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

Ahmet Gül
, Yavuz Köroǧlu
, Alper Şen

Bogazici University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Tercüme edilen katkı başlığı	Machine Learning Based Bug Prediction Engine for Smart Contracts
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2020 Turkish National Software Engineering Symposium, UYMS 2020 - Proceedings
Editörler	Bekir Tevfik Akgun, Tolga Ayav, Semih Bilgen, Geylani Kardas
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728185415
DOI'lar	https://doi.org/10.1109/UYMS50627.2020.9247056
Yayın durumu	Yayınlandı - 7 Eki 2020
Harici olarak yayınlandı	Evet
Etkinlik	14th Turkish National Software Engineering Symposium, UYMS 2020 - Istanbul, Türkiye Süre: 7 Eki 2020 → 9 Eki 2020

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	14th Turkish National Software Engineering Symposium, UYMS 2020
Ülke/Bölge	Türkiye
Şehir	Istanbul
Periyot	7/10/20 → 9/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Keywords

Blockchain
Bug Prediction
Ethereum
Smart Contracts
Solidity

Belgeye Erişim

10.1109/UYMS50627.2020.9247056

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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 Makale 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. editör / 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,

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doi = "10.1109/UYMS50627.2020.9247056",

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

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

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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, Türkiye, 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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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, editörler, 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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap