Treffer: Code Nano-Pattern Detection using Deep Learning

Title:
Code Nano-Pattern Detection using Deep Learning
Authors:
Jitendra Singh Thakur, Atul Gupta, Anubhav Trivedi
Source:
Proceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference). :1-6
Publisher Information:
ACM, 2020.
Publication Year:
2020
Subject Terms:
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Document Type:
Fachzeitschrift Article
DOI:
10.1145/3385032.3385050
Access URL:
https://dl.acm.org/doi/10.1145/3385032.3385050
https://dblp.uni-trier.de/db/conf/indiaSE/isec2020.html#TrivediTG20
Rights:
URL: https://www.acm.org/publications/policies/copyright_policy#Background
Accession Number:
edsair.doi.dedup.....1082bc68e052d2374f8cd1a10717510d
Database:
OpenAIRE

Weitere Informationen

Nano-patterns are the method-level code building blocks of the code which can reveal crucial information of the code. In this paper, we present some initial results of our investigation to detect nano-patterns in a Java code using a deep learning approach. For this purpose, first, we generated a method level tagged corpus for 15 nano-patterns using nine open source Java projects. Subsequently, the tagged corpus was used to train a Long Short-Term Memory (LSTM) network to predict the nano-patterns present in the Java code. Our deep learning model gave an average accuracy of 88.3% with an average precision of 74.4% and average recall of 78.3%.