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MSR 2019
Sun 26 - Mon 27 May 2019 Montreal, QC, Canada
co-located with ICSE 2019

MSR is proud to announce a new education track. The MSR Education Track will share educational resources for MSR classes and include tutorials presented by MSR researchers on applied MSR techniques to conference members attending the MSR conference. During the MSR conference there will be 2 parallel tutorial tracks.

MSR Education Track serves as a hub of community educational collaboration and curation of educational resources relevant to Mining Software Repositories. We plan to collect and curate short articles that link to educational resources such as lessons, MOOCs, tools, datasets, tutorials, extractors, sources of SE data, etc. You too can contribute with a simple pull request!

If you are interested in contributing or want to browse the available resources please visit here.

Dates
Tracks

Sun 26 May

msr-2019-Education
13:50 - 14:35: MSR 2019 Education - Tutorial I at Centre-Ville
msr-2019-Education13:50 - 14:35
Tutorial
Kla TantithamthavornMonash University, Australia

Mon 27 May

msr-2019-Education
13:50 - 14:35: MSR 2019 Education - Tutorial II at Centre-Ville
msr-2019-Education13:50 - 14:35
Tutorial
Christoph TreudeThe University of Adelaide

“Software Analytics in Action: A Hands-on Tutorial on Analyzing and Modelling Software Data” by Chakkrit (Kla) Tantithamthavorn from Monash University, Australia.​

Description

Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via RStudio.


"Qualitative Data Analysis in Software Engineering: A Hands-on Tutorial” by Christoph Treude from the University of Adelaide, Australia.

Description

Software repositories contain many artefacts of qualitative nature, ranging from source code comments and commit messages to issue descriptions and documentation. To uncover interesting and actionable information about software systems and projects, researchers often have to interpret these artefacts using qualitative data analysis methods, e.g., through qualitative coding. In this tutorial, we will introduce qualitative coding as an iterative process of individual and team activities and discuss how to conduct such qualitative data analysis in a rigorous way to increase the credibility of the results. We will cover different coding methods as well as the interactive design of a coding guide, and we will discuss how to ensure quality in qualitative data analysis, focusing on credibility, transferability, dependability, and confirmability. We will conclude the tutorial with a brief overview of common pitfalls in qualitative data collection.