Metrics and Tools to Guide Design of Graphical User Interfaces

User interface design metrics assist developers evaluate interface designs in early phase before delivering the software to end users. This dissertation presents a metric-based tool called GUIEvaluator for evaluating the complexity of the user interface based on its structure. The metrics-model consists of five modified structural measures of interface complexity: Alignment, grouping, size, density, and balance. The results of GUIEvaluator are discussed in comparison with the subjective evaluations of interface layouts and the existing complexity metrics-models. To extend this metrics-model, the Screen-Layout Cohesion (SLC) metric has been proposed. This metric is used to evaluate the usability of user interfaces. The SLC metric has been developed based on Aesthetic, structural, and semantic aspects of GUIs. To provide the SLC calculation, a complementary tool has been developed, which is called GUIExaminer. This dissertation demonstrates the potential of incorporating automated complexity and cohesion metrics into the user interface design process. The findings show that a strong positive correlation between the subjective evaluation and both the GUIEvaluator and GUIExaminer, at a significance level 0.05. Moreover, the findings provide evidence of the effectiveness of the GUIEvaluator and GUIExaminer to predict the best user interface design among a set of alternative user interfaces. In addition, the findings show that the GUIEvaluator and GUIExaminer can measure some usability aspects of a given user interface. However, the metrics validation proves the usefulness of GUIEvaluator and GUIExaminer for evaluating user interface designs

Work in Progress – Establishing a Master Program in Cyber Physical Systems: Basic Findings and Future Perspectives

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical Systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is introduced with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes

Metrics and Tools to Guide Design Graphical User Interfaces

Video summarizing Ph.D. dissertation for a non-specialist audience.Software EngineeringComputer ScienceCollege of Science and Mathematic

Evaluation of Software Testing Coverage Tools: An Empirical Study

Code coverage is one of the most important aspects of software testing, which helps software engineers to understand which portion of code has been executed using test suite throughout the software testing process. Automatic testing tools are widely used to provide testing coverage metrics in order to gauge the quality of software, but these tools may suffer from some shortcomings such as the difference among the values of code coverage metric of a given program using different code coverage tools. Therefore, we designed and performed a controlled experiment to investigate whether these tools have a significant difference among the measured values of coverage metric or not. We collected the coverage data that consist of branch, line, statement, and method coverage metrics. Statistically, our findings show that there is a significant difference of code coverage results among the code coverage tools in terms of branch and method coverage metrics