In software development is essential to have tools for the software diagnosis to help the programmers and development engineersto locate the bugs. In this paper, we propose a new approach that identifies the possible ...
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UML has become the de facto standard for object-oriented modelling. Currently, UML comprises several different notations with no formal semantics attached to the individual diagrams or their integration, thus preventi...
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This paper focuses on credit card fraud in Multimedia Products, which are soft-products. By soft-products, we mean intangible products that can be used and consumed without having them shipped physically, such as soft...
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In software development is essential to have tools for the software diagnosis to help the programmers and development engineers to locate the bugs. In this paper, we propose a new approach that identifies the possible...
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System testing is concerned with testing an entire system based on its specifications. In the context of object-oriented, UML development, this means that system test requirements are derived from UML analysis artifac...
System testing is concerned with testing an entire system based on its specifications. In the context of object-oriented, UML development, this means that system test requirements are derived from UML analysis artifacts such as use cases, their corresponding sequence and collaboration diagrams, class diagrams, and possibly Object Constraint Language (OCL) expressions across all these artifacts. Our goal here is to support the derivation of functional system test requirements, which will be transformed into test cases, test oracles, and test drivers once we have detailed design information. In this paper, we describe a methodology in a practical way and illustrate it with an example. In this context, we address testability and automation issues, as the ultimate goal is to fully support system testing activities with high-capability tools.
We present here an improved strategy to devise optimal integration test orders in object-oriented systems. Our goal is to minimize the complexity of stubbing during integration testing as this has been shown to be a m...
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ISBN:
(纸本)1581135564
We present here an improved strategy to devise optimal integration test orders in object-oriented systems. Our goal is to minimize the complexity of stubbing during integration testing as this has been shown to be a major source of expenditure. Our strategy to do so is based on the combined use of inter-class coupling measurement and genetic algorithms. The former is used to assess the complexity of stubs and the latter is used to minimize complex cost functions based on coupling measurement. Using a precisely defined procedure, we investigate this approach in a case study involving a real system. Results are very encouraging as the approach clearly helps obtaining systematic and optimal results. Copyright 2002 ACM.
softwareengineering is not only a technical discipline of its own. It is also a problem domain where technologies coming from other disciplines are relevant and can play an important role. One important example is kn...
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ISBN:
(纸本)1581135564
softwareengineering is not only a technical discipline of its own. It is also a problem domain where technologies coming from other disciplines are relevant and can play an important role. One important example is knowledge engineering, a term that I use in the broad sense to encompass artificial intelligence, computational intelligence, knowledge bases, data mining, and machine learning. I see a number of typical software development issues that can benefit from these disciplines and, for the sake of clarifying the discussion, I have divided them into four categories: (1) Planning, monitoring, and quality control of projects, (2) The quality and process improvement of software organizations, (3) Decision making support, (4) Automation. First, the planning, monitoring, and quality control of software development is typically based, unless it is entirely ad-hoc, on past project data and/or expert opinion. As discussed below, several techniques coming from machine learning, computational intelligence, and knowledge-based systems have shown to be useful in this context. Second, software organizations are inherently learning organizations, that need to improve, based on experience and project feedback, the way they develop software in changing and volatile environments. Large amounts of data, numerous documents, and other forms of information are typically gathered on projects. The question then becomes how to enable the intelligent storage and use of such information in future projects. Third, during the course of a project, software engineers and managers have to face important, complex decisions. They need decision models to support them, especially when project pressure is intense. Techniques originally developed for building risk models based on expert elicitation or optimization heuristics can play a key role in such a context. The last category of applications concerns automation. Many automation problems, such as test data generation, can be formulated as const
This paper is based on a workshop held at the University of Southern California Center for softwareengineering in March, 2002. The components of the Capability Maturity Model IntegrationSM (CMMISM) systems Engineerin...
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Adaptive Quality-of-Service management is critical for enabling effective collaboration between distributed clients in a heterogeneous (wired and wireless) environment. This is because both client profiles (capabiliti...
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