Promise 2011: Panel - "Practical Software Project Improvements using Actionable Predictive Models and Solutions"
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Promise 2011: Panel - "Practical Software Project Improvements using Actionable Predictive Models and Solutions" Jacky Keung.
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Promise 2011: Panel - "Practical Software Project Improvements using Actionable Predictive Models and Solutions"
- 1. Practical Software Project ImprovementsUsing actionable predictive models and solutions Panel Members: Jacky Keung (HK Polytechnic University) Wang Qing (Chinese Academy of Science) Martin Shepperd(Brunel University) Emila Mendes (Auckland University)
- 2. Welcome
- 4. Introduction
- 5. Can we use our metrics to change projects?
- 6. Actionable Metrics Research For example.. Programmer actions vs. software defects Some actions introduce defects Measuring likelihood of introducing defects Early warning system for programmers
- 7. Elementary programmer actions Opening files Writing tests Running programs …etc
- 8. Defects and Programmer Actions Can we correlate defects with programmer actions (failure-correlated actions)? Can we isolate defect-related actions? Can we prevent defects by preventing actions? If all yes, we can build a model and to prevent defects!!
- 9. Solutions need to be practical The truth is not so simple … Cannot easily change standard practices Change a process leads to other concerns Productivity, process, outcomes Is that what we have been doing over the past 30 years? …
- 10. The story begins…
- 11. Failure is a Four-Letter Word: A Satire in Empirical Research Andreas Zeller, Thomas Zimmermann, Christian Bird
- 12. Panelist Discussion Jacky Keung, Wang Qing, Martin Shepperd, Emila Mendes
- 13. Practical Software Project Improvements using Actionable Predictive Models and SolutionsMartin ShepperdBrunel Uni, UK Actionable -> causality Machine learning ≠ magic 13 Martin Shepperd
- 14. Casuality requires … X covaries with Y X has temporal precedence No more plausible competing explanations ✓ ✓ ✗ 14 Martin Shepperd
- 15. The Machine Learnerotron! 15 Martin Shepperd numbers answers
- 16. Can we use our metrics to change projects?
- 17. Your views about empirical results? How do you use them? Do you change your SE processes based on these results? What are the issues? Share your views…
- 18. Discussions Correlations do not imply causations Do not confuse causes and symptoms Generalization, samples Cherry-picking, deliberately suppressing other results Beware of fraud Threats to validity Careful use of machine learning (careful comparison with the state of the art)
- 19. How to make findings actionable? An empirical finding is more valuable the more actionable it is… What is the consequence of the result? Should I change things? How? What is the risk of this change? Result should provide for its potential implications. Immediately useful? Requires changes in other aspects? Risk of change?
- 20. Questions?
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