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

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

21 Citations (Scopus)

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.

Original language	English
Title of host publication	3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Proceedings
Publisher	Association for Computing Machinery, Inc
Pages	7-11
Number of pages	5
ISBN (Electronic)	9781450328463
DOIs	https://doi.org/10.1145/2593801.2593803
Publication status	Published - 3 Jun 2014
Externally published	Yes
Event	3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014 - Hyderabad, India Duration: 3 Jun 2014 → …

Publication series

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

Conference

Conference	3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2014
Country/Territory	India
City	Hyderabad
Period	3/06/14 → …

Bibliographical note

Publisher Copyright:
Copyright 2014 ACM.

Keywords

Applications of Bayesian networks
Software defect prediction
Systematic mapping

Access to Document

10.1145/2593801.2593803

Cite this

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). Article 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).

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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.",

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

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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).

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

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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.

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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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Keywords

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