Thesis (M.S.)--California Polytechnic State University, 2008. / Major professor: David Janzen, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "June 2008." Includes bibliographical references (leaves 57-62). Also available online. Also available on microfiche (1 sheet).

Thesis (Ph.D.)--Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--Florida State University, 2006. / Advisor: Robert van Engelen, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed Sept. 22, 2006). Document formatted into pages; contains ix, 47 pages. Includes bibliographical references.

Software measurement process is a significant part of process improvement in software organizations. The organizations usually follow a measurement process that includes measures selection, data collection, and analysis to improve their processes. Most of the software organizations face difficulties in deciding the measures to collect since there is no universal set of measures for all types of organizations and projects. Experience shows that measurement can be more successful if the measures are collected based on the goals of the organization or the project which it will serve. A few methodologies exist to aid the software organizations. Goal Question Metric (GQM) is one of the most widely known and used one. However, one of the major constraints for the organizations is the associated cost when collecting the measures. Therefore, software organizations also require selecting the optimum set of measures which are good enough for the organization. This thesis study aims to provide solution for this problem. We propose a model, named ‗Optimum Measure Set Decision Model (OMSD)‘, which is an extension of GQM paradigm. The model is based on a heuristics approach, which aims to provide the optimum set of measures from a large number of possible measures. To develop the model, we identified the factors which are significant in selecting the optimum set of measures based on the literature survey results. Then, we evaluated those factors by conducting an empirical study. As the empirical research strategy, we used traditional fixed non-experimental design strategy. We performed a survey by distributing a structured questionnaire in order to evaluate the important factors we identified when selecting the optimum number of measures to be collected in an organization. We evaluated the heuristics rules by means of some sample cases we created. Moreover, we provided an idea for an alternative solution to optimize the number of measures to be collected for the future research. / Software measurement process is a significant part of process improvement in software organizations. The organizations usually follow a measurement process that includes measures selection, data collection, and analysis to improve their processes. Most of the software organizations face difficulties in deciding the measures to collect since there is no universal set of measures for all types of organizations and projects. Experience shows that measurement can be more successful if the measures are collected based on the goals of the organization or the project which it will serve. A few methodologies exist to aid the software organizations. Goal Question Metric (GQM) is one of the most widely known and used one. However, one of the major constraints for the organizations is the associated cost when collecting the measures. Therefore, software organizations also require selecting the optimum set of measures which are good enough for the organization. This thesis study aims to provide solution for this problem. We propose a model, named ‗Optimum Measure Set Decision Model (OMSD)‘, which is an extension of GQM paradigm. The model is based on a heuristics approach, which aims to provide the optimum set of measures from a large number of possible measures. To develop the model, we identified the factors which are significant in selecting the optimum set of measures based on the literature survey results. Then, we evaluated those factors by conducting an empirical study. As the empirical research strategy, we used traditional fixed non-experimental design strategy. We performed a survey by distributing a structured questionnaire in order to evaluate the important factors we identified when selecting the optimum number of measures to be collected in an organization. We evaluated the heuristics rules by means of some sample cases we created. Moreover, we provided an idea for an alternative solution to optimize the number of measures to be collected for the future research. / Cell# 0046-762640583, 0046-762509253

Context： Software evolution comes with the increasing growth of software applications both in size and complexity. Unlike the software maintenance, software evolution addresses more on the adaption of the new fast-changing requirements. Then the term of “software evolvability” comes with its importance for evaluating the evolution status of the software. However, it is not clearly identified especially in the context of open source software (OSS). Besides the most studies are about the description of software evolvability as a quality attribute, and very few research have done on the measurement of software evolvability during the software evolution process. Objectives： In this study we perform an in-depth investigation on identification of the OSS evolvability, and figure out the appropriate metrics used for measuring the OSS evolvability. Based on that we finally proposed the open source software evolvability measurement framework (OSEM) which could be used for measuring the software evolvability generally in an OSS context. Methods： At first, we conducted a literature review by combining backward snowballing search with systematic database search. Two research questions which are RQ1 and RQ2 are proposed for helping us to retrieve the key information for building the needed framework. Then we performed a case study on VLC media player (an OSS project) to validate the processes of the proposed framework. Results： Based on literature we could explicitly identify the OSS evolvability, and figure out the differences of software evolvability addressed in OSS context and non OSS context (e.g, the traceability refers to documentation in non OSS context, however in OSS context it refers to the release version of OSS project). Besides we also fulfill the evolvability measuring method by addressing the process of prioritization of evolvability sub-characteristics. In the end we implement the OSEM framework on VLC media player and get the well documented results which are clearly presented and easy to understand. Such results could be taken by the VLC developers as an input for the design and development of the VLC. Conclusions: We conclude that the open source software measurement framework (OSEM) is applicable, based on the time we spent on the case of VLC media player it is quite fast and efficient to use such framework. The results from the conduction of this framework are documented well and very clear for OSS users/developers to follow.

M.Ing. / Software engineering organizations face a struggle for daily survival in an extremely volatile climate. Numerous times it has been shown that the quality of a service or product could make the difference between an organization existing or closing down. The way in which quality is approached in any organization is part of a strategy; unbeknown to the managers and employees in many instances. Even though there are numerous books, articles, internet sites and other sources devoted to the subject of quality, total quality management, and strategic planning, not many of these information sources link quality and the strategy of the organization in such a way as to consider the quality of the organization’s products and services to be a major factor of strategic survival – or even the very existence of the organization. Quality is known under various names and terms in organizations, these terms and definitions will be investigated to grasp the true meaning of software quality and strategy as it concerns modern software engineering organizations. The tools and techniques required to improve and measure strategy and quality will also be scrutinized. One dominant factor about quality and strategy should be borne in mind, and that is that these programs depend on teamwork and management support as the major underlying framework. There are many tools and techniques that leaders and members of software teams can employ, but one of the most important factors is to gain a picture of the total process of continuous improvement and measurement. For this reason the author has included a large section on Jack Welch, who managed to use continuous improvement techniques to create one of the best, and biggest international organizations in recent years. It is vital that all people realize that they need an improvement and measurement model, and they need guidance in using such a model. This dissertation investigates the reasoning behind implementing strategic quality processes in software engineering organizations. Following the investigation into the necessity for a quality strategy, the methods, processes, tools and techniques that are required for a strategic quality framework (improvement and measurement model) for software engineering organizations will be researched to provide a basic framework and guidance in implementing such a model.

No description available.

功能點分析(Function Point Analysis)為一種考量資訊系統商業實務貢獻的功能複雜度指標，過去數十年來，許多學者對此指標的應用有過廣泛研究，但是，應用於某些領域的系統仍未有深入探討。例如本研究使用的個案，是一個點對點訊息傳遞資訊系統，其特性是採用同儕計算架構(Peer－to－Peer, P2P)，並使用後設資料模型(Meta-data Model)進行資料塑模，使用功能點分析模型進行研究，透過實際的估算步驟，期望能夠提出功能點分析的適用性問題，以求進一步應用於業界及後續學術研究。

The quality of information systems in any organization helps to determine theefficiency of the organization. Many organizations maintain a custom software portfolio, whose quality is important to the organization. Management would like to optimize the portfolio’s quality. Decisions about software replacement or enhancement are made based on organizational needs and priorities. The development resources allocated help in determining the quality of new software, and should be put to optimal use. Enhancing existing software might sound cheap and easy but it is not always efficient. This thesis proposes a simulation model - iKriya - for this problem. It explores the consequences of various development and maintenance policies which might be applied.These depend on the state of existing software portfolio, the queue and properties of proposed projects, and the resources available. Optimal decisions are made by the simulator by taking the above mentioned factors into consideration.

As a part of the quality management, product defectiveness prediction is vital for small software organizations as for instutional ones. Although for defect prediction there have been conducted a lot of studies, process enactment data cannot be used because of the difficulty of collection. Additionally, there is no proposed approach known in general for the analysis of process enactment data in software engineering. In this study, we developed a method to show the applicability of process enactment data for defect prediction and answered &ldquo / Is process enactment data beneficial for defect prediction?&rdquo / , &ldquo / How can we use process enactment data?&rdquo / and &ldquo / Which approaches and analysis methods can our method support?&rdquo / questions. We used multiple case study design and conducted case studies including with and without process enactment data in a small software development company. We preferred machine learning approaches rather than statistical ones, in order to cluster the data which includes process enactment informationsince we believed that they are convenient with the pattern oriented nature of the data. By the case studies performed, we obtained promising results. We evaluated performance values of prediction models to demonstrate the advantage of using process enactment data for the prediction of defect open duration value. When we have enough data points to apply machine learning methods and the data can be clusteredhomogeneously, we observed approximately 3% (ranging from -10% to %17) more accurate results from analyses including with process enactment data than the without ones. Keywords: