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Heterogeneous tree structure classification to label Java programmers according to their expertise level
طبقه بندی ساختار درخت ناهمگن به برچسب برنامه نویسان جاوا با توجه به سطح تخصص آنها-2020 Open-source code repositories are a valuable asset to creating different kinds of tools and services,
utilizing machine learning and probabilistic reasoning. Syntactic models process Abstract Syntax Trees
(AST) of source code to build systems capable of predicting different software properties. The main
difficulty of building such models comes from the heterogeneous and compound structures of ASTs,
and that traditional machine learning algorithms require instances to be represented as n-dimensional
vectors rather than trees. In this article, we propose a new approach to classify ASTs using traditional
supervised-learning algorithms, where a feature learning process selects the most representative
syntax patterns for the child subtrees of different syntax constructs. Those syntax patterns are used
to enrich the context information of each AST, allowing the classification of compound heterogeneous
tree structures. The proposed approach is applied to the problem of labeling the expertise level of
Java programmers. The system is able to label expert and novice programs with an average accuracy
of 99.6%. Moreover, other code fragments such as types, fields, methods, statements and expressions
could also be classified, with average accuracies of 99.5%, 91.4%, 95.2%, 88.3% and 78.1%, respectively. Keywords: Big code | Machine learning | Syntax patterns | Abstract syntax trees | Programmer expertise | Decision trees | Big data |
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