Title
A machine learning based automatic folding of dynamically typed languages.
Abstract
The popularity of dynamically typed languages has been growing strongly lately. Elegant syntax of such languages like javascript, python, PHP and ruby pays back when it comes to finding bugs in large codebases. The analysis is hindered by specific capabilities of dynamically typed languages, such as defining methods dynamically and evaluating string expressions. For finding bugs or investigating unfamiliar classes and libraries in modern IDEs and text editors features for folding unimportant code blocks are implemented. In this work, data on user foldings from real projects were collected and two classifiers were trained on their basis. The input to the classifier is a set of parameters describing the structure and syntax of the code block. These classifiers were subsequently used to identify unimportant code fragments. The implemented approach was tested on JavaScript and Python programs and compared with the best existing algorithm for automatic code folding.
Year
DOI
Venue
2019
10.1145/3340482.3342746
MaLTeSQuE@ESEC/SIGSOFT FSE
Keywords
DocType
ISBN
Dynamically typed languages, JavaScript, Python, Automatic Folding, Source code analysis, Abstract Syntax tree
Conference
978-1-4503-6855-1
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Nickolay Viuginov100.34
Andrey Filchenkov24615.80