A mapping study on Bayesian networks for software quality prediction

Ayse Tosun Misirli; Ayşe Başar Bener

doi:10.1145/2593801.2593803

A mapping study on Bayesian networks for software quality prediction

Ayse Tosun Misirli, Ayşe Başar Bener

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

21 Atıf (Scopus)

Özet

Bayesian Networks (BN) have been used for decision making in software engineering for many years. We investigate the current status of BNs in predicting software quality in three aspects: 1) techniques used for parameter learning, 2) techniques used for structure learning, and 3) type of variables that represent BN nodes. We performed a systematic mapping study on 38 primary studies that employed BNs to predict software quality. The most popular technique for building the final structure of BNs is the use of expert knowledge with different inference algorithms. Variables in BNs are treated as categorical in more than 70% of studies. Compared to other domains, the usage of BNs is still very limited due to high dependency on expert knowledge and tools.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings
Yayınlayan	Association for Computing Machinery, Inc
Sayfalar	7-11
Sayfa sayısı	5
ISBN (Elektronik)	9781450328463
DOI'lar	https://doi.org/10.1145/2593801.2593803
Yayın durumu	Yayınlandı - 3 Haz 2014
Harici olarak yayınlandı	Evet
Etkinlik	3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Hyderabad, India Süre: 3 Haz 2014 → …

Yayın serisi

Adı	3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings

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

???event.eventtypes.event.conference???	3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014
Ülke/Bölge	India
Şehir	Hyderabad
Periyot	3/06/14 → …

Bibliyografik not

Publisher Copyright:
Copyright 2014 ACM.

Belgeye Erişim

10.1145/2593801.2593803

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Misirli, A. T., & Bener, A. B. (2014). A mapping study on Bayesian networks for software quality prediction. In 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings (pp. 7-11). Makale 2593803 (3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings). Association for Computing Machinery, Inc. https://doi.org/10.1145/2593801.2593803

Misirli, Ayse Tosun ; Bener, Ayşe Başar. / A mapping study on Bayesian networks for software quality prediction. 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings. Association for Computing Machinery, Inc, 2014. pp. 7-11 (3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings).

@inproceedings{3b78e4cee6284f80a3f2e218ffea05cd,

title = "A mapping study on Bayesian networks for software quality prediction",

abstract = "Bayesian Networks (BN) have been used for decision making in software engineering for many years. We investigate the current status of BNs in predicting software quality in three aspects: 1) techniques used for parameter learning, 2) techniques used for structure learning, and 3) type of variables that represent BN nodes. We performed a systematic mapping study on 38 primary studies that employed BNs to predict software quality. The most popular technique for building the final structure of BNs is the use of expert knowledge with different inference algorithms. Variables in BNs are treated as categorical in more than 70% of studies. Compared to other domains, the usage of BNs is still very limited due to high dependency on expert knowledge and tools.",

keywords = "Applications of Bayesian networks, Software defect prediction, Systematic mapping",

author = "Misirli, {Ayse Tosun} and Bener, {Ay{\c s}e Ba{\c s}ar}",

year = "2014",

month = jun,

day = "3",

doi = "10.1145/2593801.2593803",

language = "English",

series = "3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings",

publisher = "Association for Computing Machinery, Inc",

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booktitle = "3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings",

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Misirli, AT & Bener, AB 2014, A mapping study on Bayesian networks for software quality prediction. in 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings., 2593803, 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings, Association for Computing Machinery, Inc, pp. 7-11, 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014, Hyderabad, India, 3/06/14. https://doi.org/10.1145/2593801.2593803

A mapping study on Bayesian networks for software quality prediction. / Misirli, Ayse Tosun; Bener, Ayşe Başar.
3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings. Association for Computing Machinery, Inc, 2014. p. 7-11 2593803 (3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings).

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

TY - GEN

T1 - A mapping study on Bayesian networks for software quality prediction

AU - Misirli, Ayse Tosun

AU - Bener, Ayşe Başar

PY - 2014/6/3

Y1 - 2014/6/3

N2 - Bayesian Networks (BN) have been used for decision making in software engineering for many years. We investigate the current status of BNs in predicting software quality in three aspects: 1) techniques used for parameter learning, 2) techniques used for structure learning, and 3) type of variables that represent BN nodes. We performed a systematic mapping study on 38 primary studies that employed BNs to predict software quality. The most popular technique for building the final structure of BNs is the use of expert knowledge with different inference algorithms. Variables in BNs are treated as categorical in more than 70% of studies. Compared to other domains, the usage of BNs is still very limited due to high dependency on expert knowledge and tools.

AB - Bayesian Networks (BN) have been used for decision making in software engineering for many years. We investigate the current status of BNs in predicting software quality in three aspects: 1) techniques used for parameter learning, 2) techniques used for structure learning, and 3) type of variables that represent BN nodes. We performed a systematic mapping study on 38 primary studies that employed BNs to predict software quality. The most popular technique for building the final structure of BNs is the use of expert knowledge with different inference algorithms. Variables in BNs are treated as categorical in more than 70% of studies. Compared to other domains, the usage of BNs is still very limited due to high dependency on expert knowledge and tools.

KW - Applications of Bayesian networks

KW - Software defect prediction

KW - Systematic mapping

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U2 - 10.1145/2593801.2593803

DO - 10.1145/2593801.2593803

M3 - Conference contribution

AN - SCOPUS:84954527824

T3 - 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings

SP - 7

EP - 11

BT - 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings

PB - Association for Computing Machinery, Inc

T2 - 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014

Y2 - 3 June 2014

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Misirli AT, Bener AB. A mapping study on Bayesian networks for software quality prediction. In 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings. Association for Computing Machinery, Inc. 2014. p. 7-11. 2593803. (3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings). doi: 10.1145/2593801.2593803

A mapping study on Bayesian networks for software quality prediction

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