Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners¿ acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings. / Applied Science University, Jordan.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-01-16T13:41:31Z No. of bitstreams: 1 marcosalexandremiguel.pdf: 10203756 bytes, checksum: 35844967ee919f58955320a1c591c5dc (MD5) / Approved for entry into archive by Diamantino Mayra (mayra.diamantino@ufjf.edu.br) on 2017-01-31T10:34:46Z (GMT) No. of bitstreams: 1 marcosalexandremiguel.pdf: 10203756 bytes, checksum: 35844967ee919f58955320a1c591c5dc (MD5) / Made available in DSpace on 2017-01-31T10:34:46Z (GMT). No. of bitstreams: 1 marcosalexandremiguel.pdf: 10203756 bytes, checksum: 35844967ee919f58955320a1c591c5dc (MD5) Previous issue date: 2016-09-01 / Muitas organizações encontram problemas na tentativa de estimar esforço em atividades de manutenção de software. Quando a estimativa de esforço não está bem definida ou é imprecisa, os resultados obtidos podem refletir diretamente na entrega do software, causando insatisfação do cliente ou diminuição da qualidade do produto. O sucesso ou fracasso de projetos depende da precisão do esforço e do cronograma das atividades envolvidas. O surgimento de métodos ágeis no campo de desenvolvimento de software tem apresentado muitas oportunidades e desafios para pesquisadores e profissionais da área. Um dos principais desafios é a estimativa de esforço para as atividades de manutenção no desenvolvimento ágil de software. Nesse contexto, este trabalho apresenta um framework, nomeado GiveMe Effort, o qual objetiva apoiar as atividades de estimativa de esforço na manutenção de software usando dados históricos e informações de compreensão de software. / Many organizations encounter problems when estimating effort for software maintenance activities. When estimating effort is not well defined or are inaccurate, the results may reflect directly into the software delivery, causing customer dissatisfaction or decreased product quality. The success or failure of projects depends on the accuracy of the effort and the schedule of involved activities. The rise of agile methods in software development has presented many opportunities and challenges for researchers and professionals. In this context, a key challenge is the effort estimate for maintenance activities in the agile software development context. This work presents a framework, called GiveMe Effort, to support the effort estimation activities in software maintenance. It is based on historical data and software comprehension information.

The increase in complexity in software development is followed by higher pressure on several factors such as time-to-market. Software development projects are, however, found to often run over time due to many uncertainties throughout the project. Agile methodologies have become popular in managing software development projects due to their flexible, short-term, and iterative approach to working and planning. There is however a dilemma between being agile without fixed plans, and simultaneously having pressure from management and customers to set and meet a deadline, based on long-term plans. The Agile way of working includes self-organized teams that are involved in project management tasks such as planning and estimating. Agile teams create their own variances of different Agile frameworks as well as their own team characteristics, planning methods, and working ways. Important characteristics of Agile teams are, for instance, cross-functionality and adaptation. Agile planning includes activities such as the breakdown of work, effort estimation, prioritization, task assignment, and planning of iterations. These activities are commonly used in software development teams to manage their work and increase the ability to deliver on time.  This study aims to investigate how software development teams plan their work in practice and what Agile planning activities and Agile team characteristics are regarded, by the development teams, to be important for on-time delivery. Furthermore, it is interesting to investigate how the identified activities and characteristics are reflected in the teams’ actual planning. A case study is conducted on two software development teams at Ericsson’s organization Cloud RAN to investigate this matter.  The studied software development teams are found to use variations of Agile methodologies. The thesis identifies a number of Agile planning activities and team characteristics to be regarded, by the teams, as important for on-time delivery. Many of these activities and characteristics are also reflected in how the teams actually plan. However, there are some contrasting opinions regarding the activities and team characteristics. It can therefore not be stated that all the identified Agile activities and team characteristics are reflected in the actual team-level planning. The thesis also includes other findings impacting teams to deliver on time as well as factors affecting team-level planning.

Effective implementation (in terms of time, cost, utilization of human resources, etc.) of information systems operation is a strategic issue in today's time when business processes are integrally aligned with the informatics. Currently, costs associated with software support and maintenance represent more than 90% of total costs. Software maintenance is a set of activities needed for cost-effective support of IT solution. IS / IT Center of excellence (COE) do not cover the area of software support and maintenance, there is no formalized methodology or procedural framework for COE for support and maintenance -- in reality, it means missing processes and procedures for creating it, management and evaluation of it. Moreover, there are missing recommendation about organization structure, services to be provided and overall continuous improvement. Therefore author proposes his own solution by definition and implementation of center of excellence for support and maintenance and its sub-centers of excellence for support and maintenance of particular applications. Current ontology of support and maintenance does not capture the necessary components and links -- namely missing management, planning and effort estimation views. Therefore author proposes his redefinition and enrichment of ontology of organizational structure about elements of competence and sub-competence center, typology of tasks (management, maintenance), and their management - estimating, planning and realization. In his work, Magne Jorgensen formulated conclusions that 83 to 84% of all estimation is done by pure expert estimates and estimating models are not used basically due to their complexity. Based on extending PERT formula about quality of estimator and historical experience, author introduced his simplified, easy to use approach to effort estimation in software maintenance. Both introduced formulas were verified in sub-competence center for supporting mortgage IS with significantly better result than only pure PERT estimate (98.8% and 91.8% against pure PERT 90.1%). In conclusion, author discusses the benefits of the implementation of center of excellence for support and maintenance and sub-centers of excellence for support and maintenance of particular applications, and overall fulfilling of thesis scope.