to utilize our skillset to help advance medical/scientific research. SQA discipline and skillset is uniquely positioned as we do not build tools, we utilize tools to push boundaries, to verify declared limits; to discover software failings that coders and designers didn't imagine, love the phrase 'why would a user do that?' We, SQA, reside outside of the box, we don't just want to break things, we want to describe what we did, document our steps and show that it's repeatable.
Arguably, Y Combinator is one of the best known and most respected incubators. In February 2024, YC published a list of 'Start-ups we want to fund' innovative entrepreneur requests. On the list is 'Cure cancer.' The technology we're talking about is an MRI. Modern MRIs are sensitive enough to detect cancer masses as small as a millimeter. Kaggle has been offering bounties for a few years now. Analyzing data, approaching all publicly provided data with all skepticism - exception boundary testing, edge cases - SQA/SQE professionals know how to do this better than most other professions.
ChatGPT, Knowledge Graphs and:
- Utilize ChatGPT in developing datasets to validate data.
- Utilize Knowledge Graphs to develop complex relational, separate data to find common links.
- Utilize SQA skillset to harness ChatGPT and Knowledge Graphs outputs and framework to prove common starting points in the development of Multiple Sclerosis.
Tracy T. Burridge
20 plus years in software quality engineering from start-ups and enterprise software companies. Earned an MBA and MAT, experienced in software requirements and developing SQA frameworks and testing processes.