The capability maturity model: guidelines for improving the software process
The capability maturity model: guidelines for improving the software process
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
Using Neural Networks in Reliability Prediction
IEEE Software
Predicting Fault-Prone Modules with Case-Based Reasoning
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
The effects of case tools on software development effort
The effects of case tools on software development effort
A Bayesian Belief Network for Assessing the Likelihood of Fault Content
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
An Approach for Software Reliability Model Selection
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Simple software cost analysis: safe or unsafe?
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction
ICSEW '07 Proceedings of the 29th International Conference on Software Engineering Workshops
Robust mean-squared error estimation in the presence of model uncertainties
IEEE Transactions on Signal Processing
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As software has been pervasive and various software projects have been executed since the 1970's, software project management has played a significant role in software industry. There are three major factors in project management; schedule, effort and quality. Especially, to represent quality of products, there are various possible quality characteristics of software, but in practice, frequently, quality management revolves around defects, and delivered defect density has become the current de facto industry standard. The researches related to software quality have been focused on modeling residual defects in software in order to estimate software reliability. However, only the predicted number of defects cannot be sufficient information to provide basis for planning quality assurance activities and assessing them during execution. That is, in order to let projects managers be able to identify the project related information in early phase, we need to predict other possible information for assuring software quality such as defect density by phases, defect types and so on. In this paper, we propose a new approach for predicting distribution of in-process defects, their types based on project characteristics in early phase. For this approach, the model for prediction is established using the curve fitting method and the regression analysis. The maximum likelihood estimation is used in fitting the Weibull probability density function to the actual defect data, and the regression analysis is used to identify the relationship between the project characteristics and the Weibull parameters. The research model is validated by using cross-validation technique.