Month: February 2018

I’ve been browsing the latest issue of IST and this article cought my attention. The article is written by Tommi Mikkonen, Casper Lassenius, Tomi Männistö, Markku Oivo, Janne Järvinen. It is about technology transfer from academia to industry. It’s available at: https://doi.org/10.1016/j.infsof.2017.10.013

The best point in this article is very important – the technology is NOT created in academia and transferred to industry, it is rather created either in industry or in collaboration with academia. This observation invalidates many of the technology transfer models, where the authors assume that the companies receive the results from academia.

But, has this actually happen? How often does it really happen? I guess, not very often.

The paper presents a model of collaboration, which is presented in the following link (and figure):

https://ars.els-cdn.com/content/image/1-s2.0-S0950584917304007-gr3.jpg

I’m happy to see more collaboration models for industry-academia co-creation of results!

I often tell my students that the absolute values of measures do not always say much. Take an example of McCabe cyclomatic complexity – the number of 100 (meaning 100 independent paths through a method) does not need to denote problems. It could be a large switch statement which changes locale based on the IP address type. However, it is important to monitor thresholds of measures, based on the meaning of the function and the problem at hand.

In this article from IST, “Software metrics thresholds calculation techniques to predict fault-proneness: An empirical comparison” (https://doi.org/10.1016/j.infsof.2017.11.005), we can learn three different types of finding thresholds for software measures – ROC curves, VARL, and Alves ranking (named after the author of the method). This article shows how well we can predict the fault-proneness of modules if we use thresholds rather than absolute value.

Have a nice reading!