Predicting Fault Proneness of Programs with CNN

Kazuhiko Ogawa, Takako Nakatani

2019

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 quality of software systems.

Paper Citation

in Harvard Style

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, pages 321-328. DOI: 10.5220/0007704303210328

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Ogawa K.
AU - Nakatani T.
PY - 2019
SP - 321
EP - 328
DO - 10.5220/0007704303210328