Improving the Comprehension of R Programs by Hybrid Dataflow Analysis

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...

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Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 2490 - 2493
1. Verfasser: Sihler, Florian
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 27.10.2024
Schlagworte:
Codes
Computational statistics
Computer languages
Doctoral Thesis
Hybrid Analysis
Performance analysis
Program Comprehension
Software
Software development management
Software engineering
Static analysis
ISSN:2643-1572
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Zusammenfassung: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.CCS CONCEPTS* Theory of computation → Program analysis; * Software and its engineering → Automated static analysis; Dynamic analysis.
ISSN:2643-1572
DOI:10.1145/3691620.3695603