Early Approaches and Problems in SE data
Early work in DS for SE, and some possible challenges.
Learning Outcomes
- Appreciate how the field started; lessons from the past. Taylorism.
- Differentiate between SE “engineering” and factory work?
- Challenges with SE data mining and data sources.
- Understand limitations with SE inferences
Topics and slides
These are the submodules I covered in class.
Readings
Exercises
These are done in class. The source code below is a combo of what I typed and what I prepped before hand.
- Explore a repo using SonarQube metrics - linked in the notes
- Write R code to load and explore simple data
- Power calculations app
- Sampling exercise (in class)
- Data validity exercise
Optional Readings and Activities
These readings enrich the material but are not strictly necessary to read.
- Does AI help productivity, small N study
- Kitchenham, Empirical SE Guidelines
- Interview with Bossavit
- How to read a paper (from ResearchSkills class)
- The Mythical Man-Month
- Peopleware
- Promises and Perils of Mining Github
- Threats of Aggregating Software Repository Data
- Sampling in SE Research
- Students in experiments