Qualitative Coding of Software Engineering Artifacts, Case Study Research, Natural Language Processing Pipelines and Lexicons, Text Similarity Measures,  Information Extraction and Classification, Clustering, Supervised Machine Learning, Quantitative Evaluation of NLP and ML Techniques, Deep Learning, Word Embeddings, Transformers

Documents

Lecture Notes and Assignments

Lecture slides, assignments and all associated artifacts will be made available on BrightSpace. 

Reading List

• • Please see the Reading List.

Questions and Discussions

We are using Piazza for class discussions. The system is meant to get you help fast and efficiently from classmates and myself. 

Please create an account and enrol as soon as possible.

Lecture Plan

September 15: 

• Introduction and Practical Matters

September 22:

• Empirical Software Engineering (Case Studies, Qualitative Methods)

• Background Reading:

  • Experimentation in Software Engineering (Chapter 2: Empirical Strategies)

  • Guidelines for conducting and reporting case study research in software engineering

  • The Coding Manual for Qualitative Researchers (Chapter 1: An Introduction to Codes and Coding)

September 29:

• Empirical Software Engineering (Quantitative Methods)

• Background Reading: 

  • • Experimentation in Software Engineering (Chapter 10: Analysis and Interpretation)

October 6:

• The Natural Language Processing Pipeline and Lexicons

• Background  Reading:

  • • --

October 13:

• Clustering and Topic Modelling

  • Background  Reading:

    • Chapter 7 (Cluster Analysis: Basic Concepts and Algorithms) from Introduction to Data Mining (2nd Ed.)

October 20:

• Machine Learning (A Whirlwind Tour, Input, Output)

• Background Reading:

  • • • Chapters 1-4 of Data Mining: Practical Machine Learning Tools and Techniques

October 27:

• Fall 2023 Reading Week (no class)

November 3:

• Deep Learning (Part 1) - Guest Lecture by Mo H. Amini

November 10:

• Deep Learning  (Part 2) - Guest Lecture by Mo H. Amini

November 17:

• Evaluation of Machine Learning Techniques

• Background  Reading:

  • • Chapters 5 of Data Mining: Practical Machine Learning Tools and Techniques

• Project-progress presentations

  • Schedule to be announced.

November 24:

• Project-progress presentations

  • Schedule to be announced.

December 1:

• Project-progress presentations and wrap-up

  • Schedule to be announced.