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Authors: Kazuhiko Ogawa and Takako Nakatani

Affiliation: The Open University of Japan, 2-11, Wakaba, Mihama-ku, 261-8586, Chiba and Japan

Keyword(s): Deep Learning, Convolutional Neural Network, Program Quality, Fault Prone.

Abstract: There has been a lot of research aimed at improving the quality of software systems. Conventional methods do have the ability to evaluate the quality of software systems with regard to software metrics. (i.e. complexity, usability, modifiability, etc.) In this paper, we apply one of the deep learning techniques, CNN (Convolutional Neural Network), in order to infer the fault proneness of a program. The CNN approach consists of three steps: training, verification of the learning quality, and application. In the first step, in order to make training data, we transformed 27 program source codes into 1490 images with colored elements, so that the features of the images remain. In the second step, we set the goal of the accuracy of machine learning and trained with the training data. In the third step, we forced the trained system to infer the fault proneness of 692 images, which were transformed through 5 programs. This paper presents the effectiveness of our approach for improving the q uality of software systems. (More)

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Paper citation in several formats:
Ogawa, K. and Nakatani, T. (2019). Predicting Fault Proneness of Programs with CNN. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 321-328. DOI: 10.5220/0007704303210328

@conference{hamt19,
author={Kazuhiko Ogawa. and Takako Nakatani.},
title={Predicting Fault Proneness of Programs with CNN},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT},
year={2019},
pages={321-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007704303210328},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: HAMT
TI - Predicting Fault Proneness of Programs with CNN
SN - 978-989-758-350-6
IS - 2184-433X
AU - Ogawa, K.
AU - Nakatani, T.
PY - 2019
SP - 321
EP - 328
DO - 10.5220/0007704303210328
PB - SciTePress