Design an Empirical Study of Pull Request Review Throughput
Overview
What this challenge is about.
Design an Empirical Study of Pull Request Review Throughput. Advanced challenge in research. Conducting rigorous research on real questions, earn a blockchai...
The Brief
What you'll do, and what you'll demonstrate.
Design an empirical study that determines whether and how Larkspur's pull request review process slows feature delivery, with findings rigorous enough to defend and specific enough to act on.
This is not a research exercise. It is the work a researcher does to produce findings that withstand scrutiny. That distinction matters to every hiring manager who has seen candidates summarize papers and none who have produced original findings under expert review.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Translate a vague organizational concern into falsifiable, measurable hypotheses
- Select statistical tests that match the distribution and structure of observational software data
- Report effect sizes and confidence intervals so findings convey practical, not just statistical, significance
- Build a reproducible analysis pipeline a peer can re-run end to end
- Convert statistical findings into recommendations an engineering team can ship
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Empirical Software Engineering
Apply empirical software engineering to solve real industry problems and demonstrate production-level capability.
- Software Analytics
Apply software analytics to solve real industry problems and demonstrate production-level capability.
- Statistical Analysis
Apply statistical analysis to solve real industry problems and demonstrate production-level capability.
- Data Engineering
Design and build pipelines that collect, transform, and deliver reliable data at scale.
- Code Review
Apply code review to solve real industry problems and demonstrate production-level capability.
- Hypothesis Testing
Apply hypothesis testing to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Developer Productivity Engineer
This challenge mirrors the core of the role: measuring engineering workflows, proving where time is lost with defensible statistics, and proposing changes a platform team can adopt to speed delivery without guesswork.
This challenge sharpens
- empirical-software-engineering
- software-analytics
- code-review
Software Analytics Engineer
You build a pipeline that turns raw repository records into trustworthy metrics and tested conclusions, which is exactly the daily work of turning engineering telemetry into decisions leaders act on.
This challenge sharpens
- software-analytics
- statistical-analysis
- data-engineering
Research Software Engineer
Framing falsifiable hypotheses, matching tests to data, and reporting effect sizes with reproducible code bridges directly to research engineering, where rigor and repeatability decide whether findings are believed.
This challenge sharpens
- hypothesis-testing
- statistical-analysis
- empirical-software-engineering