Using Bayesian Belief Networks to Model Software Project Management Antipatterns

Settas, Dimitrios; Bibi, Stamatia; Sfetsos, Panagiotis; Stamelos, Ioannis; Gerogiannis, Vassilis C.

doi:10.1109/sera.2006.68

Cited by 23 publications

(23 citation statements)

References 8 publications

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“…Settas et al, Stamelos, and Stamelos et al modeled software processes to support other project management activities. Settas et al and Stamelos used Bayesian networks to help managerial decision making by modeling software project management antipatterns. Stamelos et al modeled the uncertainties of factors to estimate software productivity.…”

Section: Introductionmentioning

confidence: 99%

Assisting the continuous improvement of Scrum projects using metrics and Bayesian networks

Perkusich

Gorgônio

Almeida

et al. 2016

J Software Evolu Process

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Scrum is a simple process to understand, but hard to adopt. Therefore, there is a need for resources to assist on its adoption. In this paper, we present the process followed to build a Bayesian network to assist on the assessment of the quality of the software process in the context of Scrum projects. The model provides data to help Scrum Masters lead the improvement of business value delivery of Scrum teams. The process is divided into 2 phases. In the first phase, we built the Bayesian network based on expert knowledge extracted from the literature and experts. We used a top‐down approach and reasoning to define the key metrics necessary to build the models and their relationships. In the second phase, we updated the Bayesian network based on limitations of the first version. We validated the Bayesian network inferences with 10 simulated scenarios. Comparing both versions, for all scenarios, we improved the accuracy of the inferences. Therefore, we concluded that the Bayesian networks adequately represent Scrum projects from the viewpoint of the Scrum Master. Finally, the model built is in conformance with agile methods tailoring and can be adapted to any Scrum team.

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Section: Introductionmentioning

confidence: 99%

Assisting the continuous improvement of Scrum projects using metrics and Bayesian networks

Perkusich

Gorgônio

Almeida

et al. 2016

J Software Evolu Process

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“…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%

“…Settas et al [22] and Stamelos [23] used Bayesian networks to help managerial decision making by modeling software project management antipatterns. Stamelos et al [24] modeled the uncertainties of factors to estimate software productivity.…”

Section: Overview Of Bayesian Network Applied To Software Engineeringmentioning

confidence: 99%

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

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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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“…The Project Monitoring with Stakeholders Model is based on the measurement information model defined by ISO/IEC 15939, and added stakeholder's (a purchaser and a developer) goal, key goal indicator, key performance indicator, corrective action, and check timing [31]. The Antipattern Bayesian Network Model provides the mathematical model of a project management antipattern and can be used to measure and handle uncertainty in mathematical terms [35]. The software project tracking literature [3,16] consists of GANTT Chart, SLIP Chart, TIMELINE Chart, CPM, PERT, COST Charts, S-Curve based methods such as Integrated Cost/Schedule/Work method and Earned Value Analysis.…”

Section: Related Workmentioning

confidence: 99%

Traditional and hybrid software project tracking technique formulation: state space approach with initial state uncertainty

Tyagi

Munisamy²,

Reddy

2014

CSIT

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Software projects are required to be tracked during their execution for controlling them. According to state-space approach, the tracking technique consists of software project state transition equation and software project status measurement equation. A key factor in tracking software projects is to represent the project with uncertainty involved in the parameters. Traditional and hybrid software project tracking technique is designed with state space approach and simulated using discrete event simulation in plan-space and execution-space. The uncertainty considered here is epistemological and is modeled as a normal distribution using an approximation method. The initial state of the project in execution-space also has an uncertainty associated with it. The project tracking technique consists of project state transition equation and project status measurement equation, in plan-space and is formulated with Monte Carlo method in execution-space. The project status is derived using project measurement equation as a function of project state. The project state is derived using project state transition equation. The software product is developed iteratively and incrementally. With Monte Carlo simulation runs, simulation result shows the uncertainty propagation iteration-wise, both individually and totally; and the effect of uncertainty on project status is shown by showing project status in execution-space and plan-space. Besides, the project completion somewhere during the last iteration is shown with simulation.

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Using Bayesian Belief Networks to Model Software Project Management Antipatterns

Cited by 23 publications

References 8 publications

Assisting the continuous improvement of Scrum projects using metrics and Bayesian networks

Assisting the continuous improvement of Scrum projects using metrics and Bayesian networks

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

Traditional and hybrid software project tracking technique formulation: state space approach with initial state uncertainty

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