- Dr Huaming Chen, University of Sydney
Applying machine learning algorithms to source code related SE task is rapidly developing and attracts the attention from both researchers and industry engineers. While there are many program languages available, applying such techniques, i.e., the representation learning models, for different languages may achieve different performance. Particularly, they all have their own strict syntax, which determines the abstract syntax tree. Thus, a lot of different open-source supply chain are available, for example the parsing tools are used to build AST from source code.
In this project, we will investigate the impact of such tools from open-source supply chain on the ML4SE task quality.
In this project, you will conduct an investigation towards the analysis of open-source supply chain for ML4SE tasks and explore the impact of such related prominent tools on performance evaluation. The project will:
- investigate the open-source supply chain on ML4SE tasks.
- understand the impact of the tools on ML4SE tasks.
You will be expected to work with Dr. Yi Lu (QUT) and Dr. Huaming Chen (USYD) for this project.
- identify the available tools on key steps for ML4SE tasks
- build the evaluation framework, including dataset and models, for ML4SE tasks.
Skills and experience
- knowledge of data mining and machine learning
- knowledge of software analysis, such as static software analysis
- good programming skills.
You may be eligible to apply for a research scholarship.
Contact the supervisor for more information.