big data: the weakest link
TRANSCRIPT
Big Data: the weakest link
Vivek Nair, Tim Menzies{vivekaxl,tim.menzies}@gmail.comHPCC Eng. Summit - Sept 29, 2015
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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
Use a Higher-Level languages?
• ECL solves this problem?
• But if you can write it quick, – you can write it wrong, quick.
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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
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So, what do we see?
• 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
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
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Static features and commit history can act as a cue for expertise
● Our motivationo “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
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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]
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[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.
But what are we clustering?Developer products
• Lightweight parsing of source code • Developers profiles, accessed via LinkedIn
Data processing1. Github repos (for code) Linkedin (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
Earlycareer
Later career
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
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
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