Improving the Comprehension of R Programs by Hybrid Dataflow Analysis

Abstract

Context Comprehending code is crucial in all areas of software development, with many existing supporting tools and techniques for various languages. However, for R, a widely used programming language, especially in the field of statistical computing, the support is limited. R offers a large number of packages as well as dynamic features, which make it challenging to analyze and understand. Objective We aim to (i) gain a better understanding of how R is used in the real world, (ii) devise better analysis strategies for R, which are able to handle its dynamic nature, and (iii) improve the comprehension of R scripts by using these analyses, providing new methods and procedures applicable to program comprehension in general. Method In eight contributions, we analyze feature usage in R scripts, develop a new static dataflow analysis intertwining control and dataflow, and more. We enable and propose new techniques for program comprehension using a combination of static and dynamic analysis.

BibTeX

@inproceedings{Sihler:ASE24,
  author    = {Florian Sihler},
  title     = {Improving the Comprehension of R Programs by Hybrid Dataflow Analysis},
  booktitle = {ASE},
  pages     = {2490--2493},
  publisher = {{ACM}},
  year      = {2024},
}

Improving the Comprehension of R Programs by Hybrid Dataflow Analysis

Abstract

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