DevOps Research-Based Teaching Using Qualitative Research and Inter-Coder Agreement

Abstract

DevOps is becoming a main competency required by the software industry. However, academic institutions have been slow to provide DevOps training in software engineering (SE) curricula. One reason for this is the fact that the problems addressed by DevOps may be hard to understand to students who have not previously worked in the industry or on projects of meaningful size and complexity. This paper shows an experience that integrates DevOps in SE curricula through research-based teaching (RBT). We aim to expose students to the problems that have led companies to adopt DevOps by researching and analyzing real cases of companies, thereby placing students at the center of learning. The contribution of this work is to innovate the application of RBT in software engineering by showing that the RBT approach is, at least, as good as the traditional approach and that it also leads to some extra benefits. This innovative solution has been implemented by using (i) qualitative analysis, specifically coding techniques, to discover knowledge and (ii) inter-coder agreement (ICA), specifically Krippendorff’s <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> coefficients, to measure the extent of students’ learning. These techniques allow teachers to determine whether students’ learning in the subject is homogeneous and to analyze disagreements among students during their analysis. This approach provides teachers with new tools (Krippendorff’s <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> coefficients) to identify those concepts that are less understood by students and to evaluate whether improvements in the research instruments (e.g., the codebook used in the qualitative analysis) also generate improvements in the students’ agreement. This RBT experience shows evidence that can be used to assess whether a similar experience and the use of ICA could be applied in similar learning contexts with similar research contexts.