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Estimation of project success using Bayesian classifier

Published: 28 May 2006 Publication History

Abstract

The software projects are considered to be successful if the cost and the duration are within the estimated ones and the quality is satisfactory. To attain project success, the project management, in which the final status of project is estimated, must be incorporated.In this paper, we consider estimation of the final status(that is, successful or unsuccessful) of project by applying Bayesian classifier to metrics data collected from project. In order to attain high estimation accuracy rate, we must select only a set of appropriate metrics to be applied. Here we consider two selection methods: the first method by the experts and the second method by the statistical test.Then we conducted an experiment using 28 project data and 29 metrics data in an organization of a certain company. The result showed that the method by the test gave higher accuracy rates than the method by the experts, and Bayesian classifier with the test method is effective to estimate project success.

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  • (2023)Genetic algorithm based probabilistic model for agile project success in global software developmentApplied Soft Computing10.1016/j.asoc.2023.109998135(109998)Online publication date: Mar-2023
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cover image ACM Conferences
ICSE '06: Proceedings of the 28th international conference on Software engineering
May 2006
1110 pages
ISBN:1595933751
DOI:10.1145/1134285
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 28 May 2006

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Author Tags

  1. Bayesian classifier
  2. estimation
  3. project success

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ICSE06
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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

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  • (2024)6G secure quantum communication: a success probability prediction modelAutomated Software Engineering10.1007/s10515-024-00427-y31:1Online publication date: 29-Mar-2024
  • (2023)Genetic algorithm based probabilistic model for agile project success in global software developmentApplied Soft Computing10.1016/j.asoc.2023.109998135(109998)Online publication date: Mar-2023
  • (2021)Attempt to Determine Non-Fault Case through System Engineer Sensitivity during Each Software Design ProcessSEが持つ感覚的評価から非障害案件の程度を各ソフト設計工程で確率的に予測する試みTransactions of Japan Society of Kansei Engineering10.5057/jjske.TJSKE-D-21-0000120:3(301-309)Online publication date: 2021
  • (2020)Systems Thinking in Software Projects-an Artificial Neural Network ApproachIEEE Access10.1109/ACCESS.2020.30401698(213619-213635)Online publication date: 2020
  • (2019)A Systematic Approach for Improving the Software Management ProcessInternational Journal of Innovation and Technology Management10.1142/S021987701940006616:04Online publication date: 24-Jun-2019
  • (2017)Incorporation of ISO 25010 with machine learning to develop a novel quality in use prediction system (QiUPS)International Journal of System Assurance Engineering and Management10.1007/s13198-017-0649-x9:2(344-353)Online publication date: 21-Jun-2017
  • (2016)Success Management as a PM Knowledge Area – Work-in-ProgressProcedia Computer Science10.1016/j.procs.2016.09.256100(1095-1102)Online publication date: 2016
  • (2014)Analysis and Improvement of Release Readiness – A Genetic Optimization ApproachProduct-Focused Software Process Improvement10.1007/978-3-319-13835-0_12(164-177)Online publication date: 2014
  • (2013)Incremental Estimation of Project Failure Risk with Naive Bayes Classifier2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1109/ESEM.2013.40(283-286)Online publication date: Oct-2013
  • (2012)The framework for monitoring the development process and inspection of government information system and technologyProceedings of the 2012 Joint international conference on Electronic Government and the Information Systems Perspective and Electronic Democracy, and Proceedings of the 2012 Joint international conference on Advancing Democracy, Government and Governance10.1007/978-3-642-32701-8_4(29-43)Online publication date: 3-Sep-2012
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