Month: January 2022

Post-Corona branding…

Post Corona: From Crisis to Opportunity: Galloway, Scott: 9780593332214: Amazon.com: Books

A good holiday reading is something that is an essential addition to the time spent with family and friends. Every year I try to get hold of a good book to get inspiration for the upcoming year. Last year, I’ve read “Grit”, which is about perseverance. This year, I noticed a book of NYU Stern professor Scott Galloway – “Post Corona: From Crisis to Opportunity”. Galloway is also the author of “The Four”, which is a book about Apple, Google, Facebook, and Amazon.

Now, to the topic of the day – the post-corona book. I’ve read this book a bit slower than I usually do (which is a good thing). When I read it I had Galloway’s voice in my head talking about the opportunities of large companies – a big tech, as he calls them. His thesis is that the pandemic actually accelerated their growth to the size which makes them really hard to disrupt. By mergers and acquisitions, they can “cannibalize” their competition, unless the competition is them.

I thought that this book would be about Zoom – a company unheard of before the pandemic, now a synonym for a phone call. I thought that the book would be about health services and telemedicine – another area that was small and now is big. Now, it was nothing like that. The book was about The Four and how they capitalize on their brands in times of pandemic.

There is a thesis out there, that if you are getting something for free, it’s not worth much. In this book, Galloway popularizes another thesis – if you are getting something for free, you are the product, not the consumer. He uses this as a way of explaining why Apple charges so much for their products – for not using our data, whereas Google and Facebook/Meta capitalize on our data. Apple connects 200 data points per day from us, while Google collects 2000 data points per hour – a small difference.

I’m not a privacy freak, but I do not want to be a product unless I choose to. I do not want companies to monetize on me, my behavior, and my family. But, and that’s a sad thing, I do want to have great services for a reasonable price. I want my maps to work well – the one in the car’s GPS simply does not make it. I want to watch short tutorials on YouTube – Netflix does not produce tutorials about variational autoencoders (yet).

To sum up, I like the thesis posed by Galloway, that the next big thing taken up by Amazon, will probably be medical insurance or schools. It is not difficult to see that the telemedicine model is essentially mature enough for being disrupted. I really recommend this book as food for thought in the post-pandemic (or endemic) world of 2022.

Test prioritization – a systematic review (review)

Image source: pixabay

Test case selection and prioritization using machine learning: a systematic literature review (springer.com)

Testing is an important activity in every software engineering project. In professional organizations, the process is structured and well-organized. In smaller projects, start-up style organizations, or in research studies, the process is less organized.

There are different views on why we do testing. Some think that we do testing to find defects, some to prove that the software works correctly, finally some think that we do this to waste time (well, not so many maybe). In my experience it is the combination of the first and the second. We do testing to find defects and also to track how good our software gets over time (software reliability growth modelling).

This paper presents a systematic literature review on using machine learning to select and prioritize test cases. I think that the authors summarize their contribution in a very good way (quote):

  • The main ML techniques used for TSP are: supervised learning (ranking models), unsupervised learning (clustering), reinforcement learning, and natural language processing.
  • ML-based TSP techniques mainly rely on features that are easy to compute and based on data that are practical to collect in a CI context, including execution history, coverage information, code complexity, and textual data.
  • ML-based TSP techniques are evaluated using a variety of metrics that are, sometimes, calculated differently in TS and TP, making it difficult to compare their results. Most of the currently available subjects have extremely low failure rates, making them unsuitable for evaluating ML-based TSP techniques.
  • Comparing the performance of ML-based TSP techniques is challenging due to the variation of evaluation metrics, test suite sizes, and failure rates across studies. Reporting failure rates alongside performance values helps provide more interpretable results to the wider research community.
  • Only six out of the 29 selected studies (21%) can be considered reproducible, thus raising methodological issues in the studies and a lack of confidence in reported results.

I think the biggest surprise, for me, is that complexity-based metrics are still used widely in this context. I’m happy that there are new approaches on the rise, for example textual analyses. I guess there is a point in combining approaches, but complexity seems like a very coarse-grained instrument for this type of analysis. We know it correlates well with size, and the larger the test (or UUT), the higher the probability of triggering a failure.

Well, I guess I need to make more experiments myself to check if I miss something.