Software measurement plays an important role in today's software development. Coupling is a measure used in software development that influences the understandability, maintainability and reusability of software components. Many proposals have been made for measuring object-oriented coupling of software components; however, most measures are not useful in the real world of software development. This paper provides a practical guide for evaluating coupling between object-oriented components. This technique is meant for use by software developers who are engaged in the development of software designed to be easy to understand and easy to maintain.

Software Engineering has attempted to improve the software development process for over two decades. A primary attempt at this process lies in the arena of measurement. "You can't control what you can't measure" [DEMT82]. This thesis attempts to measure the development of multimedia products. Multimedia languages seem to be the trend of future languages. Problem areas such as Education, Instruction, Training, and Information Systems require that various media allow the achievement of such goals. The first step in this measurement process is the placement of multimedia languages, namely Authorware, in the existing taxonomy of language paradigms. The next step involves the measurement of various distinguishing properties of the language. Finally, the measurement process is selected and evaluated. This evaluation gives insight as to the next step in establishing the goal of control, through measurement, of the multimedia software development process. / Master of Science

Aspect oriented programming has over the last decade become the subject of intense research within the domain of software engineering. Aspect mining, which is concerned with identification of cross cutting concerns in legacy software, is an important part of this domain. Aspect refactoring takes the identified cross cutting concerns and converts these into new software constructs called aspects. Software that have been transformed using this process becomes more modularized and easier to comprehend and maintain. The first attempts at mining for aspects were dominated by manual searching and parsing through source code using simple tools. More sophisticated techniques have since emerged including evaluation of execution traces, code clone detection, program slicing, dynamic analysis, and use of various clustering techniques. The focus of most studies has been to maximize aspect mining performance measured by various metrics including those of aspect mining precision and recall. Other metrics have been developed and used to compare the various aspect mining techniques with each other. Aspect mining automation and presentation of aspect mining results has received less attention. Automation of aspect mining and presentation of results conducive to aspect refactoring is important if this research is going to be helpful to software developers. This research showed that aspect mining can be automated. A tool was developed which performed automated aspect mining and visualization of identified cross cutting concerns. This research took a different approach to aspect mining than most aspect mining research by recognizing that many different categories of cross cutting concerns exist and by taking this into account in the mining process. Many different aspect mining techniques have been developed over time, some of which are complementary. This study was different than most aspect mining research in that multiple complementary aspect mining algorithms was used in the aspect mining and visualization process.

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Introduction: Simulation has an increasing role in medical education. It offers the ability to learn and practice in a safe environment. Ultrasound is a key tool for many clinicians; however, it requires significant experience to gain expertise. The most common method to gain experience is by training courses with volunteers, where experts are present for one-on-one teaching. This is time and labour intensive. Commercial ultrasound simulators are increasingly available with software capable of generating automated metrics. We sought validity evidence to support the use of automated metrics as a tool for assessment of learners completing a Focused Assessment with Sonography in Trauma (FAST) exam. Methods: Three groups with differing expertise were recruited to participate: novices with no ultrasound training, intermediates who had completed a formal course within six months, and experts with at least five years of clinical experience. All participants were recorded while completing a FAST exam. Automated metrics of time, path length, angular movement, and percent area viewed were obtained. This video was then scored using the Quality of Ultrasound Imaging and Competence (QUICk) by two expert assessors. Participants were also asked to complete ten find fluid exercises, where automated metrics were generated. Automated metrics from the recorded FAST and QUICk were compared using Kruskall-Wallis to assess for differences in expertise. Correlations between QUICk score and the automated metrics were assessed using Pearson’s correlation coefficient. Find fluid exercises were also assessed using repeated measures one-way ANOVA models. Results: Time, angular movement, and percent area viewed left upper quadrant (LUQ) were significantly different with novices requiring more time and angular movement, and higher percent area viewed LUQ than experts. The QUICk scores were significantly higher for the experts and intermediates compared to the novices. The scores from the QUICk overall and checklist did not correlate with any automated metrics. Individual components of positioning and handling, probe handling, and image scrolling were negatively correlated with percent area viewed LUQ. Overall, the QUICk tool could differentiate novices from both intermediates and experts when using the VIMEDIX-AR simulator. Several automated metrics could differentiate expertise. Further work should develop a composite score of automated metrics to assess learners. / Thesis / Master of Science (MSc) / Simulation has become ubiquitous in medical education, offering a safe environment to learn and practice new skills. With the increasing availability of point of care ultrasound and the need for significant training to generate and interpret images, simulation is becoming ever more important. We sought to assess an expert assessment tool for use with an ultrasound simulator and to validate automated metrics associated with the VIMEDIX-AR simulator. The expert assessment tool could reliably differentiate different expertise levels. Three of our automated metrics could discern different levels of expertise. Further work is needed to assess if a composite score of automated metrics could better differentiate skill.

Software bugs prediction is one of the most active research areas in the software engineering community. The process of testing and debugging code proves to be costly during the software development life cycle. Software metrics measure the quality of source code to identify software bugs and vulnerabilities. Traceable code patterns are able to de- scribe code at a finer granularity level to measure quality. Micro patterns will be used in this research to mechanically describe java code at the class level. Machine learning has also been introduced for bug prediction to localize source code for testing and debugging. Deep Learning is a branch of Machine Learning that is relatively new. This research looks to improve the prediction of software bugs by utilizing micro patterns with deep learning techniques. Software bug prediction at a finer granularity level will enable developers to localize code to test and debug during the development process.

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Software engineering often requires a team arrangement because of the size and scope of modern projects. Several team structures have been defined and used, but these structures generally define only the tasks and jobs required for the team. Various process and product metrics seek to improve quality, even though it is generally agreed that the greatest potential benefit lies in people issues. This study uses a team-based personality profiling tool, the Belbin Self-Perception Inventory, to explore the characteristics offered by the Company Worker, one of the team roles defined by Belbin. / Master of Science

Routing metrics are important mechanisms to adjust routing protocols' path selection according to the needs of a network system. However, if a routing metric design does not correctly match a particular routing protocol, the protocol may not be able to find an optimal path; routing loops can be produced as well. Thus, the compatibility between routing metrics and routing protocols is increasingly significant with the widespread deployment of wired and wireless networks. However, it is usually difficult to tell whether a routing metric can be perfectly applied to a particular routing protocol. Manually enumerating all possible test cases is very challenging and often infeasible. Therefore, it is highly desirable to have an automatic solution so that one can avoid putting an incompatible combination of routing metric and protocol into use. In this thesis, the above issue has been addressed by developing two automated checking systems for examining the compatibility between real world routing metric and protocol implementations. The automatic routing protocol checking system assumes that some properties of routing metrics are given and the system's job is to check if a new routing protocol is able to achieve optimal, consistent and loop- free routing when it is combined with metrics that hold the given metric properties. In contrast to the protocol checking system, the automatic routing metric checking system assumes that a routing protocol is given and the checking system needs to verify if a new metric implementation will be able to work with this protocol. Experiments have been conducted to verify the correctness of both protocol and metric checking systems. / Master of Science