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» Research » Student research projects » Testing Technical Debt in Data Science Software (HDR) [Open]Testing Technical Debt in Data Science Software (HDR) [Open]
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Description
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.
Requirements
- 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/
Keywords
- Empirical Software Engineering
- Scientific Software / Data Science Software
- Technical Debt / Smells
- Mining Software Repositories
- Mixed Methods, Developers Surveys
