scholarly journals Optimizing compilation with preservation of structural code coverage metrics to support software testing

2012 ◽  
Vol 24 (3) ◽  
pp. 184-218 ◽  
Author(s):  
Raimund Kirner ◽  
Walter Haas
Keyword(s):  
Software Testing ◽  
Code Coverage ◽  
Coverage Metrics ◽  
Support Software ◽  
Structural Code

Green DRCT

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.

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.

Loop Transparency for Scalable Dynamic Symbolic Execution

2012 ◽  
Vol 201-202 ◽  
pp. 242-245
Author(s):  
Xiao Li Ji ◽  
Xiao Song Zhang ◽  
Ting Chen ◽  
Xiao Shan Li ◽  
Lei Jiang
Keyword(s):  
Software Testing ◽  
Symbolic Execution ◽  
Test Program ◽  
New Approach ◽  
Code Coverage ◽  
Testing Tool ◽  
Dynamic Symbolic Execution ◽  
Decision Graph ◽  
Dynamic Software ◽  
Number Of Iterations

Dynamic symbolic execution is a promising approach for software analyzing and testing. However, it fails to scale to large programs due to the exponential number of paths to be explored. This paper focus on tackling loop caused path explosion problems and proposes a new approach to reduce paths that produce the same effects. We present a loop transparency strategy that makes use of the decision graph of under test program to discard constraints that produce paths with only a different number of iterations. A dynamic software testing tool LTDse based on loop transparency is designed and evaluated on three benchmarks. The experimental results show that our approach is effective since it can achieve better code coverage or require fewer program executions than traditional strategies.

ON THE PROMISE OF NEURAL NETWORKS TO SUPPORT SOFTWARE TESTING

1998 ◽  
pp. 3-32 ◽  
Author(s):  
Anneliese von Mayrhauser ◽  
Charles W. Anderson ◽  
Tom Chen ◽  
Richard Mraz ◽  
C. A. Gideon
Keyword(s):  
Neural Networks ◽  
Software Testing ◽  
Support Software
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