Testing Technical Debt in Data Science Software (HDR) [Open]
This project will continue existing work in the area of Test Technical Debt for datascience/scientific programming (thus, also extending existing knowledge to functional programming, and dynamically-typed programming languages).
You will be conducting systematic literature reviews, mining software repositories, and using mixed-methods.
- You must hold an Honours Degree First Class
- Previous experience with programming languages (ideally, both Python and R). Coding and programming is an essential skill for this position.
- Writing academic papers presented at student conferences.
M. Vidoni, "Evaluating Unit Testing Practices in R Packages," 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), 2021, pp. 1523-1534, https://doi.org/10.1109/ICSE43902.2021.00136
Further papers can be found at: https://melvidoni.rbind.io/project/2020-rse/
- Empirical Software Engineering
- Scientific Software / Data Science Software
- Technical Debt / Smells
- Mining Software Repositories
- Mixed Methods, Developers Surveys