Data veracity is a concept where we define the degree to which data corresponds to the true values. It comes from the metrological concept of “measurement trueness”, which is the degree to which the measurement quantifies the value correctly.
Well, that sounds very simple, but it is in fact quite complex. In our previous work, we scrutinized what it means to have veracious data in transport systems (https://ieeexplore.ieee.org/abstract/document/7535482). It turns out that “lying” is not the only option here.
In this book, the author looks into the way how things can be untrue. Sometimes deliberately by lying, sometimes by mistake. Sometimes, as we learn in the last chapter (with Brazilian aardvark), a mistake can actually end up being accepted as truth over time.
I recommend the book as it is written in a fantastic manner, providing examples from the real world (e.g. the alleged drone sightings over Gatwick in 2018). It even goes a bit further and discusses the need of replication of studies and that we should get more funding for making the scientific results more solid and robust.
Software Engineering is one of the newest engineering fields with a growing need from the society side. The field develops rapidly which poses challenges in developing sustainable software engineering education – allowing the alumni to be effective in their work over a long period of time (long-term impact of the education) and keeping the education attractive for the potential students and industry.
The objective of this presentation is to describe the experiences from using business intelligence methods to develop, profile and monitor software engineering education on the master level. In particular we address the following research questions:
Which data sources should be used in developing a profile of a master program?
How to combine, prioritize and communicate the analyses of the data from the different sources?
How to identify barriers and enables of attractive sustainable software engineering education?
The results are a set of experiences from using data from the national agencies in Sweden (e.g. the Swedish Council for Higher Education – UHR, the Swedish job agency – Arbetsförmedlingen, international master education portals – mastersportal.eu) as input in development and evaluation of a master program in Software Engineering at University of Gothenburg.
The conclusions show that using the available sources lead to creating sustainable programs and we recommend using the data sources to a larger extent in the national and international level.
The paper is an experimental validation of whether requirement diagrams speed up the understanding of requirement specifications or whether they increase/decrease comprehension. The results show that the comprehension is increased while there is no change in time.