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

Univ. of Maryland, Baltimore
IEEE Transactions on Software Engineering (Impact Factor: 1.61). 11/2007; 33(10):643-658. DOI: 10.1109/TSE.2007.70723
Source: IEEE Xplore


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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    • "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.
    08/2015; 188. DOI:10.4204/EPTCS.188.7
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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.
    Knowledge-Based Systems 09/2014; 71. DOI:10.1016/j.knosys.2014.08.020 · 2.95 Impact Factor
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    • "Its key characteristic lies in the conceptual unfolding of data. Nowadays, FCA has been applied in many domains such as information retrieval [3], machine learning [4], knowledge discovery [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15], and software engineering [16] [17]. What is more, it has shown a trend of multidisciplinary intersection. "
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    ABSTRACT: Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: “if conditions 1,2,…, and m hold, then decisions hold.” In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency.
    The Scientific World Journal 07/2014; 2014:1-10. DOI:10.1155/2014/685362 · 1.73 Impact Factor
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