ai-se/best-practice-se-text-mining

Best-Practice-SE-text-mining

For BIGDSE ’16

Notes

• Understand the relevance of SE processes for ML/Big Data

• There has been a constant push for using ML in SE. But, what about SE for ML

• We'd like to explore what SE can teach ML

• Big data and ML practitioners have an variety of tools at their disposal, with the growing size of such requires a validation team.

• Mythical man month - 1/2 of the time is used of testing

• Coding takes only 1/6th the entire time.

• Industrial data mining has taught us that the significance of goal of a certain task

• Key take way: Your goals are not my goals.

DM at LN

• The problem is indeed unique. No dearth of data, but labeling data is quite expensive
• Emulating real world data is hard — forum such as stack exchange can be used to address these issues.
• TAR is primarily a binary classification task.
• StackEx using a site level granularity produces a satisfactory analogy to the real problem in hand.
• Binary classification of this sort is vastly different from other techniques. This enables us to take to shortcuts.
• These lessons are by no means general, we only endeavor to highlight the challenges in industrial data mining.

Structure: feel free to modify this

Abstract

Introduction

• Motivations and background
• Description of Data
• Related works

Technology Assisted Review

1. My goals are not your goals.

2. My data isn't your data

3. Describe Prec/Recall and their importance

4. StackEx data

5. Prevelance

6. Sampling - Stratified sampling, Unequal Sampling

7. Big data sometimes isn't

8. Challenges in EDISC: See p13 of the refernce.

Experiments

• Best Decision
• All other decisons comparing with the best one

Discusions

• some words to justify the best decision
• lessons learnt from this project
• The role of validation teams:
  • Mining large industrial data has signification lessons that can be learnt from SE practices
• validity threats

Conclusion

best-practice-se-text-mining's Issues

Experiments: Comparison of results - the validation part

Discussion1: some words to justify the best decision

Method

Discussion2: lessons learnt

Discussion3: validity threats

Abstract and Conclusion

Introduction3: related works

Camera Ready Updates

• Section 3
• Reviewer 2
• Reviewer 3

Introduction2: Data Description

Describe why we are using StackExchange Data, how is it like.
Especially for SExx data sets, how we get it, the characteristic of the data sets.

Data sets will be used in this paper:

tex drupal academia apple gamedev rpg english electronics physics scifi SE0 SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 SE11 SE12 SE13 SE14

Introduction1: motivations, backgrounds...