Article

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

Univ. of Maryland, Baltimore;
IEEE Transactions on Software Engineering (Impact Factor: 2.59). 11/2007; 33(10):643-658. DOI: 10.1109/TSE.2007.70723
Source: DBLP

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.

0 Bookmarks
 · 
132 Views
  • Source
    [Show abstract] [Hide abstract]
    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. 01/2014;
  • Source
    [Show abstract] [Hide abstract]
    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. · 1.73 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Rule acquisition is one of the main purposes in the analysis of decision formal contexts. In general, the number of implications in a decision formal context is an exponential increase to the scale of the database. So, it is important to introduce effective inference rules between implications for eliminating as many superfluous implications as possible. This study puts forward a criterion called ‘strongness’ to assess the effectiveness of inference rules in terms of eliminating superfluous implications. We define a new inference rule in decision formal contexts and prove that the proposed inference rule is stronger than the existing one. Furthermore, we figure out the exact number of the superfluous implications that we can additionally remove by using the proposed inference rule compared with the existing one.
    International Journal of Computational Intelligence Systems 09/2014; 7(6):1-12. · 1.47 Impact Factor

Full-text (2 Sources)

View
3 Downloads
Available from