The optimization of success probability for software projects using genetic algorithms

Reyes, Francisco Vega; Cerpa, Narciso; Candia-Véjar, Alfredo; Bardeen, Matthew

doi:10.1016/j.jss.2010.12.036

Cited by 51 publications

(28 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The authors applied genetic algorithms to obtain this optimization of resources, and the prediction of success. The model also suggests a cost effective investment proposal [46] .…”

Section: The Technical Expertise Of the Project Managermentioning

confidence: 97%

See 1 more Smart Citation

Artificial Intelligence Applied to Project Success: A Literature Review

Martínez¹,

Rodríguez²

2015

IJIMAI

View full text Add to dashboard Cite

-Project control and monitoring tools are based on expert judgement and parametric tools. Projects are the means by which companies implement their strategies. However project success rates are still very low. This is a worrying situation that has a great economic impact so alternative tools for project success prediction must be proposed in order to estimate project success or identify critical factors of success. Some of these tools are based on Artificial Intelligence. In this paper we will carry out a literature review of those papers that use Artificial Intelligence as a tool for project success estimation or critical success factor identification.

show abstract

“…The authors applied genetic algorithms to obtain this optimization of resources, and the prediction of success. The model also suggests a cost effective investment proposal [46] .…”

Section: The Technical Expertise Of the Project Managermentioning

confidence: 97%

“…Within these groups we find those that apply artificial intelligence algorithms to critical success factors identification for measuring project success [36], [39], [43]- [46].…”

Section: A Determining Critical Success Factorsmentioning

confidence: 99%

Artificial Intelligence Applied to Project Success: A Literature Review

Martínez¹,

Rodríguez²

2015

IJIMAI

View full text Add to dashboard Cite

show abstract

“…The GA chromosome encoding strategies used in these cases are project-specific input data types, for example, costs [13], [17], tasks/activities [13], [17], and project risks [17], [37].…”

Section: Information Technology and Management Science ______________mentioning

confidence: 99%

Application of Genetic Algorithms for Decision-Making in Project Management: A Literature Review

Ancveire

Poļaka

2019

ITMS

View full text Add to dashboard Cite

In software development projects, managers still have to face a variety of organisational and technical limitations despite the development of technology and approaches to improve the project management process. Projects, Human Resources and Costs are planned for a specific period of time. However, in the progression of project execution, there is a need to make various decisions and to dynamically adjust the work plan during the project in order to conform to its evolution. Thus, there is a need for a method that employs the latest technology to support the project management decision-making process.The aim and the expected result of the article are to identify and collect available information in the scientific literature to answer the following questions: (1) Which challenges of project management have been addressed using genetic algorithms? (2) What are the opportunities and limitations of genetic algorithms in the project management decision-making process? (3) What are the potential solutions to the identified genetic algorithm problems?

show abstract

“…Furthermore, outsourced software projects are riskier than in-house ones because they involve multiple stakeholders and decision-makers (i.e., contractors and customers). Hence, there is a great need of intelligent risk prediction models that are effective in improving the evaluation of success probability [14,62].…”

Section: Introductionmentioning

confidence: 99%

Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

Feng

et al. 2015

Decision Support Systems

View full text Add to dashboard Cite

The optimization of success probability for software projects using genetic algorithms

Cited by 51 publications

References 38 publications

Artificial Intelligence Applied to Project Success: A Literature Review

Artificial Intelligence Applied to Project Success: A Literature Review

Application of Genetic Algorithms for Decision-Making in Project Management: A Literature Review

Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

Contact Info

Product

Resources

About