Towards exhaustive branch coverage with PathCrawler

Al-'adah muhakkamah, literally ‘customary usage is the determining factor’,is one of the five universal maxims of Islamic jurisprudencewhich have a wide branch coverage agreed upon by the scholars.This maxim is built on the basis of custom and local wisdom that exists in every community, where this custom is continuously carried out by the community without any denial from them; it is precisely individuals who violate these customs will be considered "freaks". Therefore, Islam makes the customs of a community a legal basis as long as they fulfill the requirements stipulated by Sharia.The formulation of the problem in this study is about: (1) the definition of the al-'adah muhakkamah maxim, (2) the conditions for applying it, and (3) its application in determining the amount and type of dowry. This paper tries to study a jurisprudence principle and apply it to a jurisprudence problem so as to form a complete understanding of the rules as well as how to apply them in the branch of jurisprudence. The conclusions of this study are: (1) that 'adah is something that happens repeatedly which is accepted by common sense and human nature, (2) an 'adah can become a legal basis if it does not contradict the arguments of the Sharia, it is more often done than abandoned, there is no words of the' adah doer who excludes, and the 'adah must exist at the time the covenant occurs,(3) the rule of al-'adah muhakkamah can be applied to determine the number and types ofmis^ldowries, as well as musamma dowries which are absolutely stated.

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Selection of Best Test Cases using Support Vector Machine for ARPT

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5166.098319 ◽

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.

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Bi-Objective Constraint and Hybrid Optimizer for the Test Case Prioritization

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9515.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3436-3448

Keyword(s):

Taylor Series ◽

Optimization Algorithm ◽

Fitness Function ◽

Regression Testing ◽

Test Case ◽

Test Cases ◽

Test Case Prioritization ◽

Optimal Test ◽

Average Percentage ◽

Branch Coverage

Regression testing is performed to make conformity that any changes in software program do not disturb the existing characteristics of the software. As the software improves, the test case tends to grow in size that makes it very costly to be executed, and thus the test cases are needed to be prioritized to select the effective test cases for software testing. In this paper, a test case prioritization technique in regression testing is proposed using a novel optimization algorithm known as Taylor series-based Jaya Optimization Algorithm (Taylor-JOA), which is the integration of Taylor series in Jaya Optimization Algorithm (JOA). The optimal test cases are selected based on the fitness function, modelled depending on the constraints, namely fault detection and branch coverage. The experimentation of the proposed Taylor-JOA is performed with the consideration of the evaluation metrics, namely Average Percentage of Fault Detected (APFD) and the Average Percentage of Branch Coverage (APBC). The APFD and the APBC of the proposed Taylor-JOA is 0.995, and 0.9917, respectively, which is high as compared to the existing methods that show the effectiveness of the proposed Taylor-JOA in the task of test case prioritization

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Program Test Data Generation for Branch Coverage with Genetic Algorithm: Comparative Evaluation of A Maximization and Minimization Approach

Computer Science Conference Proceedings ◽

10.5121/csit.2012.2140 ◽

2012 ◽

Cited By ~ 1

Author(s):

Ankur Pachauri Gursaran

Keyword(s):

Genetic Algorithm ◽

Test Data ◽

Comparative Evaluation ◽

Test Data Generation ◽

Data Generation ◽

Branch Coverage ◽

Program Test ◽

Minimization Approach

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Green DRCT

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2017010102 ◽

2017 ◽

Vol 7 (1) ◽

pp. 14-29

Author(s):

Sangharatna Godboley ◽

Arpita Dutta ◽

Durga Prasad Mohapatra

Keyword(s):

Energy Consumption ◽

Software Testing ◽

Transformation Method ◽

New Approach ◽

Code Coverage ◽

Coverage Metrics ◽

Branch Coverage ◽

Concolic Testing ◽

Green Software ◽

Environment Sustainability

Being a good software testing engineer, one should have the responsibility towards environment sustainability. By using green principles and regulations, we can perform Green Software Testing. In this paper, we present a new approach to enhance Branch Coverage and Modified Condition/Decision Coverage uses concolic testing. We have proposed a novel transformation technique to improve these code coverage metrics. We have named this new transformation method Double Refined Code Transformer (DRCT). Then, using JoulMeter, we compute the power consumption and energy consumption in this testing process. We have developed a tool named Green-DRCT to measure energy consumption while performing the testing process.

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