Assessing the differences of clone detection methods used in the fault-prone module prediction

Masateru Tsunoda, Yasutaka Kamei, Atsushi Sawada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

We have investigated through several experiments the differences in the fault-prone module prediction accuracy caused by the differences in the constituent code clone metrics of the prediction model. In the previous studies, they use one or more code clone metrics as independent variables to build an accurate prediction model. While they often use the clone detection method proposed by Kamiya et al. to calculate these metrics, the effect of the detection method on the prediction accuracy is not clear. In the experiment, we built prediction models using a dataset collected from an open source software project. The result suggests that the prediction accuracy is improved, when clone metrics derived from the various clone detection tool are used.

Original languageEnglish
Title of host publication2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages15-16
Number of pages2
ISBN (Electronic)9781509018550
DOIs
Publication statusPublished - May 20 2016
Event10th International Workshop on Software Clones, IWSC 2016 - Osaka, Japan
Duration: Mar 15 2016 → …

Other

Other10th International Workshop on Software Clones, IWSC 2016
CountryJapan
CityOsaka
Period3/15/16 → …

Fingerprint

Experiments
Open source software

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Tsunoda, M., Kamei, Y., & Sawada, A. (2016). Assessing the differences of clone detection methods used in the fault-prone module prediction. In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016 (pp. 15-16). [7476788] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SANER.2016.65

Assessing the differences of clone detection methods used in the fault-prone module prediction. / Tsunoda, Masateru; Kamei, Yasutaka; Sawada, Atsushi.

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 15-16 7476788.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tsunoda, M, Kamei, Y & Sawada, A 2016, Assessing the differences of clone detection methods used in the fault-prone module prediction. in 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016., 7476788, Institute of Electrical and Electronics Engineers Inc., pp. 15-16, 10th International Workshop on Software Clones, IWSC 2016, Osaka, Japan, 3/15/16. https://doi.org/10.1109/SANER.2016.65
Tsunoda M, Kamei Y, Sawada A. Assessing the differences of clone detection methods used in the fault-prone module prediction. In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 15-16. 7476788 https://doi.org/10.1109/SANER.2016.65
Tsunoda, Masateru ; Kamei, Yasutaka ; Sawada, Atsushi. / Assessing the differences of clone detection methods used in the fault-prone module prediction. 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 15-16
@inproceedings{41261e1c1d614dc9a48e18bd38fae8a7,
title = "Assessing the differences of clone detection methods used in the fault-prone module prediction",
abstract = "We have investigated through several experiments the differences in the fault-prone module prediction accuracy caused by the differences in the constituent code clone metrics of the prediction model. In the previous studies, they use one or more code clone metrics as independent variables to build an accurate prediction model. While they often use the clone detection method proposed by Kamiya et al. to calculate these metrics, the effect of the detection method on the prediction accuracy is not clear. In the experiment, we built prediction models using a dataset collected from an open source software project. The result suggests that the prediction accuracy is improved, when clone metrics derived from the various clone detection tool are used.",
author = "Masateru Tsunoda and Yasutaka Kamei and Atsushi Sawada",
year = "2016",
month = "5",
day = "20",
doi = "10.1109/SANER.2016.65",
language = "English",
pages = "15--16",
booktitle = "2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Assessing the differences of clone detection methods used in the fault-prone module prediction

AU - Tsunoda, Masateru

AU - Kamei, Yasutaka

AU - Sawada, Atsushi

PY - 2016/5/20

Y1 - 2016/5/20

N2 - We have investigated through several experiments the differences in the fault-prone module prediction accuracy caused by the differences in the constituent code clone metrics of the prediction model. In the previous studies, they use one or more code clone metrics as independent variables to build an accurate prediction model. While they often use the clone detection method proposed by Kamiya et al. to calculate these metrics, the effect of the detection method on the prediction accuracy is not clear. In the experiment, we built prediction models using a dataset collected from an open source software project. The result suggests that the prediction accuracy is improved, when clone metrics derived from the various clone detection tool are used.

AB - We have investigated through several experiments the differences in the fault-prone module prediction accuracy caused by the differences in the constituent code clone metrics of the prediction model. In the previous studies, they use one or more code clone metrics as independent variables to build an accurate prediction model. While they often use the clone detection method proposed by Kamiya et al. to calculate these metrics, the effect of the detection method on the prediction accuracy is not clear. In the experiment, we built prediction models using a dataset collected from an open source software project. The result suggests that the prediction accuracy is improved, when clone metrics derived from the various clone detection tool are used.

UR - http://www.scopus.com/inward/record.url?scp=84978042708&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978042708&partnerID=8YFLogxK

U2 - 10.1109/SANER.2016.65

DO - 10.1109/SANER.2016.65

M3 - Conference contribution

AN - SCOPUS:84978042708

SP - 15

EP - 16

BT - 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -