Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An experimental study of individual subjective effort estimation and combinations of the estimates
Proceedings of the 20th international conference on Software engineering
A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models
IEEE Transactions on Software Engineering
Components of Software Development Risk: How to Address Them? A Project Manager Survey
IEEE Transactions on Software Engineering
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Comparing Software Prediction Techniques Using Simulation
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Assessing and Understanding Efficiency and Success of SoftwareProduction
Empirical Software Engineering
Assessing Project Success Using Subjective Evaluation Factors
Software Quality Control
SEL's Software Process Improvement Program
IEEE Software
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
The Repeatability of Code Defect Classifications
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Application of multivariate analysis for software fault prediction
Software Quality Control
Evaluating the Perceived Effect of Software Engineering Practices in the Italian Industry
ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
Evaluating logistic regression models to estimate software project outcomes
Information and Software Technology
The optimization of success probability for software projects using genetic algorithms
Journal of Systems and Software
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To increase the likelihood for software project success, it is important to be able to identify the drivers of success. This paper compares three methods to identify similar projects with the objective to predict project success. The hypothesis is that projects with similar characteristics are likely to have the same outcome in terms of success. Two of the methods are based on identifying similar projects using all available information. The first method of these aims at identifying the most similar project. The second method identifies a group of projects as most similar. Finally, the third method pinpoints some key characteristics to identify project similarity. Our measure of success for these identifications is whether project success for these projects identified as similar is the same. The comparison between methods is done in a case study with 46 projects with varying characteristics. The paper evaluates the performance of each method with regards to its ability to predict project success. The method using key drivers of project success is superior to the others in the case study. Thus, it is concluded that it is important for software developing organizations to identify its key project characteristics to improve its control over project success.