Adaptive Hybrid Semantic Selection of SAWSDL Services with SAWSDL-MX2

2010 ◽  
Vol 6 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Matthias Klusch ◽  
Patrick Kapahnke ◽  
Ingo Zinnikus
Keyword(s):  
Support Vector Machine ◽  
Comparative Evaluation ◽  
Structural Similarity ◽  
Support Vector ◽  
Test Collection ◽  
Semantic Matching ◽  
Retrieval Performance ◽  
Text Similarity ◽  
Random Subset ◽  
Selection Of

In this paper, the authors present an adaptive, hybrid semantic matchmaker for SAWSDL services, called SAWSDL-MX2. It determines three types of semantic matching of an advertised service with a requested one, which are described in standard SAWSDL: logic-based, text-similarity-based and XML-tree edit-based structural similarity. Before selection, SAWSDL-MX2 learns the optimal aggregation of these different matching degrees off-line over a random subset of a given SAWSDL service retrieval test collection by exploiting a binary support vector machine-based classifier with ranking. The authors present a comparative evaluation of the retrieval performance of SAWSDL-MX2.

Adaptive Hybrid Semantic Selection of SAWSDL Services with SAWSDL-MX2

2012 ◽  
pp. 146-171 ◽  
Author(s):  
Matthias Klusch ◽  
Patrick Kapahnke ◽  
Ingo Zinnikus
Keyword(s):  
Support Vector Machine ◽  
Comparative Evaluation ◽  
Structural Similarity ◽  
Support Vector ◽  
Test Collection ◽  
Semantic Matching ◽  
Retrieval Performance ◽  
Text Similarity ◽  
Random Subset ◽  
Selection Of

In this paper, the authors present an adaptive, hybrid semantic matchmaker for SAWSDL services, called SAWSDL-MX2. It determines three types of semantic matching of an advertised service with a requested one, which are described in standard SAWSDL: logic-based, text-similarity-based and XML-tree edit-based structural similarity. Before selection, SAWSDL-MX2 learns the optimal aggregation of these different matching degrees off-line over a random subset of a given SAWSDL service retrieval test collection by exploiting a binary support vector machine-based classifier with ranking. The authors present a comparative evaluation of the retrieval performance of SAWSDL-MX2.

scholarly journals Selection of Best Test Cases using Support Vector Machine for ARPT

2019 ◽  
Vol 8 (3) ◽  
pp. 4265-4271
Keyword(s):  
Support Vector Machine ◽  
Software Testing ◽  
Support Vector ◽  
Test Cases ◽  
Testing Strategy ◽  
Random Partition ◽  
Code Coverage ◽  
Time Consumption ◽  
Branch Coverage ◽  
Selection Of

Software testing is an essential activity in software industries for quality assurance; subsequently, it can be effectively removing defects before software deployment. Mostly good software testing strategy is to accomplish the fundamental testing objective while solving the trade-offs between effectiveness and efficiency testing issues. Adaptive and Random Partition software Testing (ARPT) approach was a combination of Adaptive Testing (AT) and Random Partition Approach (RPT) used to test software effectively. It has two variants they are ARPT-1 and ARPT-2. In ARPT-1, AT was used to select a certain number of test cases and then RPT was used to select a number of test cases before returning to AT. In ARPT-2, AT was used to select the first m test cases and then switch to RPT for the remaining tests. The computational complexity for random partitioning in ARPT was solved by cluster the test cases using a different clustering algorithm. The parameters of ARPT-1 and ARPT-2 needs to be estimated for different software, it leads to high computation overhead and time consumption. It was solved by Improvised BAT optimization algorithms and this approach is named as Optimized ARPT1 (OARPT1) and OARPT2. By using all test cases in OARPT will leads to high time consumption and computational overhead. In order to avoid this problem, OARPT1 with Support Vector Machine (OARPT1-SVM) and OARPT2- SVM are introduced in this paper. The SVM is used for selection of best test cases for OARPT-1 and OARPT-2 testing strategy. The SVM constructs hyper plane in a multi-dimensional space which is used to separate test cases which have high code and branch coverage and test cases which have low code and branch coverage. Thus, the SVM selects the best test cases for OARPT-1 and OARPT-2. The selected test cases are used in OARPT-1 and OARPT-2 to test software. In the experiment, three different software is used to prove the effectiveness of proposed OARPT1- SVM and OARPT2-SVM testing strategies in terms of time consumption, defect detection efficiency, branch coverage and code coverage.

Performance Prediction by an SVM with a Firefly Optimization Method

2020 ◽  
pp. 2150017
Author(s):  
Yongquan Yan
Keyword(s):  
Support Vector Machine ◽  
Economic Loss ◽  
Optimization Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Software Aging ◽  
Memory Prediction ◽  
Firefly Optimization ◽  
Aging Phenomena ◽  
Selection Of

Since software system is becoming more and more complex than before, performance degradation and even abrupt download, which are called software aging phenomena, bring about a great deal of economic loss. To counter these problems, some methods are used. Support vector machine is an effective method to tackle software aging problems, but its performance is influenced by the selection of hyper-parameters. A method is proposed to optimize the hyper-parameter selection of support vector machine in this work. The proposed method which is used as a training algorithm to optimize the parameter selection of support vector machine, utilizes the global exploration power of firefly method to achieve faster convergence and also a better accuracy. In the experiment, we use two metrics to test the effect of the proposed method. The results indicate that the presented method owns the highest accuracy in both the available memory prediction and heap memory prediction of Web server for software aging predictions.

A support vector machine model for intelligent selection of data representations

2014 ◽  
Vol 18 ◽  
pp. 70-81 ◽  
Author(s):  
Gabriela Czibula ◽  
Istvan Gergely Czibula ◽  
Radu Dan Gaceanu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Machine Model ◽  
Data Representations ◽  
Selection Of
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