Software maintenance project delays prediction using Bayesian Networks

Melo, Ana Duarte; Sanchez, Adilson J.

doi:10.1016/j.eswa.2006.10.040

Cited by 63 publications

(28 citation statements)

References 17 publications

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“…In the second case, mistakes can appear due to noise, discretization methods or database errors. The use of Bayesian networks in software engineering is not new [26,27,46,13], for example they have been applied to maintenance [13], defect prediction [26] and the implementation of a software project [46].…”

Section: Bayesian Network and Requirements Engineeringmentioning

confidence: 99%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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Requirements analysis is the software engineering stage that is closest to the users' world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.

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Section: Bayesian Network and Requirements Engineeringmentioning

confidence: 99%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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“…The Bayesian estimation method combines subjective experience and real data to estimate attribute values using statistical methods (Cho, 2006;de Melo & Sanchez, 2008). Suppose the probability density function for the random variable x is f(x), and this function has an unknown attribute h. Also suppose a subjective judgment probability density function h(h) is used to express the value of the random variable h. Given a set of random samples x 1 , .…”

Section: Bayesian Estimationmentioning

confidence: 99%

Building reliability growth model using sequential experiments and the Bayesian theorem for small datasets

Chang

Chen

2010

Expert Systems with Applications

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“…Representation of Bayesian Network comprises two parts [9]: (a). The qualitative part of Bayesian Networks is a graphical description of variables the showing distribution and the relationship.…”

Section: Bayesian Networkmentioning

confidence: 99%

Prediction Models for Early Risk Detection of Cardiovascular Event

et al. 2010

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Cardiovascular disease (CVD) is the major cause of death globally. More people die of CVDs each year than from any other disease. Over 80% of CVD deaths occur in low and middle income countries and occur almost equally in male and female. In this paper, different computational models based on Bayesian Networks, Multilayer Perceptron,Radial Basis Function and Logistic Regression methods are presented to predict early risk detection of the cardiovascular event. A total of 929 (626 male and 303 female) heart attack data are used to construct the models.The models are tested using combined as well as separate male and female data. Among the models used, it is found that the Multilayer Perceptron model yields the best accuracy result.

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Software maintenance project delays prediction using Bayesian Networks

Cited by 63 publications

References 17 publications

Bayesian networks for enhancement of requirements engineering: a literature review

Bayesian networks for enhancement of requirements engineering: a literature review

Building reliability growth model using sequential experiments and the Bayesian theorem for small datasets

Prediction Models for Early Risk Detection of Cardiovascular Event

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