This paper presents a Semantic Software Development Model (SSDM ) for object-oriented software. It organizes all the information generated during the software development lifecycle including requirements, design, implementation, testing, and maintenance. Based on SSDM, software testing and maintenance can be carried out in a more systematic, efficient and complete manner, and can be enhanced by a set of proactive rules defined. IntroductionDeveloping software with high quality is one of the important objectives of software engineering. In the past, numerous testing and maintenance techniques have been developed to facilitate software testing and maintenance. Nowadays people consider more and more about the completeness and effectiveness of such techniques in order to increase the developers' confidence in software quality.To some extent, all these techniques have been developed based on the information collected about software requirements, design, implementation, testing and maintenance. If the information can be organized into a complete and tight-coupled model, the model can then be used to support more systematic, complete and efficient software testing and maintenance.In this paper, we will discuss model-based test case generation and model-based regression test selection for software testing and maintenance. Related WorkModel-based testing and maintenance have been provided in many software development systems. For example , Rhapsody [1] integrated a set of technologies to develop software systems in a highly reusable and platform-independent way based on the idea of executable models [2], where model-based testing and maintenance are able to specify and run tests, and to detect defects from the test results by visualizing the failure points within the model. It also provides a model-based testing component to facilitate consistency checking between the requirements and the system constructed. Model-based test case generation techniques have been well-developed. These techniques are suitable for state-based systems that are modeled by a formal or semi -formal description language, like Extended Finite State Machine (EFSM), Specification Description Language (SDL), etc. A set of tools ([6], [9], [10], [11], etc) have been developed based on these techniques to generate system-level test suites from system models. Instead of using models, some other methodologies such as Genetic algorithms (GAs) have been proposed for test case generation. For example, i n the paper about path testing [3], Jin-Cherng Lin et al. used a set of GAs to generate test cases to test a selected path. They developed a new fitness function (SIMILARITY) to determine the distance between the exercised path and the target path for a given target path. In a paper about real-time system testing [4], J. Wegener, et al. declared an appropriate fitness function to measure the execution time for a real-time system. Through establishing the longest and shortest execution times, GAs can be used to validate the temporal correctness. In a paper abo...
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