%0 Thesis %1 ImproveABE %A Yan-Fu, Li %D 2009 %K Cost DataSets Effort MetaResearch estimation meta methodology %T Improvement and implementation of analog based method for software project cost estimation %X Cost estimation is an important issue in project management. The effective application of project management methodologies often relies on accurate estimates of project cost. Cost estimation for software project is of particular importance as a large amount of the software projects suffer from serious budget overruns. Aiming at accurate cost estimation, several techniques have been proposed in the past decades. Analogy based estimation, which mimics the process of project managers making decisions and inherits the formal expressions of case based reasoning, is one of the most frequently studied methods. However, analogy based estimation is often criticized for its relatively poor predictive accuracy, large computational expense, and intolerance to uncertain inputs. To alleviate these drawbacks, this thesis is devoted to improve the analogy based method from three aspects: accuracy, efficiency, and robustness. A number of journal/conference papers have been published under this objective. The research works that have been done are grouped into four chapters (each chapter is focused on one component of analogy based estimation): chapter 3 summarizes the work on mutual information based feature selection technique for similarity function; chapter 4 presents the research on genetic algorithm based project selection method for historical database; chapter 5 presents the work on non-linear adjustment to solution function; chapter 6 presents the probabilistic model of analogy based estimation with focus on the number of nearest neighbors. The remaining chapters in this thesis, namely chapters 2 and 7, are the literature review and the conclusions and future works. Research in chapters 3 to 5 aims to enhance analogy based estimation¿s accuracy. For instance, in chapter 5 the adjustment mechanism has been largely improved for a more accurate analogy based method. Efficiency is another important aspect of estimation performance. In chapter 3, our study on refiningthe historical dataset has achieved a significant reduction of unnecessary projects and therefore improved the efficiency of analogy based method. Moreover, in chapter 6 the study on probabilistic model lead to a more robust and reliable analogy based method tolerable to uncertain inputs. The promising results show that this thesis makes significant contributions to the knowledge of analogy based software cost estimation in both the fields of software engineering and project management.

@phdthesis{ImproveABE, abstract = {Cost estimation is an important issue in project management. The effective application of project management methodologies often relies on accurate estimates of project cost. Cost estimation for software project is of particular importance as a large amount of the software projects suffer from serious budget overruns. Aiming at accurate cost estimation, several techniques have been proposed in the past decades. Analogy based estimation, which mimics the process of project managers making decisions and inherits the formal expressions of case based reasoning, is one of the most frequently studied methods. However, analogy based estimation is often criticized for its relatively poor predictive accuracy, large computational expense, and intolerance to uncertain inputs. To alleviate these drawbacks, this thesis is devoted to improve the analogy based method from three aspects: accuracy, efficiency, and robustness. A number of journal/conference papers have been published under this objective. The research works that have been done are grouped into four chapters (each chapter is focused on one component of analogy based estimation): chapter 3 summarizes the work on mutual information based feature selection technique for similarity function; chapter 4 presents the research on genetic algorithm based project selection method for historical database; chapter 5 presents the work on non-linear adjustment to solution function; chapter 6 presents the probabilistic model of analogy based estimation with focus on the number of nearest neighbors. The remaining chapters in this thesis, namely chapters 2 and 7, are the literature review and the conclusions and future works. Research in chapters 3 to 5 aims to enhance analogy based estimation¿s accuracy. For instance, in chapter 5 the adjustment mechanism has been largely improved for a more accurate analogy based method. Efficiency is another important aspect of estimation performance. In chapter 3, our study on refiningthe historical dataset has achieved a significant reduction of unnecessary projects and therefore improved the efficiency of analogy based method. Moreover, in chapter 6 the study on probabilistic model lead to a more robust and reliable analogy based method tolerable to uncertain inputs. The promising results show that this thesis makes significant contributions to the knowledge of analogy based software cost estimation in both the fields of software engineering and project management.}, added-at = {2010-12-08T15:10:24.000+0100}, author = {Yan-Fu, Li}, biburl = {https://www.bibsonomy.org/bibtex/2829dae7821ccb81ec577d4154fa8939d/gron}, interhash = {b7fff51414f1672bbdea5271080a0bce}, intrahash = {829dae7821ccb81ec577d4154fa8939d}, keywords = {Cost DataSets Effort MetaResearch estimation meta methodology}, month = {July}, school = {National University of Singapore}, timestamp = {2010-12-08T15:10:24.000+0100}, title = {Improvement and implementation of analog based method for software project cost estimation}, year = 2009 }