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

Before Class

Lectures

• Early Work in DSSE • video
• Problems in Data Science for SE • video1 • video2

Readings

• Tu and Godfrey, Linux evolution
• Bad Smells in Software Analytics Papers
• Promises and Perils of Mining Git
• Aranda and Venolia, The secret life of bugs: Going past the errors and omissions in software repositories
• Nagappan, Zimmermann, Bird, Diversity in SE Research (not diversity in the EDI sense, rather in sampling)

In Class

Slides

• Early Approaches notes
• Problems notes

Code and Data

• Write R code to load and explore simple data
• Power calculations app

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
• 10 X productivity study and critiques