scholarly journals Using Aggloramative Clustering to Asses and Improve Software Reliability

2019 ◽  
Vol 8 (4) ◽  
pp. 10530-10535
Keyword(s):  
Software Testing ◽  
Software Reliability ◽  
Clustering Algorithms ◽  
Test Cases ◽  
Clustering Methods ◽  
Novel Technique ◽  
Data Treatment ◽  
Test Study

For the reduction of cost in software testing we propose a novel technique for testing and classifying methods based on clustering methods for classifying test cases for powerful and non-viable groups. This technique is based on data treatment obtained by pre-release of program while testing. Here we introduce 2 new clustering algorithms such as centroid and hierarchical based clustering. The test study expresses that the experiment bunching results can be distinguished viably with high review proportion and noteworthy rate exactness. The present paper tells about the presentation of clustering which move towards by comparing and investigating the factors like criteria coverage, features of construction and pre-release faults quality.

scholarly journals Enhanced Performance of Adaptive Random Partitioning Testing by Unifying the ARPT-1 and ARPT-2 Strategies

2020 ◽  
Vol 9 (4) ◽  
pp. 1156-1160
Keyword(s):  
Computational Complexity ◽  
Software Testing ◽  
Clustering Algorithms ◽  
Hybrid Approach ◽  
Support Vector ◽  
Test Cases ◽  
Random Testing ◽  
Software Products ◽  
Testing Strategies ◽  
Time Required

The software testing is considered as the most powerful and important phase. Effective testing process will leads to more accurate and reliable results and high quality software products. Random testing (RT) is a major software testing strategy and their effortlessness makes them conceivable as the most efficient testing strategies concerning the time required for experiment determination, its significant drawback of RT is defect detection efficacy. This draw back has been beat by Adaptive Testing (AT), however AT is enclosed of computational complexity. One most important method for improving RT is Adaptive random testing (ART). Another class of testing strategies is partition testing is one of the standard software program checking out strategies, which involves dividing the enter domain up into a set number of disjoint partitions, and selecting take a look at cases from inside every partition The hybrid approach is a combination of AT and RPT that is already existing called as ARPT strategy. In ARPT the random partitioning is improved by introducing different clustering algorithms solves the parameter space of problem between the target method and objective function of the test data. In this way random partitioning is improved to reduce the time conception and complexity in ARPT testing strategies. The parameters of enhanced ARPT testing approaches are optimized by utilizing different optimization algorithms. The computational complexity of Optimized Improved ARPT (OIARPT) testing strategies is reduced by selecting the best test cases using Support Vector Machine (SVM). In this paper the testing strategies of Optimized Improved ARPT with SVM are unified and named as Unified ARPT (UARPT) which enhances the testing performance and reduces the time complexity to test software.

scholarly journals RISK-BASED TESTING: IDENTIFYING, ASSESSING, MITIGATING & MANAGING RISKS EFFICIENTLY IN SOFTWARE TESTING

2020 ◽  
Author(s):  
Omdev Dahiya ◽  
Kamna Solanki ◽  
Amita Dhankhar
Keyword(s):  
Software Testing ◽  
Test Cases ◽  
Novel Technique ◽  
Software Product ◽  
Testing Schedule ◽  
Client Side ◽  
Bug Fixes ◽  
Source Of Information ◽  
Important Test

Most of the software organizations often strive hard while deciding the releasedates of their software product. This is because no organization wants to take riskswhere the fault is revealed in the developed product on the client-side. This will leadto expensive bug-fixes, and the image of the developer company is tarnished. On theother hand, testing beyond a particular time would lead to a loss of revenue for theorganization. The effective approach for handling the risky components will enablesoftware testers to identify more important test cases that can reveal faults associatedwith those components. After identification of those test cases, software testers work tofix fault sooner by managing the testing schedule by running such test cases earlier.Faults associated with hazardous components can also be detected sooner. In riskbased testing, the probability of a fault becoming a reality is assessed, and the damagethat this fault can cause when leading to failure is considered. This study haspresented an overall layout of risk-based testing. We have summarized the researchfindings of numerous researchers in this field. This will help the newcomers in thisfiled to provide a comprehensive source of information altogether. The futuredirection of this study will focus on proposing a novel technique for risk-based testing,considering different parameters together.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

scholarly journals A Prioritization Approach for Regression Test Cases Based on a Revised Genetic Algorithm

2021 ◽  
Vol 50 (3) ◽  
pp. 443-457
Author(s):  
Thamer Alrawashdeh ◽  
Fuad ElQirem ◽  
Ahmad Althunibat ◽  
Roba Alsoub
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Regression Testing ◽  
Test Cases ◽  
Average Percentage ◽  
Lower Priority ◽  
Regression Test ◽  
Predefined Criterion ◽  
Good Coverage ◽  
Selection Of

The regression testing is a software-based testing approach executed to verify that changes made to the softwaredo not affect the existing functionality of the product. On account of the constraints of time and cost, it isimpractical to re-execute all the test cases for software whenever a change occurs. In order to overcome sucha problem in the selection of regression test cases, a prioritization technique should be employed. On the basisof some predefined criterion, the prioritization techniques create an execution schedule for the test cases, sothe higher priority test cases can be performed earlier than the lower priority test cases in order to improvethe efficiency of the software testing. Many prioritization criteria for regression test cases have been proposedin software testing literature; however, most of such techniques are code-based. Keeping in view this fact, thisresearch work has proposed a prioritization approach for regression test cases generated from software specificationswhich are based on the criterion of the Average Percentage Transition Coverage (APTC) by using arevised genetic algorithm. This criterion evaluates the rate of transitions coverage by incorporating knowledgeabout the significance of transitions between activates in the form of weights. APTC has been used as a fitnessevaluation function in a genetic algorithm to measure the effectiveness of a test cases sequence. Moreover, inorder to improve the coverage percentage, the proposed approach has revised the genetic algorithm by solvingthe problem of the optimal local solution. The experimental results show that the proposed approach demonstratesa good coverage performance with less execution time as compared to the standard genetic algorithmand some other prioritization techniques.

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.

scholarly journals RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Zhao ◽  
Zhao-Yu Fang ◽  
Cui-Xiang Lin ◽  
Chao Deng ◽  
Yun-Pei Xu ◽  
...  
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Gene Selection ◽  
Clustering Algorithms ◽  
Selection Methods ◽  
Clustering Methods ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Random Forest Classification ◽  
Forest Classification

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION

2013 ◽  
Vol 12 (5) ◽  
pp. 3443-3451
Author(s):  
Rajesh Pasupuleti ◽  
Narsimha Gugulothu
Keyword(s):  
Linear Approximation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Privacy Preserving ◽  
Distance Measures ◽  
Clustering Methods ◽  
Sensitive Data ◽  
Processing Information ◽  
Data Objects ◽  
Approximation Of Function

Clustering analysis initiatives  a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the  requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by  user.  In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good  results in practice with an example of  business data are provided.  It also  explains privacy preserving clusters of sensitive data objects.

Short Text Clustering Algorithms for Weibo Topic Detection

2014 ◽  
Vol 971-973 ◽  
pp. 1747-1751 ◽  
Author(s):  
Lei Zhang ◽  
Hai Qiang Chen ◽  
Wei Jie Li ◽  
Yan Zhao Liu ◽  
Run Pu Wu
Keyword(s):  
Text Analysis ◽  
Semantic Information ◽  
Clustering Algorithms ◽  
Text Clustering ◽  
Massive Data ◽  
Topic Detection ◽  
Clustering Methods ◽  
Short Text ◽  
Short Text Clustering ◽  
Application Requirements

Text clustering is a popular research topic in the field of text mining, and now there are a lot of text clustering methods catering to different application requirements. Currently, Weibo data acquisition is through the API provided by big microblogging platforms. In this essay, we will discuss the algorithm of extracting popular topics posted by Weibo users by text clustering after massive data collection. Due to the fact that traditional text analysis may not be applicable to short texts used in Weibo, text clustering shall be carried out through combining multiple posts into long texts, based on their features (forwards, comments and followers, etc.). Either frequency-based or density-based short text clustering can deliver in most cases. The former is applicable to find hot topics from large Weibo short texts, and the latter is applicable to find abnormal contents. Both the two methods use semantic information to improve the accuracy of clustering. Besides, they improve the performance of clustering through the parallelism.

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