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Code Nano-Pattern Detection using Deep Learning

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ISEC '20: Proceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)

Article No.: 16, Pages 1 - 6

https://doi.org/10.1145/3385032.3385050

Published: 25 March 2020 Publication History

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Abstract

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%.

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Cited By

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Shafiq SMayr‐Dorn CMashkoor AEgyed A(2024)Balanced knowledge distribution among software development teams—Observations from open‐ and closed‐source software developmentJournal of Software: Evolution and Process10.1002/smr.2655Online publication date: 13-Feb-2024
https://doi.org/10.1002/smr.2655
Shafiq SMashkoor AMayr-Dorn CEgyed A(2021)A Literature Review of Using Machine Learning in Software Development Life Cycle StagesIEEE Access10.1109/ACCESS.2021.31197469(140896-140920)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3119746

Index Terms

Code Nano-Pattern Detection using Deep Learning
1. Computing methodologies
  1. Machine learning

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Published In

ISEC '20: Proceedings of the 13th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference)

February 2020

166 pages

ISBN:9781450375948

DOI:10.1145/3385032

General Chairs:
Sanjeev Jain
IIITDM Jabalpur, India
,
Atul Gupta
IIITDM Jabalpur, India
,
Program Chairs:
David Lo
SMU, Singapore
,
Diptikalyan Saha
IBM Research. India
,
Publications Chair:
Richa Sharma
Lock Haven University, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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iSOFT: iSOFT
SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

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Published: 25 March 2020

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ISEC 2020

ISEC 2020: 13th Innovations in Software Engineering Conference

February 27 - 29, 2020

Jabalpur, India

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Shafiq SMayr‐Dorn CMashkoor AEgyed A(2024)Balanced knowledge distribution among software development teams—Observations from open‐ and closed‐source software developmentJournal of Software: Evolution and Process10.1002/smr.2655Online publication date: 13-Feb-2024
https://doi.org/10.1002/smr.2655
Shafiq SMashkoor AMayr-Dorn CEgyed A(2021)A Literature Review of Using Machine Learning in Software Development Life Cycle StagesIEEE Access10.1109/ACCESS.2021.31197469(140896-140920)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3119746

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Automated detection of cardiac arrhythmia using deep learning techniques

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