A procedure to detect problems of processes in software development projects using Bayesian networks

Perkusich, Mirko; Soares, Gustavo; Almeida, Hyggo; Perkusich, Angelo

doi:10.1016/j.eswa.2014.08.015

Cited by 75 publications

(61 citation statements)

References 37 publications

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“…Stamelos et al [24] modeled the uncertainties of factors to estimate software productivity. Perkusich et al [19] modeled software processes to support continuous improvement.…”

Section: Overview Of Bayesian Network Applied To Software Engineeringmentioning

confidence: 99%

“…Settas et al [22], Stamelos [23], Stamelos et al [24] and [19] modeled software processes to support other project management activities. Settas et al [22] and Stamelos [23] used Bayesian networks to help managerial decision making by modeling software project management antipatterns.…”

Section: Overview Of Bayesian Network Applied To Software Engineeringmentioning

confidence: 99%

“…It has been applied in several context of software engineering such as risk management [8,9,15,13], quality prediction [1,14] and process management [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Method to Build Bayesian Networks based on Artifacts and Metrics to Assess Agile Projects

Willamy¹,

Nunes²,

Perkusich

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

Self Cite

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Managing software development projects is a complex task because it requires organizing and monitoring several activities. Recently, in order to assist on software projects management, artifact-based models were proposed in the literature. However, the current solutions do not present means to monitor projects health and assist on decisions making. Due to the recent popularization of agile methods, they are the units of study of this research. In this work, we present a method to build artifact and measurementbased models to assess agile projects health. We applied the method to build a generic model based on industrys best practice. We defined the models artifacts and metrics based on findings of a literature review and the assistance of an expert. For each models artifact, we applied the Goal-Question-Metric paradigm to define the metrics. Afterwards, from the GQM meta-model, we constructed a Bayesian network. We validated the model with simulated scenarios. Given the successful results, we concluded that the method and model are promising.

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“…Stamelos et al [24] modeled the uncertainties of factors to estimate software productivity. Perkusich et al [19] modeled software processes to support continuous improvement.…”

Section: Overview Of Bayesian Network Applied To Software Engineeringmentioning

confidence: 99%

Section: Overview Of Bayesian Network Applied To Software Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

A Method to Build Bayesian Networks based on Artifacts and Metrics to Assess Agile Projects

Willamy¹,

Nunes²,

Perkusich

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The problems are usually solved by introducing some soft computing method, e.g., fuzzy logic, genetic or evolutionary algorithms or neural networks, to support or optimize the decision-making process. Bayesian networks were proposed as a tool for problem detection in a process of development scrumbased software projects (Perkusich et al, 2015). Chaves-González et al (2015) were dealing with the next release problem in software development using a multiobjective swarm intelligence evolutionary algorithm.…”

mentioning

confidence: 99%

A Fuzzy Logic-Based System for Enhancing Scrum Method

Stupar¹,

Milošević²,

Petrović³

2017

ManageFon

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“…Nonlinear relationships between variables in uncertain environments can be simulated for prediction and diagnosis (Chanda & Aggarwal, 2016;Chin, Tang, Yang, Wong, & Wang, 2009;Perkusich, Soares, Almeida, & Perkusich, 2015). Bayesian learning algorithms can efficiently aggregate the output of members of networks (Chen, 2016;Wang et al, 2010) and handle both nominal and numeric attributes well Q5 (Duan & Da Xu, 2012).…”

Section: Introductionmentioning

confidence: 99%

A methodology for assessing the effect of portfolio management on NPD performance based on Bayesian network scenarios

Chen

2016

Expert Systems

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Firm growth and profitability come primarily from new product development. Portfolio management has been emphasized in improving new product development (NPD) performance under multiple project environments. However, few researchers have demonstrated the consequence of different combinations of portfolio management practices on NPD performance.In this study, a decision support methodology based on Bayesian network scenarios is used to simulate the effect of portfolio management on NPD performance in uncertain environments. Portfolio management (PM), the extended application of investment portfolio theory to new product development (Cooper, Edgett, & Kleinschmidt, 1999), has been widely recognized as a crucial technique to improve NPD performance, especially for project-based organizations. As a strategic tool, portfolio management is about making important decisions including NPD project selection, resource allocation and balancing NPD projects (Cooper et al., 1999). Therefore, firms need to address the issues about how to conduct PM to realize strategic objectives (Lerch & Spieth, 2013).

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A procedure to detect problems of processes in software development projects using Bayesian networks

Cited by 75 publications

References 37 publications

A Method to Build Bayesian Networks based on Artifacts and Metrics to Assess Agile Projects

A Method to Build Bayesian Networks based on Artifacts and Metrics to Assess Agile Projects

A Fuzzy Logic-Based System for Enhancing Scrum Method

A methodology for assessing the effect of portfolio management on NPD performance based on Bayesian network scenarios

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