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      • Open Access Article

        1 - An Approach to Compose Viewpoints of Different Stakeholders in the Specification of Probabilistic Systems
        Mahboubeh Samadi haghighi haghighi
        Developing large and complex systems often involves many stakeholders each of which has her own expectations from the system; hence, it is difficult to write a single formal specification of the system considering all of stakeholders’ requirements at once; instead, each Full Text
        Developing large and complex systems often involves many stakeholders each of which has her own expectations from the system; hence, it is difficult to write a single formal specification of the system considering all of stakeholders’ requirements at once; instead, each stakeholder can specify the system from her own viewpoint first. Then, the resulting specifications can be composed to prepare the final specification. Much work has been done so far for the specification of non-probabilistic systems regarding viewpoints (or expectations) of different stakeholders; however, because of big trend to apply formal methods on probabilistic systems, in this paper, we present an approach to compose viewpoints of different stakeholders in the specification of probabilistic systems. According to this approach, different viewpoints are separately specified using the Z notation. Then, the resulting specifications are composed using some new operators proposed in this paper. We show the applicability of the presented approach by performing it on a known case study. Manuscript Document
      • Open Access Article

        2 - A Bio-Inspired Self-configuring Observer/ Controller for Organic Computing Systems
        Ali Tarihi haghighi haghighi feridon Shams
        The increase in the complexity of computer systems has led to a vision of systems that can react and adapt to changes. Organic computing is a bio-inspired computing paradigm that applies ideas from nature as solutions to such concerns. This bio-inspiration leads to the Full Text
        The increase in the complexity of computer systems has led to a vision of systems that can react and adapt to changes. Organic computing is a bio-inspired computing paradigm that applies ideas from nature as solutions to such concerns. This bio-inspiration leads to the emergence of life-like properties, called self-* in general which suits them well for pervasive computing. Achievement of these properties in organic computing systems is closely related to a proposed general feedback architecture, called the observer/controller architecture, which supports the mentioned properties through interacting with the system components and keeping their behavior under control. As one of these properties, self-configuration is desirable in the application of organic computing systems as it enables by enabling the adaptation to environmental changes. However, the adaptation in the level of architecture itself has not yet been studied in the literature of organic computing systems. This limits the achievable level of adaptation. In this paper, a self-configuring observer/controller architecture is presented that takes the self-configuration to the architecture level. It enables the system to choose the proper architecture from a variety of possible observer/controller variants available for a specific environment. The validity of the proposed architecture is formally demonstrated. We also show the applicability of this architecture through a known case study. Manuscript Document
      • Open Access Article

        3 - Using Static Information of Programs to Partition the Input Domain in Search-based Test Data Generation
        Atieh Monemi Bidgoli haghighi haghighi
        The quality of test data has an important effect on the fault-revealing ability of software testing. Search-based test data generation reformulates testing goals as fitness functions, thus, test data generation can be automated by meta-heuristic algorithms. Meta-heurist Full Text
        The quality of test data has an important effect on the fault-revealing ability of software testing. Search-based test data generation reformulates testing goals as fitness functions, thus, test data generation can be automated by meta-heuristic algorithms. Meta-heuristic algorithms search the domain of input variables in order to find input data that cover the targets. The domain of input variables is very large, even for simple programs, while this size has a major influence on the efficiency and effectiveness of all search-based methods. Despite the large volume of works on search-based test data generation, the literature contains few approaches that concern the impact of search space reduction. In order to partition the input domain, this study defines a relationship between the structure of the program and the input domain. Based on this relationship, we propose a method for partitioning the input domain. Then, to search in the partitioned search space, we select ant colony optimization as one of the important and prosperous meta-heuristic algorithms. To evaluate the performance of the proposed approach in comparison with the previous work, we selected a number of different benchmark programs. The experimental results show that our approach has 14.40% better average coverage versus the competitive approach Manuscript Document