Week 1 (Sep 14): Introduction and Course Overview
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Week 2 (Sep 21): MSR Tutorial
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Week 3 (Sept 28): Predicting Bugs
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Predicting fault incidence using software change history
Todd L. Graves, Alan F. Karr, J. S. Marron, and Harvey P. Siy
Analysis Techniques: Basic linear regression, GLM, R2, model error, exponential decay
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Predictors of customer perceived software quality
Audris Mockus, Ping Zhang, and Paul Luo Li
Analysis Techniques: Classification, Logistic Regression (Building and Interpreting Co-efficients), R2, model error
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Predicting Defects for Eclipse
Thomas Zimmermann, Rahul Premraj, and Andreas Zeller
Analysis Techniques: Using R, Classification, Ranking
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[READING]
[ASSIGNMENT]
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How, and Why, Process Metrics are better
Foyzur Rahman and Premkumar Devanbu
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[READING]
ASSIGNMENT
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Predicting Bugs from History
Thomas Zimmermann, Nachiappan Nagappan, and Andreas Zeller
(Evolution Book)
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[READING]
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Week 4 (Oct 5): Mining Social Structures and Code Reviews
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Will My Patch Make it? and How Fast?: Case Study on the Linux Kernel.
Yujuan Jiang, Bram Adams, Daniel M. German
Analysis Techniques: Decision Tree
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An Empirical Study of the Impact of Modern Code Review Practices on Software Quality
Shane McIntosh, Yasutaka Kamei, Bram Adams, and Ahmed E. Hassan
Analysis Techniques: Bootstrap validation
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ASSIGNMENT
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On the Role of Developer's Scattered Changes in Bug Prediction
Dario Di Nucci, Fabio Palomba, Sandro Siravo, Gabriele Bavota, Rocco Oliveto, and Andrea De Lucia
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Does Distributed Development
Affect Software Quality?
An Empirical Case Study
of Windows Vista
Christian Bird, Nachiappan Nagappan, Premkumar Devanbu, Harald Gall, and Brendan Murphy
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[READING]
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Week 5 (Oct 12): Thanksgiving (No Class)
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Week 6 (Oct 19): Mining of Non-Structured Data
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Assignment Status Update -- OCT 16 (10 min presentation)
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Week 7 (Oct 26): Assignment Presentation
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Assignment DUE -- OCT 26 (20 mins presentation + 10 page IEEE report)
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Project Proposal DUE -- Oct 30 (2 pages IEEE format)
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Week 8 (Nov 2): Project Proposal Presentations
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Project Proposal Presentation (10 mins + 10 mins questions)
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Week 9 (Nov 9): Mining Mobile Apps
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Week 10 (Nov 16): Large Scale Analysis I
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Improving Software Diagnosability via Log Enhancement
Ding Yuan, Jing Zheng, Soyeon Park, Yuanyuan Zhou, and Stefan Savage
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The Promises and Perils of Mining Github
Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M. German, Daniela Damian
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Towards Building a Universal Defect Prediction Model
Feng Zhang, Audris Mockus, Iman Keivanloo, Ying Zou: Towards building a universal defect prediction model.
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[READING]
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Bugs as deviant behavior: A general approach to inferring errors in systems code
Dawson Engler, David Yu Chen, Seth Hallem, Andy Chou, and Benjamin Chelf
Analysis Techniques: Markov Models
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[READING]
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Scalable statistical bug isolation
Ben Liblit, Mayur Naik, Alice X. Zheng, Alex Aiken, and Michael I. Jordan
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[READING]
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Week 11 (Nov 23): Large Scale Analysis II
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Capturing, indexing, clustering, and retrieving system history
Ira Cohen, Steve Zhang, Moises Goldszmidt, Julie Symons, Terence Kelly, and Armando Fox
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vPerfGuard: an Automated Model-Driven Framework for
Application Performance Diagnosis in Consolidated
Cloud Environment
Pengcheng Xiong, Calton Pu, Xiaoyun Zhu, and Rean Griffith
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Performance Debugging in the Large via Mining Millions of Stack Traces
Shi Han, Yingnong Dang, Song Ge, Dongmei Zhang, and Tao Xie |
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Amassing and indexing a large sample of version control systems: towards the
census of public source code history
Audris Mockus
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[READING]
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Week 12 (Nov 30): Project Presentations
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Project Presentation DUE -- Nov 30 (20 mins presentation)
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Project Report DUE -- DEC 22 (10 page IEEE report)
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