Analyzing Big Data's Weakest Link (hint: it might be you)
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The document discusses the role of human involvement versus systems automation in big data analysis, emphasizing the challenges posed by human error despite advances in algorithms and computing power. It highlights research on software analytics and defect prediction, suggesting that expertise can be identified through data mining techniques that analyze programmer behavior and coding history. The text also outlines a strategy for clustering developers to enhance programming efficiency and community collaboration.
![Software analytics results:
learn predictors for expertise
● “...counts of the cumulative number of different
developers changing a file over its lifetime can help
to improve defect predictions…”[1]
● “Quantify person's experience with a part of code
using change history of the code”[2]
● “RevFinder, a file location-based code-reviewer
recommendation approach” [3]
● “30% of its code entities has more than 0.3 of
similarity with at least one developer vocabulary”
[4]
17
[1] Ostrand, Thomas J., Elaine J. Weyuker, and Robert M. Bell.
"Programmer-based fault prediction." Proceedings of the 6th
International Conference on Predictive Models in Software Engineering.
ACM, 2010.
[2] Mockus, Audris, and James D. Herbsleb. "Expertise browser: a
quantitative approach to identifying expertise." Proceedings of the
24th international conference on software engineering. ACM, 2002.
[3] Thongtanunam, Patanamon, et al. "Who should review my code? A
file location-based code-reviewer recommendation approach for
Modern Code Review."Software Analysis, Evolution and Reengineering
(SANER), 2015 IEEE 22nd International Conference on. IEEE, 2015.
[4] Santos, Katyusco de F., Dalton DS Guerrero, and Jorge CA de
Figueiredo. "Using Developers Contributions on Software Vocabularies
to Identify Experts."Information Technology-New Generations (ITNG),
2015 12th International Conference on. IEEE, 2015.](https://image.slidesharecdn.com/day1timmenzies-151006211806-lva1-app6891/85/Analyzing-Big-Data-s-Weakest-Link-hint-it-might-be-you-17-320.jpg)
Analyzing Big Data's Weakest Link (hint: it might be you)
- 1. Big Data: the weakest link Vivek Nair, Tim Menzies {vivekaxl,tim.menzies}@gmail.com HPCC Eng. Summit - Sept 29, 2015
- 2. Where is the weakest link? 2
- 3. Where is the weakest link? 3
- 4. Where is the weakest link? 4
- 5. Where is the weakest link? 5
- 6. Where is the weakest link? 6
- 7. Premise of Big Data Analysis is a “systems” task? • Better conclusions = same algorithms + more data + more cpu • If so, then … – No role for human error – All insight is auto-generated from CPUs. Analysis is a “human” task? • Current results on “software analytics” – A human-intensive process 7
- 8. Q: Is Big Data a “Systems” or “Human”-task? A: Yes 8
- 9. Code used in my last paper (1100 LOC of Python calling scikitlearn) 9
- 10. Use a Higher-Level languages? • ECL solves this problem? • But if you can write it quick, – you can write it wrong, quick. 10
- 11. Is this really a problem? • Q: What would we expect to see if… – Top experts, publishing in top journals – Many of the same data sets – 8 years of trying • A: – Perhaps some upward progress – Perhaps a little less variance 11 So, what do we see?
- 12. • Software analytics – Defect prediction – Many of the same learners, – Many of the same data sets • 42 papers, top journals, • 23 author groups • 2002 to 2010 • Y-axis measures mean performance 12 Researcher Bias: The Use of Machine Learning in Software Defect Prediction, Martin Shepperd, David Bowes, and Tracy Hall, IEEE TRANS on Soft. Eng. , 40(6), JUNE 2014
- 14. A little theory • James D. Herbsleb, CMU • Socio-Technical Coordination • A predictor for higher defects: – Groups of programmers working on similar functions then, – but do not sharing that expertise 14
- 15. Q: How to find expertise groups within the HPCC community? A: using data mining 15
- 16. Static features and commit history can act as a cue for expertise ● Our motivation o “relation between embodiment and language acquisition by locating the ‘minimal set of necessary features’ that enable language of any kind to be learned” - The Philosophy of Expertise 16
- 17. Software analytics results: learn predictors for expertise ● “...counts of the cumulative number of different developers changing a file over its lifetime can help to improve defect predictions…”[1] ● “Quantify person's experience with a part of code using change history of the code”[2] ● “RevFinder, a file location-based code-reviewer recommendation approach” [3] ● “30% of its code entities has more than 0.3 of similarity with at least one developer vocabulary” [4] 17 [1] Ostrand, Thomas J., Elaine J. Weyuker, and Robert M. Bell. "Programmer-based fault prediction." Proceedings of the 6th International Conference on Predictive Models in Software Engineering. ACM, 2010. [2] Mockus, Audris, and James D. Herbsleb. "Expertise browser: a quantitative approach to identifying expertise." Proceedings of the 24th international conference on software engineering. ACM, 2002. [3] Thongtanunam, Patanamon, et al. "Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review."Software Analysis, Evolution and Reengineering (SANER), 2015 IEEE 22nd International Conference on. IEEE, 2015. [4] Santos, Katyusco de F., Dalton DS Guerrero, and Jorge CA de Figueiredo. "Using Developers Contributions on Software Vocabularies to Identify Experts."Information Technology-New Generations (ITNG), 2015 12th International Conference on. IEEE, 2015.
- 18. Q: And what data mining suite will we use to mine data about programmers? • A: need you ask? 18
- 19. Source Code 19
- 20. But what are we clustering? Developer products • Lightweight parsing of source code • Developers profiles, accessed via social media sites
- 21. Languages Used
- 22. Skill Set (self promotion)
- 23. Data processing 1. Github repos (for code) ➔ Social media(for years of work) 2. Static code analysis: frequency counts of AST features (e.g. count loops, returns, var comparisons, map, etc ) 3. Bayes classifier Early career Later career
- 24. Classification - Features: Nodes of AST - Algorithms Used: Simple Cart, Random Forest, Naive Bayes etc. - Can distinguish expert from novice programmers •precision= 78% early career •precision = 74% later career * Using Weka
- 25. Current status The good news • Can auto-find groups of better programmers • Can do that for very large data sets – The ECL advantages The other news • Seeking larger data sets • Talking to HackerRank • Looking at ways to instrument the HPCC forums – Matchmaker tools – Affinity groups 25
- 26. Where is the weakest link? 26
- 27. Where is the weakest link? 27
- 28. We can make that link stronger 28
- 29. Acknowledgements: Thanks to funding from LexisNexis 29