Software runaways: monumental software disasters
Software runaways: monumental software disasters
Managing risk: methods for software systems development
Managing risk: methods for software systems development
Software developer perceptions about software project failure: a case study
Journal of Systems and Software - Special issue on software engineering education and training for the next millennium
The Mythical Man-Month: Essays on Softw
The Mythical Man-Month: Essays on Softw
Software development cost estimation approaches – A survey
Annals of Software Engineering
Fear of Trying: The Plight of Rookie Project Managers
IEEE Software
IEEE Software
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Adopting the SW-CMM in a Small IT Organization
IEEE Software
A Survey on Software Estimation in the Norwegian Industry
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Australian Software Development: What Software Project Management Practices Lead to Success?
ASWEC '05 Proceedings of the 2005 Australian conference on Software Engineering
What do software practitioners really think about project success: an exploratory study
Journal of Systems and Software
Estimation of project success using Bayesian classifier
Proceedings of the 28th international conference on Software engineering
Predicting good requirements for in-house development projects
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Information and Software Technology
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Missing Data Imputation Techniques
International Journal of Business Intelligence and Data Mining
What do software practitioners really think about project success: A cross-cultural comparison
Journal of Systems and Software
Communications of the ACM - Finding the Fun in Computer Science Education
How large are software cost overruns? A review of the 1994 CHAOS report
Information and Software Technology
Predicting software development project outcomes
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Evaluating logistic regression models to estimate software project outcomes
Information and Software Technology
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Evaluation of three methods to predict project success: a case study
PROFES'05 Proceedings of the 6th international conference on Product Focused Software Process Improvement
An integrative framework for intelligent software project risk planning
Decision Support Systems
Perceived causes of software project failures - An analysis of their relationships
Information and Software Technology
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The software development process is usually affected by many risk factors that may cause the loss of control and failure, thus which need to be identified and mitigated by project managers. Software development companies are currently improving their process by adopting internationally accepted practices, with the aim of avoiding risks and demonstrating the quality of their work. This paper aims to develop a method to identify which risk factors are more influential in determining project outcome. This method must also propose a cost effective investment of project resources to improve the probability of project success. To achieve these aims, we use the probability of success relative to cost to calculate the efficiency of the probable project outcome. The definition of efficiency used in this paper was proposed by researchers in the field of education. We then use this efficiency as the fitness function in an optimization technique based on genetic algorithms. This method maximizes the success probability output of a prediction model relative to cost. The optimization method was tested with several software risk prediction models that have been developed based on the literature and using data from a survey which collected information from in-house and outsourced software development projects in the Chilean software industry. These models predict the probability of success of a project based on the activities undertaken by the project manager and development team. The results show that the proposed method is very useful to identify those activities needing greater allocation of resources, and which of these will have a higher impact on the projects success probability. Therefore using the measure of efficiency has allowed a modular approach to identify those activities in software development on which to focus the project's limited resources to improve its probability of success. The genetic algorithm and the measure of efficiency presented in this paper permit model independence, in both prediction of success and cost evaluation.