Class Schedule and Reading List

	Lectures	Readings and Presentations
Week 2 1/16 (MLK) 1/18	Software Analytics---What is it? Data Scientists---Who are they?	Interactions with Big Data Analytics, Fisher et al. ACM Interactions 2012 The Emerging Role of Data Scientists on Software Development Teams, Kim et al. ICSE' 16
Week 3 1/23 1/25	Change Recommendation Automated Software Repair	Mining Version Histories to Guide Software Changes, Zimmermann et al. ICSE 2004 Automatically Finding Patches using Genetic Programming, Weimer et al. ICSE 2009
Week 4 1/30 2/1	Defect Prediction	Use of Relative Code Churn Measures to Predict System Defect Density, Nagappan and Ball, ICSE 2005  Cross Project Defect Prediction, Zimmermann et al, ESEC FSE 2009 *Please Read Chapter 12 and 13 of "Probability and Statistics for Engineering and the Sciences." Project Proposal Description Due on 1/31 11:59PM
Week 5 2/6 2/8	Software Anomaly Detection and Debugging	Bug Isolation via remote program sampling, Liblit et al. PLDI 2003 Detecting Object Usage Anomalies, Wasylkowski et al. FSE 2007
Week 6 2/13 2/15	Software Anomaly Detection and Debugging	FaultTracer: a spectrum-based approach to localizing failure-inducing program edits, Zhang et al. JSEP 2013. Debugging in the (Very) Large: Ten Years of Implementation and Experience, Glerum et al. SOSP 2009
Week 7 2/20 (President Day) 2/22	Hadoop and MapReduce	MapReduce: Simplified Data Processing on Large Clusters, Dean and Ghematwat, OSDI 2004 Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing, Zaharia et al. NSDI 2012
Week 8 2/27 3/1	Big Data Analytics Assistance--Debugging	Assisting Developers of Big Data Analytics Applications When Deploying on Hadoop Clouds, Shang et al. ICSE 2013 BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark, Gulzar et al. ICSE 2016
Week 9 3/6 3/8	Big Data Analytics Assistance--Debugging	Inspector Gadget: A Framework for Custom Monitoring and Debugging of Distributed Dataflows, Olston and Reed, VLDB 2011 Scalable Lineage Capture for Debugging DISC Analytics, Logothetis et al. SoCC 2013 Titian: Data Provenance Support in Spark, Interlandi et al. Optimizing Interactive Development of Data-Intensive Applications, Tetali et al.
Week 10 3/13 3/15	Buffers	Final Open Book Exam 3/15 12-2PM in Class
Final Week	Project final presentation on 3/21 and/or 3/22 for individual scheduling.	Project final report is due on 3/20 11:59PM

Prerequisite Reading Materials

Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil & Rachel Schutt (Find an E-book on "Doing Data Science" http://proquest.safaribooksonline.com/9781449363871). Linear Regression: Pages 55-71 (Chapter 3: Algorithms)
Logistics Regression and Logit Function: Chapter 3: Logistics Regression Pages 113- 134, Decision Tree and Entropy: Chapter 7, Pages 185- 187

"Probability and Statistics for Engineering and the Sciences."--Chapter 12 "Simple and Linear Regression and Correlation" and Chapter 13 "Nonlinear and Multiple Regression" share background on linear regression, step wise regression, R^2, Adjusted R^2, SSE and SST. There are multiple copies of the book at UCLA library.
 
http://catalog.library.ucla.edu/vwebv/holdingsInfo?searchId=6117&recCount=50&recPointer=11&bibId=59896

R Cookbook, The E-version of R Cookbook is available at UCLA library. Read Chapter 9

Grading

Class Co-Teaching Presentation and Demonstrations: 40% (Tool 20% and Paper 20%)
Project Final Report, Presentation, and Demo: 30%
Reading Questions and Discussion Participation (including Course Evaluation Participation): 10%
Open Book Final Exam: 20% 

Tool Demonstrations and Co-Teaching

Part A. Tool Demonstration: This class will emphasize the use of tools for big software data analytics. Each team will create a set of toy examples, on-line tutorials, and live in-class demonstration to teach tools used for big software data analytics. Example tools could include ASM Bytecode Instrumentation Toolkit, AspectJ, R, R Studio, Hadoop, Spark, etc. The should submit the teaching materials 5 days before the date you will present. When providing your teaching material, please make your tutorial online and email its URL to the instructor. You are welcome to consult me on the choice of your tool or technology. Each presentation should be about 15 minutes long. The presentation can be done in a team of 2 people (or occasionally 3).

Part B. Paper Presentation: Each person will select one paper to discuss recent advances related to the lecture's topic. For example, in Week 5, the class discussion topic is automated debugging and the student will select a state-of-the-art recent papers (ideally in the last two to three years) on the topic of debugging practices and automated debugging support. The student will perform critical reading of the paper and assess the technology and concept. The student will then present their findings and analysis to complement my lecture and class discussion, which are based on seminal papers in each topic. You are welcome to consult me on the choice of your paper. Each presentation should be about 15 minutes long. The presentation will be done individually.

Please sign up your name for tool demo and paper presentations. The sign up schedule is available at http://tinyurl.com/cs239winter2017

Project Assignment

The course project will involve hands-on-experience of developing big data software analytics. A project could be a replication of an existing idea, a proposal of a new research project, a translation of a methodology to a new problem domain, or assessment of an existing technique via case studies or controlled experiment. For your final presentation, you are required to perform live demonstration of your project in class. The idea does not have to be novel. The goal of this project is to gain hands on experience by re-implementing, re-producing, and combining the ideas of research papers that we read in class. The project teams should be formed in a group of 3-5 people.

Project Proposal Description Due on 1/31 11:59PM
Project Final Report Due on 3/20 11:59PM

Your report should be structured like a conference paper, meaning that your report should contain:

Abstract
A well-motivated introduction
Related work with proper citations
Description of your methodology
Evaluation results
Discussion of your approach, threats to validity, and additional experiments
Conclusions and future work
Appendix: Describe how to run and test your implementation.

If you are doing a project that involves implementation, please submit your source code by sharing an on-line repository. Please describe how to run and test your code in your report.

Here's grading guidelines for your project report.
Motivation & Problem Definition
Does this report sufficiently describe motivation of this project?
Does this report describe when and how this research can be used by whom in terms of examples and scenarios?
Does the report clearly define a research problem?
Related Work
Does the report adequately describe related work?
Does the report cite and use appropriate references?
Approach
Does the report clearly & adequately present your research approach (algorithm description, pseudo code, etc.)?
Does the report include justifications for your approach?
Evaluation
Does this report clarify your evaluation’s objectives (research questions raised by you)?
Does this report justify why it is worthwhile to answer such research questions?
Does this report concretely describe what can be measured and compared to existing approaches (if exist) to answer such research questions?
Is the evaluation study design (experiments, case studies, and user studies) sound?
Results
Does the report include empirical results that support the author’s claims/ research goals?
Does the report provide any interpretation on results?
Is the information in the report sound, factual, and accurate?
Discussions & Future Work
Does the report suggest future research directions or make suggestions to improve or augment the current research?
Did the report demonstrate consideration of alternative approaches? Does the report discuss threats to validity of this evaluation?
Clarity and Writing
Is the treatment of the subject a reasonable scope as a class project?
How well are the ideas presented? (very difficult to understand =1, very easy to understand =5)
Overall quality of writing and readability (very poor =1, excellent =5)

Reading Questions

Class Discussion: Think-Pair-Share

How Does It Work?
1) Think. The teacher provokes students' thinking with a question or prompt or observation. The students should take a few moments (probably not minutes) just to THINK about the question.

2) Pair. Using designated partners (such as with Clock Buddies), nearby neighbors, or a deskmate, students PAIR up to talk about the answer each came up with. They compare their mental or written notes and identify the answers they think are best, most convincing, or most unique.

3) Share. After students talk in pairs for a few moments (again, usually not minutes), the teacher calls for pairs to SHARE their thinking with the rest of the class. She can do this by going around in round-robin fashion, calling on each pair; or she can take answers as they are called out (or as hands are raised). Often, the teacher or a designated helper will record these responses on the board or on the overhead

Homework, project report, and presentation grading scheme

• 5 pt: Excellent design, complete implementation, selection of a task that is highly intellectually challenging, creative, concise yet comprehensive writing, nearly perfect answers, beautifully written and verbally communicated, eloquent presentation within a time limit
• 4 pt: Very good, mostly correct answers, i.e., >85%, selection of a intellectually challenging project topic, well written, good verbal presentation, well practiced presentation within a time limit
  
• 3 pt: Good understanding of the key concepts, mostly correct answers, i.e., >70%, selection of a intellectually challenging project topic, well written, good verbal presentation, well practiced presentation within a time limit
• 2 pt or 1pt: Poor, shallow, minimally sufficient, or needlessly wordy, key concepts misunderstood or missing, selection of easy project tasks, poor written and verbal communication, presentation over time, not following specified formats

Class Policy