Digitalisierung
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Debian, as a collection of software packages and components, is known to be one of the largest software projects in the history of mankind. Combined with a traceable history over many years, the artefacts created by Debian developers and users make it one of science’s favourite targets to quantitatively or qualitatively understand how real-world software development works (or does not), how people collaborate, and many other other related questions. Unfortunately, while scientists make ample use of the resources and artefacts created by FLOSS and friends, the exchange of insights and ideas does not seem to extend in both directions: Developers, users and integrators are often unaware of results obtained in science. This talk will introduce the Debian community to a selection the most important results obtained by scientific (software engineering) research, with a special focus on large-scale socio-technical analysis of projects like Debian, and the possible implications and improvements these may bring to Debian development itself.
Embedded Linux drives an every-increasing number of appliances in many domains and applications, some even real-time and/or safety critical. Traditional quality assurance of such systems is based on testing and formal verification, but the huge amount of code and the rapid dynamics of the Linux ecosystem, as well as fundamental limitations of formal methods make these approaches unsatisfactory.
Statistical quality assurance for reliability, error rates, maximal latencies etc. is needed. We will discuss current best practises, how to design and run automated statistical tests that capture relevant information, and how to properly evaluate the resulting data. Practical real-world examples and recipes are played through using the open source R language. Most importantly, we identify common mistakes in (over-)interpreting statistical results and predictions that may eventually harm people.