review-article

A 20‐year mapping of Bayesian belief networks in software project management

Authors:

Abraham L. R. de Sousa,

Cleidson R. B. de Souza,

Rodrigo Q. ReisAuthors Info & Claims

IET Software, Volume 16, Issue 1

Pages 14 - 28

https://doi.org/10.1049/sfw2.12043

Published: 09 November 2021 Publication History

Abstract

In recent years, more software engineering researchers have focussed on methods, techniques, tools, and processes to support software project management (SPM) addressing quality, cost, and time constraints. In this direction, the use of Bayesian belief networks (BBNs) has gained attention for providing a powerful mechanism for cause–effect analysis with both qualitative and quantitative data to support decision making under uncertainty. This work aims to provide an overview of the first 20 years of research in BBNs applied to SPM, and by so doing it contributes to the structuring of this research topic and the identification of new research opportunities. We conducted a systematic mapping (SM) study on the use of BBN as a decision‐support tool for SPM issues. We analysed over 109 relevant publications, from 1999 to 2018 (i.e., 20 years), to understand the motivations for using Bayesian networks (why), the problem domain and model scope addressed by researchers (what), the stage of the life cycle in which BBNs are being used (when), the venues of the publications (where), and the tools used to model the Bayesian networks (how). We draw the following conclusions from the results of our SM: (1) The application of Bayesian networks in SPM has been an active research topic since 1999. (2) Prediction and planning are the most common purposes in 60% of selected papers. (3) Software quality (55%) is the problem domain most investigated. (4) Most of the surveyed studies embrace the project (47%) and product (37%) scopes. And finally, (5) the development phase (86%) is when Bayesian networks have been more used. As future work, we highlight the need to further investigate methodologies that enable the use of BBNs in real software development contexts.

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Cited By

Kumar CJha SYadav DPrakash SPrasad M(2024)A generalized approach to construct node probability table for Bayesian belief network using fuzzy logicThe Journal of Supercomputing10.1007/s11227-023-05458-y80:1(75-97)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11227-023-05458-y
del Rey SMartínez‐Fernández SSalmerón A(2023)Bayesian Network analysis of software logs for data‐driven software maintenanceIET Software10.1049/sfw2.1212117:3(268-286)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1049/sfw2.12121
Siebert J(2023)Applications of statistical causal inference in software engineeringInformation and Software Technology10.1016/j.infsof.2023.107198159:COnline publication date: 10-May-2023
https://dl.acm.org/doi/10.1016/j.infsof.2023.107198

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cover image IET Software

IET Software Volume 16, Issue 1

February 2022

123 pages

EISSN:1751-8814

DOI:10.1049/sfw2.v16.1

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© 2021 The Authors. IET Software published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

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Published: 09 November 2021

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Kumar CJha SYadav DPrakash SPrasad M(2024)A generalized approach to construct node probability table for Bayesian belief network using fuzzy logicThe Journal of Supercomputing10.1007/s11227-023-05458-y80:1(75-97)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11227-023-05458-y
del Rey SMartínez‐Fernández SSalmerón A(2023)Bayesian Network analysis of software logs for data‐driven software maintenanceIET Software10.1049/sfw2.1212117:3(268-286)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1049/sfw2.12121
Siebert J(2023)Applications of statistical causal inference in software engineeringInformation and Software Technology10.1016/j.infsof.2023.107198159:COnline publication date: 10-May-2023
https://dl.acm.org/doi/10.1016/j.infsof.2023.107198

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