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The Software Engineering Research Group is one of research groups within the Computer Science Department. The Group focuses on fundamental and applied research in state-of-the-art software engineering methodologies and techniques, with a strong emphasis on practical training and skill transfer. Research in software engineering supplements teaching performed by the Software Engineering Subject Group.


Group Members:


Projects

The following is a list of recent research projects that are performed by members of the Software Engineering Research Group. Please refer to individual team member's web site for more information and project details.


  • Research on applying object-oriented iterative development methodologies to the design and development of advanced large-scale mission critical Artificial Intelligence (AI) applications such as AI scheduling software and AI staff rostering software. (Dr. Chun)

  • Research on how AI techniques, such as constraints and rules, can be applied to object-oriented design process to help enforce design rules as well as ensure design consistency. (Dr. Chun)

  • Research in the refinement of multiple OO models to develop a systematic model refinement approach to smooth the transition from UML design to implementation. This includes deriving model refinement techniques to formalize, verify and translate selected first-cut UML models to become more precise, consistent and are programming language-directed, and developing a software tool that automates the process. (Dr. Chow)

  • Research on practical software engineering issues with the design and development of Web-based school administrative and e-learning applications to fit the special needs of local primary/secondary schools. (Dr. Kwok)

  • Research on improving the cost-effectiveness of existing software testing strategies, extend the theoretical foundations of previous work, and enhance the strategies to handle informal specifications. A prototype system will be built to demonstrate the practicality and merits of the proposed strategies. (Dr. Yu)

  • Research to improve the efficiency of the MUMCUT testing strategy that detects single faults in Boolean expressions but uses substantially fewer test cases than those required by existing strategies. This project seeks to improve MUMCUT by further eliminating unnecessary test cases and to characterize its ability to detect double faults. (Dr. Yu)

  • Research in developing a methodology for test case design that incorporates both the black-box (specification) and white-box (source code) approaches. This new methodology uses white-box information for selecting black-box generated test cases. He plans to automate the approach to further reduce resource needs for software testing. (Dr. Yu)

  • Investigation into the practical issues and problems when classification-tree methodology is put into use in various phases of software development by practicing software developers who are less knowledgeable in the methodology. Classification-tree methodology is based on specification and can be applied not only to the planning of testing activities and resources, but also in other quality assurance activities. (Dr. Yu)

  • Research in knowledge acquisition and verification for XML data modeling. This research aims to create a standard XML conceptual schema and an XML design methodology. This is done by acquiring users' data requirements through an easy-to-use HCI (human computer interface) design that let user specify their requirements in questions and answers mode. Result is stored as rules and metadata, and can be transformed into a DTD Graph for display. It also performs reverse engineering to recover XML database design from DTD or XSD (XML Schema Definition) into a DTD Graph for visualization. The final product is a CASE tool for user to design an XML database with code generation and conceptual schema diagram display, from design to implementation and vice versa with computer automation. (Dr. Fong)

  • Research on the development of software testing techniques strategies for context-aware and embedded applications. This project will investigate the effects of the embedded environments of the computing entities, formulate effective test data selection and test oracle formulation strategies to tackle practical issues like unreliable environmental contexts and task collaborations in pervasive environments. (Dr. Chan)

  • Research on developing effective fault localization techniques. This project seeks to integrate analytical and statistical approaches to improve the effectiveness of fault localization for both conventional and wireless sensor network applications. (Dr. Chan)

  • Research on adaptive randomness. This project will seek evidences on the usefulness of adaptive randomness such as adaptive random testing, and devise effective methods under such a notion to improve existing techniques. (Dr. Chan)

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