Developers must be able to constantly learn new technologies, adapt to new environments, and overcome challenges when learning and practicing their craft. However, failure to overcome obstacles in these situations can introduce a sense of mounting frustration in developers that can negatively impact learning outcomes. Prior research (by other authors) using bio-metric sensors demonstrated a correlation between bugs (and how long developers are "stuck" in a bug) with feelings and expressions of frustration, anger and toxicity.
- Note that you will be given an existing dataset, cleaned up and organised.
- The datasets are large. Your computer should be able to handle them.
- You will be working with Python, Keras + Tensorflow implementations, plus the BERTs' own implementations.
This project has been completed in S2, 2022.
Te student must have experience with:
- Deep Learning implemented in Python with Keras and Tensorflow
- Setup of CNN, LSTM, BERT models
- Setup of WordEmbeddings
- Ability/hardware to handle a very large dataset (2.1 million commits)
- Demonstrated academic writing skills.
- Excellent attention to details to compare results accross models and across programming languages.
- Empirical Software Engineering and Natural Language Processing
- Mining Software Repositories with Cross-Programming Language Comparison
- Investigation of deep learning and BERT methods
- Deel Learning
- BERT, pre-trained models