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.

• Early Work in DSSE

• Problems in Data Science for SE

Readings

• Tu and Godfrey, Linux evolution
• 10 X productivity study and critiques
• 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

Exercises

These are done in class. The source code below is a combo of what I typed and what I prepped before hand.

3. Explore a repo using SonarQube metrics - linked in the notes
4. Write R code to load and explore simple data
5. Power calculations app
6. Sampling exercise (in class)
7. 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