Applying Concept Analysis to User-Session-Based Testing of Web Applications

Department of Computer and Information Sciences, University of Delaware, Ньюарк, Delaware, United States
IEEE Transactions on Software Engineering (Impact Factor: 1.61). 11/2007; 33(10):643-658. DOI: 10.1109/TSE.2007.70723
Source: IEEE Xplore

ABSTRACT

The continuous use of the Web for daily operations by businesses, consumers, and the government has created a great demand for reliable Web applications. One promising approach to testing the functionality of Web applications leverages the user-session data collected by Web servers. User-session-based testing automatically generates test cases based on real user profiles. The key contribution of this paper is the application of concept analysis for clustering user sessions and a set of heuristics for test case selection. Existing incremental concept analysis algorithms are exploited to avoid collecting and maintaining large user-session data sets and to thus provide scalability. We have completely automated the process from user session collection and test suite reduction through test case replay. Our incremental test suite update algorithm, coupled with our experimental study, indicates that concept analysis provides a promising means for incrementally updating reduced test suites in response to newly captured user sessions with little loss in fault detection capability and program coverage.

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Available from: Emily Hill, Nov 06, 2015
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    • "Formal concept analysis, presented by Wille [64] in the same year as rough set theory, has attracted many researchers [4] [56] [60] [84] [87] to this promising field. Up to now, its applications cover data mining [1] [13], knowledge discovery [10] [46] [69], machine learning [23], software engineering [53], etc. Within this theory, formal contexts, formal concepts and concept lattices are three basic notions for data analysis. "
    Dataset: 10KBS

    Full-text · Dataset · Jan 2016
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    • "As the explosive growth of web applications in the last two decades, the demand of their quality assurance , such as requirement of reliability, usability, and security, has grown significantly. Software testing has been an effective approach to ensuring the quality of web applications [14] [4] [20]. In practice, programmers and test engineers typically construct test cases either manually or using industrial testing automation tools such as Selenium [18], Watir [21], and Sahi [13]. "
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    ABSTRACT: We present TAO, a software testing tool performing automated test and oracle generation based on a semantic approach. TAO entangles grammar-based test generation with automated semantics evaluation using a denotational semantics framework. We show how TAO can be incorporated with the Selenium automation tool for automated web testing, and how TAO can be further extended to support automated delta debugging, where a failing web test script can be systematically reduced based on grammar-directed strategies. A real-life parking website is adopted throughout the paper to demonstrate the effectivity of our semantics-based web testing approach.
    Full-text · Article · Aug 2015
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    • "For example, many kinds of approaches have been proposed for building different types of concept lattices such as the Wille's concept lattices [15] [26] [37] [38], the fuzzy concept lattices [3–5,34,42,51], the variable threshold concept lattices [64], the real concept lattices [19] [31] [58] and the rough concept lattices [7] [60]. Nowadays, the concept lattice theory has been applied to a variety of areas such as machine learning and software engineering [6] [23] [44] [46]. "
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    ABSTRACT: Rule acquisition is one of the main purposes in the analysis of formal decision contexts. In general, given a formal decision context, some of its objects may not be essential to the rule acquisition. This study investigates the issue of reducing the object set of a formal decision context without losing the decision rule information provided by the entire set of objects. Using concept lattices, we propose a theoretical framework of object compression for formal decision contexts. And under this framework, it is proved that the set of all the non-redundant decision rules obtained from the reduced database is sound and complete with respect to the initial formal decision context. Furthermore, a complete algorithm is developed to compute a reduct of a formal decision context. The analysis of some real-life databases demonstrates that the proposed object compression method can largely reduce the size of a formal decision context and it can remove much more objects than both the techniques of clarified context and row reduced context.
    Full-text · Article · Sep 2014 · Knowledge-Based Systems
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