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hlibbabii edited this page Feb 5, 2019 · 3 revisions

Introduction

Why does my research matter? research question

Goal?

Approach

Structure of the thesis

Logically our research can be divided into three big parts:

  • studying source code vocabulary modeling approaches;
  • training of a language model;
  • building classifiers to solve support of logging activities tasks.

These three parts are tightly connected to each other, namely, the output of the one is used as input of the next one. Having identified best approaches for vocabulary building we contribute to building of a high-performing language model that can be trained in a reasonable time. Having a pretrained language model, we can use it for training classifiers following transfer learning techniques.

  1. Introduction
  2. State of the art
  3. Background information
    1. Brief Introduction to language modeling
    2. Brief Introduction to Neural Networks and Deep Learning
    3. Using Recurrent Neural Networks for Language Modeling
    4. Long Short Term Memory Networks
  4. Vocab modeling
    1. Data Gathering and Tokenization
    2. Reducing Vocabulary Size
    3. Preprocess Configurations
    4. Vocabulary modeling evaluation
  5. Language modeling
    1. Neural network architecture
    2. Neural Network Training
    3. Subword aware accuracy and entropy
    4. Neural Cache
    5. Language modeling evaluation
  6. Building classifiers
    1. Generation of labeled data for classification
    2. Transfer learning
    3. Classification parameter hypersearch
    4. Classifier evaluation
  7. Discussion
  8. Future work
  9. Conclusion
  10. Appendix A. Preprocessing Configurations Examples
  11. Appendix B. Subword Representation Examples
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