Using Ensembles for Web Effort Estimation

Abstract

Accurate effort estimation enables effective managerial decisions to be made by project managers when embarking on a project, and this trend also applies when managing Web projects. Web development has steadily increased over the years; research into identifying sound ways to improve the effort estimates being made by thousands of Web companies worldwide would be valuable. Looking at the state of the art in the domain of Web resource estimation, we show that numerous effort estimation techniques have been investigated; none of which have proven conclusively to be the best.I would like to thank my supervisors, Pat Riddle and Emilia Mendes, for their patience, guidance, and encouragement over the course of this PhD. It has been a long journey, and without their invaluable input and understanding, this thesis would not have been possible.I would like to thank the Department of Computer Science at the University of Auckland for giving me the opportunity to do a PhD here, for providing financial support in terms of my PhD scholarship and employment opportunities, as well as for all their technical and administrative help.I would like to thank my family for their patience, love, and support, through the various ups and downs natural to an endeavor as complicated and lengthy as this.Lastly, to my friends and colleagues, thank you for your support, academic and otherwise, that have made my PhD experience both interesting and enjoyable. Damir Azhar31/08/2015iii iv Contents List of Figures ix List of Tables xiiiClick hereClick here using Web project data from the Tukutuku dataset [4].3. We expanded on the replicated methodology, introducing the use of bootstrap aggregation, commonly referred to as bagging, to the process of ensemble creation. This too is novel in the area of Web effort estimation. Bagging enables multiple experimental runs to be performed using a single dataset. We investigated three variants of bagging using 10 experimental runs for each variant, to provide a clearer picture of ensemble Web effort estimation performance.4. Using ensemble results obtained from the bagging experiments, we broke down ensemble performance via a mathematical formalization of the accuracy-diversity trade-off. This allowed us to quantify the relationship between ensemble error, ensemble diversity, and the accuracy of the component classifiers. The insight obtained from the analysis of this relationship is valuable for creating effective ensembles.All of the above contributions collectively further the general understanding of ensemble behaviour and performance, when used for Web effort estimation. Practitioners will be able to use these contributions to implement effective effort estimation ensembles, while researchers can build on them to further research into this domain. OrganizationThe remainder of the thesis is organized as follows:Chapter 2 presents a systematic literature review of Web resource estimation, detailing both the review process and its findings. We conclude this chapter with a discussion of what can be done to...

Data mining for software engineering and humans in the loop

“…Finally, many of the techniques that were applied to software effort estimation have also been used for Web effort estimation [4,27], which also include the use of ensembles [5]. Mendes pioneered this field, and also led the creation of the Tukutuku database, which is to date the only crosscompany database on Web project data [31].…”

Implementation of Scott Knott Algorithm in Rule-Base Expert System for Competency Assessment of Human Resources

“…The assessment stage conducted by Scott-Knott algorithm that function only works on balanced design and designed to help researchers cooperate with analysis of variance (ANOVA) experiments. In this study, the design chosen is a repeatable measurement equal to a complete random block design (RCDB) [2]. Scott-Knott is algorithm hierarchy classification used as an exploration data analysis tool that its application with analysis of variance (ANOVA).…”

“…In which comparison of treatment ratio is an important step to find homogeneous and non-homogeneous groups, it means that each situation leads to a significant F test [10]. Adaptation of random complete block design (RCDB) explained in the following general equation: (2) in which g is the gap value of each individual, μ is the actual of entire average treatment (mean rate). While, is the effect of treatment to or the difference of competence level 2, is the effect from block to j or individuals within this framework, and is error in the form of effect comes from the experimental unit to j which is subject to treatment to , is the employee (treatment) and j is the level 2 competency.…”

Effort Estimation Techniques for Web Application Development: A Review

“…Replication of previous methods and Snott-Knott algorithm [45] were used to shortlist best out of available techniques for ensembles. The results obtained in this research work showed better estimation accuracy by adapting ensembles in comparison to solo estimation models and identified 15 best estimation ensembles [46]. Giulio B., et al, 2015, in their research work proposed a methodology, Web Framework Points(WFP) to estimate efforts for web application development using CMF.…”

“…Damir Azhar, et al 2013, conducted a study to investigate the accuracy of ensembles for web effort estimation at early stages of web development. Replication of previous methods and Snott-Knott algorithm [45] were used to shortlist best out of available techniques for ensembles.…”