Context: WS-BPEL has been recognized as service composition language which is both executable and process-oriented. Customers are benefited from the collaboration of various providers of web services. An XML format ba...
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ISBN:
(纸本)9781479942367
Context: WS-BPEL has been recognized as service composition language which is both executable and process-oriented. Customers are benefited from the collaboration of various providers of web services. An XML format based language known as BPEL different from syntax and semantics from traditional program language. Objective: In this paper, we create a BPEL process creation example and framework for faultlocalization by implementing BEES algorithm. Method: Based on the example of BPEL process creation we create an environment for local faultlocalization and uses BEES optimization algorithm to generate combinations of different base roots that result in lower fault occurrence. Result: BEES optimization algorithm is used to calculate number of faults occurrence. Results show that different combinations of different base tags result in lower fault occurrence. Conclusion: We conclude that our proposed framework of BPEL process creation and faultlocalization is effective in locating faults by generating test cases in BPEL programs.
The existing Software fault localization frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and ...
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The existing Software fault localization frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of faultlocalization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software faultlocalization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as faultlocalization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software faultlocalization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth.
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