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.

Note: I have a fully-unded MPhil or PhD positiion available with this project. The commencement is immediate as the successful applicant will be directed for immediate commencement.


  • 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.
How to apply:
Please read the instructions on my website, in the section "Graduate's Recruitment": https://melvidoni.rbind.io/students/
Note that women and female-identifying candidates are particularly encouraged to apply.

Background Literature

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


Updated:  10 August 2021/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing