loading
Documents

Research.Publish.Connect.

Paper

Authors: Thomas Karanikiotis ; Michail Papamichail ; Ioannis Gonidelis ; Dimitra Karatza and Andreas Symeonidis

Affiliation: Electrical and Computer Engineering Dept., Aristotle University of Thessaloniki, Intelligent Systems & Software Engineering Labgroup, Information Processing Laboratory, Thessaloniki, Greece

ISBN: 978-989-758-443-5

Keyword(s): Developer-perceived Readability, Readability Interpretation, Size-based Clustering, Support Vector Regression.

Abstract: In the context of collaborative, agile software development, where effective and efficient software maintenance is of utmost importance, the need to produce readable source code is evident. Towards this direction, several approaches aspire to assess the extent to which a software component is readable. Most of them rely on experts who are responsible for determining the ground truth and/or set custom evaluation criteria, leading to results that are context-dependent and subjective. In this work, we employ a large set of static analysis metrics along with various coding violations towards interpreting readability as perceived by developers. In an effort to provide a fully automated and extendible methodology, we refrain from using experts; rather we harness data residing in online code hosting facilities towards constructing a dataset that includes more than one million methods that cover diverse development scenarios. After performing clustering based on source code size, we employ Su pport Vector Regression in order to interpret the extent to which a software component is readable on three axes: complexity, coupling, and documentation. Preliminary evaluation on several axes indicates that our approach effectively interprets readability as perceived by developers against the aforementioned three primary source code properties. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.94.21.209

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Karanikiotis, T.; Papamichail, M.; Gonidelis, I.; Karatza, D. and Symeonidis, A. (2020). A Data-driven Methodology towards Interpreting Readability against Software Properties.In Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-443-5, pages 61-72. DOI: 10.5220/0009891000610072

@conference{icsoft20,
author={Thomas Karanikiotis. and Michail D. Papamichail. and Ioannis Gonidelis. and Dimitra Karatza. and Andreas L. Symeonidis.},
title={A Data-driven Methodology towards Interpreting Readability against Software Properties},
booktitle={Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2020},
pages={61-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009891000610072},
isbn={978-989-758-443-5},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - A Data-driven Methodology towards Interpreting Readability against Software Properties
SN - 978-989-758-443-5
AU - Karanikiotis, T.
AU - Papamichail, M.
AU - Gonidelis, I.
AU - Karatza, D.
AU - Symeonidis, A.
PY - 2020
SP - 61
EP - 72
DO - 10.5220/0009891000610072

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.