Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci

Ahmet Gül; Yavuz Köroǧlu; Alper Şen

doi:10.1109/UYMS50627.2020.9247056

Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci

Translated title of the contribution: Machine Learning Based Bug Prediction Engine for Smart Contracts

Ahmet Gül, Yavuz Köroǧlu, Alper Şen

Bogazici University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

As blockchain solutions become widespread, identifying potential bugs in smart contracts written in Solidity language will be important for these solutions to work correctly. To accurately detect these bugs, the developer must use several state-of-the-art bug detection tools and investigate the potential bugs they report. In this study, we first show that one tool is not enough to detect all the bugs as our Static Analysis for Solidity tool (SA-Solidity) and the known SmartCheck and Securify tools identify different bugs in SmartEmbed's experimental set of smart contracts. Then, we develop Machine Learning-based Bug Predictor for Solidity (MLBP-Solidity) which predicts files that would be reported by all the previous bug detection tools. MLBP-Solidity eases the burden on the developer by allowing him/her to focus on a subset of files that are most probably buggy. Our experimental results show that MLBP-Solidity achieves 91-99% accuracy, depending on the type of predicted bug.

Translated title of the contribution	Machine Learning Based Bug Prediction Engine for Smart Contracts
Original language	Turkish
Title of host publication	2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings
Editors	Bekir Tevfik Akgun, Tolga Ayav, Semih Bilgen, Geylani Kardas
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728185415
DOIs	https://doi.org/10.1109/UYMS50627.2020.9247056
Publication status	Published - 7 Oct 2020
Externally published	Yes
Event	14th Turkish National Software Engineering Symposium, UYMS 2020 - Istanbul, Turkey Duration: 7 Oct 2020 → 9 Oct 2020

Publication series

Name	2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings

Conference

Conference	14th Turkish National Software Engineering Symposium, UYMS 2020
Country/Territory	Turkey
City	Istanbul
Period	7/10/20 → 9/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Access to Document

10.1109/UYMS50627.2020.9247056

Cite this

Gül, A., Köroǧlu, Y., & Şen, A. (2020). Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci. In B. T. Akgun, T. Ayav, S. Bilgen, & G. Kardas (Eds.), 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings Article 9247056 (2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UYMS50627.2020.9247056

Gül, Ahmet ; Köroǧlu, Yavuz ; Şen, Alper. / Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci. 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings. editor / Bekir Tevfik Akgun ; Tolga Ayav ; Semih Bilgen ; Geylani Kardas. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings).

@inproceedings{ce9fd90cd0f242e6a4fe8e978d80a6b9,

title = "Akilli Sozle{\c s}meler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci",

abstract = "As blockchain solutions become widespread, identifying potential bugs in smart contracts written in Solidity language will be important for these solutions to work correctly. To accurately detect these bugs, the developer must use several state-of-the-art bug detection tools and investigate the potential bugs they report. In this study, we first show that one tool is not enough to detect all the bugs as our Static Analysis for Solidity tool (SA-Solidity) and the known SmartCheck and Securify tools identify different bugs in SmartEmbed's experimental set of smart contracts. Then, we develop Machine Learning-based Bug Predictor for Solidity (MLBP-Solidity) which predicts files that would be reported by all the previous bug detection tools. MLBP-Solidity eases the burden on the developer by allowing him/her to focus on a subset of files that are most probably buggy. Our experimental results show that MLBP-Solidity achieves 91-99\% accuracy, depending on the type of predicted bug.",

keywords = "Blockchain, Bug Prediction, Ethereum, Smart Contracts, Solidity",

author = "Ahmet G{\"u}l and Yavuz K{\"o}roǧlu and Alper {\c S}en",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 14th Turkish National Software Engineering Symposium, UYMS 2020 ; Conference date: 07-10-2020 Through 09-10-2020",

year = "2020",

month = oct,

day = "7",

doi = "10.1109/UYMS50627.2020.9247056",

language = "T{\"u}rk{\c c}e",

series = "2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Akgun, \{Bekir Tevfik\} and Tolga Ayav and Semih Bilgen and Geylani Kardas",

booktitle = "2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings",

address = "United States",

}

Gül, A, Köroǧlu, Y & Şen, A 2020, Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci. in BT Akgun, T Ayav, S Bilgen & G Kardas (eds), 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings., 9247056, 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 14th Turkish National Software Engineering Symposium, UYMS 2020, Istanbul, Turkey, 7/10/20. https://doi.org/10.1109/UYMS50627.2020.9247056

Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci. / Gül, Ahmet; Köroǧlu, Yavuz; Şen, Alper.
2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings. ed. / Bekir Tevfik Akgun; Tolga Ayav; Semih Bilgen; Geylani Kardas. Institute of Electrical and Electronics Engineers Inc., 2020. 9247056 (2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci

AU - Gül, Ahmet

AU - Köroǧlu, Yavuz

AU - Şen, Alper

PY - 2020/10/7

Y1 - 2020/10/7

N2 - As blockchain solutions become widespread, identifying potential bugs in smart contracts written in Solidity language will be important for these solutions to work correctly. To accurately detect these bugs, the developer must use several state-of-the-art bug detection tools and investigate the potential bugs they report. In this study, we first show that one tool is not enough to detect all the bugs as our Static Analysis for Solidity tool (SA-Solidity) and the known SmartCheck and Securify tools identify different bugs in SmartEmbed's experimental set of smart contracts. Then, we develop Machine Learning-based Bug Predictor for Solidity (MLBP-Solidity) which predicts files that would be reported by all the previous bug detection tools. MLBP-Solidity eases the burden on the developer by allowing him/her to focus on a subset of files that are most probably buggy. Our experimental results show that MLBP-Solidity achieves 91-99% accuracy, depending on the type of predicted bug.

AB - As blockchain solutions become widespread, identifying potential bugs in smart contracts written in Solidity language will be important for these solutions to work correctly. To accurately detect these bugs, the developer must use several state-of-the-art bug detection tools and investigate the potential bugs they report. In this study, we first show that one tool is not enough to detect all the bugs as our Static Analysis for Solidity tool (SA-Solidity) and the known SmartCheck and Securify tools identify different bugs in SmartEmbed's experimental set of smart contracts. Then, we develop Machine Learning-based Bug Predictor for Solidity (MLBP-Solidity) which predicts files that would be reported by all the previous bug detection tools. MLBP-Solidity eases the burden on the developer by allowing him/her to focus on a subset of files that are most probably buggy. Our experimental results show that MLBP-Solidity achieves 91-99% accuracy, depending on the type of predicted bug.

KW - Blockchain

KW - Bug Prediction

KW - Ethereum

KW - Smart Contracts

KW - Solidity

UR - https://www.scopus.com/pages/publications/85097544554

U2 - 10.1109/UYMS50627.2020.9247056

DO - 10.1109/UYMS50627.2020.9247056

M3 - Konferans katkısı

AN - SCOPUS:85097544554

T3 - 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings

BT - 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings

A2 - Akgun, Bekir Tevfik

A2 - Ayav, Tolga

A2 - Bilgen, Semih

A2 - Kardas, Geylani

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th Turkish National Software Engineering Symposium, UYMS 2020

Y2 - 7 October 2020 through 9 October 2020

ER -

Gül A, Köroǧlu Y, Şen A. Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci. In Akgun BT, Ayav T, Bilgen S, Kardas G, editors, 2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9247056. (2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings). doi: 10.1109/UYMS50627.2020.9247056

Akilli Sozleşmeler Icin Makine Oǧgrenmesi Tabanli Hata Tahmin Araci

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this