Judging from my industry friends, some time in their professional career (usually later), the teaching bug bites them and they wonder about passing on their knowledge to a new generation of industrialists and entrepreneurs. A regular position as a professor at a German public university or a polytechnic (university of applied sciences) is often unattainable because of age and a missing or limited publication record, and even though I’d recommend it, contributing as an unpaid lecturer and building up a vita to go for an honorary professor position seems unattractive.
In September 2020, I will be teaching a workshop series on commercial open source startups at UC Santa Cruz (and starting November, as a course, at FAU). The series at UCSC is being faciliated by CROSS, the Center for Research in Open Source Software, and I’m getting help from Thomas Otter (@vendorprisey). If you would like to register, check out the official announcement! If you are affiliated with UC Santa Cruz, talk to Stephanie Lieggi (or me) to get in! If you are just curious, here is the general syllabus.
Software product management is easily the least well understood yet most important business function in software companies. I have been teaching Software Product Management by Case for about ten years now, and it is time I change a gear or two. Hence, I’m asking whether anyone is interested in helping me teach this course, whether in small or large capacity. For details, please see this slide deck:
Abstract: Pattern discovery, the process of discovering previously unrecognized patterns, is usually performed as an ad-hoc process with little resulting certainty in the quality of the proposed patterns. Pattern validation, the process of validating the accuracy of proposed patterns, has rarely gone beyond the simple heuristic of “the rule of three”. This article shows how to use established scientific research methods for the purpose of pattern discovery and validation. The result is an approach to pattern discovery and validation that can provide the same certainty that traditional scientific research methods can provide for the theories they are used to validate. This article describes our approach and explores its usefulness for pattern discovery and evaluation in a series of studies.
Keywords: Patterns, pattern discovery, pattern validation, theory codification, theory building and evaluation, research design
Reference: Riehle, D., Harutyunyan, N., & Barcomb, A. (2020). Pattern Discovery and Validation Using Scientific Research Methods. Friedrich-Alexander-Universität Erlangen-Nürnberg, Dept. of Computer Science, Technical Reports, CS-2020-01, February 2020.