A systematic literature review on the applications of Bayesian networks to predict software quality

Tosun, Ayşe; Bener, Ayşe; Akbarinasaji, Shirin

doi:10.1007/s11219-015-9297-z

Cited by 44 publications

(22 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since Fenton and Neil's pioneering work, many others have followed on their footsteps and employed BNs for decision-making in software engineering. We have identified four secondary studies investigating the use of Bayesian networks in software engineering; three targeted at specific research areas, such as effort estimation (Radlinski 2010), quality prediction (Tosun et al 2015) and requirements engineering enhancement (del Aguila and del Sagrado 2015). One, by Misirli and Bener (2014), looked at the use of BNs throughout all areas in software engineering.…”

Section: Bayesian Network Applied To Decision-making In Software Engmentioning

confidence: 99%

Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework

et al. 2017

View full text Add to dashboard Cite

To sustain growth, maintain competitive advantage and to innovate, companies must make a paradigm shift in which both short-and long-term value aspects are employed to guide their decision-making. Such need is clearly pressing in innovative industries, such as ICT, and is also the core of Value-based Software Engineering (VBSE). The goal of this paper is to detail a framework called VALUE-improving decision-making relating to softwareintensive products and services development-and to show its application in practice to a large ICT company in Finland. The VALUE framework includes a mixed-methods approach, as follows: to elicit key stakeholders' tacit knowledge regarding factors used during a decisionmaking process, either transcripts from interviews with key stakeholders are analysed and validated in focus group meetings or focus-group meeting(s) are directly applied. These value factors are later used as input to a Web-based tool (Value tool) employed to support decision making. This tool was co-created with four industrial partners in this research via a design science approach that includes several case studies and focus-group meetings. Later, data on

show abstract

Section: Bayesian Network Applied To Decision-making In Software Engmentioning

confidence: 99%

Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework

et al. 2017

View full text Add to dashboard Cite

show abstract

“…One of the constraints of the NB classifier is that it considers that all the features are conditionally independent. A Bayesian Network is another Bayesian classifier which can overcome this constraint [ 181 , 182 ]. The literature shows that the Bayesian classifier method is not utilized much for breast image classification.…”

Section: Performance Of Different Classifier Model On Breast Imagementioning

confidence: 99%

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Nahid

Kong

2017

Computational and Mathematical Methods in Medicine

106

View full text Add to dashboard Cite

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

show abstract

“…Langseth and Portinale [12] provided a thorough literature survey on BNs applied to reliability engineering, focusing on modeling framework, including BN model construction, causal interpretation, and BN inference. Tosun et al [13] provided a systematic review of BNs applied to software quality prediction, also focusing on BN modeling steps, namely, structure learning, parameter learning, use of tools, data characteristics, and validation. Mkrtchyan et al [14] reviewed the use of BNs in human reliability analysis, analyzed five groups of BN applications, and identified the process of constructing BNs.…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian Networks in Reliability Evaluation

Cai

Kong

et al. 2019

IEEE Trans. Ind. Inf.

193

View full text Add to dashboard Cite

The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

show abstract

A systematic literature review on the applications of Bayesian networks to predict software quality

Cited by 44 publications

References 30 publications

Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework

Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Application of Bayesian Networks in Reliability Evaluation

Contact Info

Product

Resources

About