How Data Science is Changing Software Testing – Robert Musson talk

I enjoyed Robert Musson’s recent presentation How Data Science is Changing Software Testing and recommend you watch it or at least read Robert’s Presentation Slides which don’t do it full justice, but should tease you.
As the abstract stated: It will describe the new skills required of test organizations and the ways individuals can begin to make the transition.

I worked with Bob a few times while at Microsoft, and he truly was one of the original Data Science testers for the past decade doing Data Analytics.   He says (37:50 into video) the tide has turned recently and he has “seen more progress in last 6 months than seen in past 10 years.”

So now I need to learn

  • Statistics, e.g., r-value, p-value, Poisson and Gamma distributions
    Homogenous (non-changing) or Non-homogenous (changing) Poisson for reliability measurements to get me used to time analysis.
  • R language (open source version of S).
    Object oriented with many packages to do exploratory data analysis and quick linear models.
  • Python for easier data manipulation including building dictionaries and packages for linear algebra

So I can prepare for the mindset change.

Mindset change is to one of information discovery vs. bug discovery

An audience member asked how to learn, and Bob recommended for many courses, including statistic courses..  He called out specifically,
Model Thinking – Scott Page – U. of Michigan.
I love models, but I might also start with


About testmuse

Software Test Architect in distributed computing.
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2 Responses to How Data Science is Changing Software Testing – Robert Musson talk

  1. Pingback: Testing Bits – 5/11/14 – 5/17/14 | Testing Curator Blog

  2. robertbinder says:

    Hi Keith – thanks for reporting this – it is a very good presentation. I’ve long felt the application of testing to databases is pretty much terra incognita – unstructured data adds to the challenges. Musson’s points are well-taken, but apart from avoiding mis-modeling and validating the actual predictive power of a model, I don’t see much help for evaluating either the systems that collect the data, the data itself, or the correctness of what models predict. As models are often developed to try to answer entirely new questions, it can be hard to determine if their answers are “right.” For an excellent discussion of the pitfalls in data analysis, I suggest anything by Nicholas Taleb: The Black Swan, Fooled by Randomness, and Fragile.

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