Reliable code coverage technique in software testing

2013 ◽  
Author(s):  
D. N. Rao ◽  
M. V. Srinath ◽  
P. Hiranmani Bala
Keyword(s):  
Software Testing ◽  
Code Coverage

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.

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.

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.

scholarly journals A Stochastic Method for Test Case Selection in Software Testing

2020 ◽  
Vol 8 (4S5) ◽  
pp. 265-271
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Code Coverage ◽  
Test Case Selection ◽  
Redundant Data ◽  
Case Selection ◽  
Selection Of

The quality of the software is a very important aspect in the development of software application. In order to make sure there is the software of good quality, testing is a critical activity of software development. Thus, software testing is the activity which focuses on the computation of an attribute or the ability of either a system or program that decides if user requirements are met. There is a proper strategy for the design of software for which testing has to be adopted. The techniques of test case selection attempt at reduction of the test cases that need to be executed at the same time satisfying the needs of testing that has been denoted by the test criteria. In the time of software testing, and the resource will be the primary constraints at the time of testing since this has been a highly neglected phase in the Software Development Life Cycle (SDLC). The optimizing of a test suite is very critical for the reduction of the testing phase and also the selection of the test cases that eliminate unwanted or redundant data. All work in literature will make use of techniques of single objective optimization that does not have to be efficient as the code coverage will play an important role at the time of selection of test case. As the test case choice is Non-Deterministic, the work also proposes a novel and multi-objective algorithm like the Non-Dominated Sorting Genetic Algorithm II (NSGA II) and the Stochastic Diffusion Search (SDS) algorithm that makes use of the cost of execution and code coverage as its objective function. The results prove a faster level of convergence of the algorithm with better coverage of code in comparison to the NSGA II.

Markov approach for quantifying the software code coverage using genetic algorithm in software testing

2019 ◽  
Vol 14 (1) ◽  
pp. 27 ◽  
Author(s):  
M. Boopathi ◽  
R. Sujatha ◽  
C. Senthil Kumar ◽  
S. Narasimman ◽  
A. Rajan
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Code Coverage ◽  
Software Code

Research on Embedded Software Testing Based on Code Coverage and Variable Watching

2012 ◽  
Vol 263-266 ◽  
pp. 1694-1699
Author(s):  
Song Yang Du ◽  
Hua Yang ◽  
Ying Zhang ◽  
Zhong Wei Chen
Keyword(s):  
Embedded System ◽  
Software Testing ◽  
Real Time ◽  
Embedded Software ◽  
Code Coverage ◽  
Testing Tools ◽  
Normal Method ◽  
The Embedded System ◽  
Strict Requirement

The limitation of traditional testing tools makes it difficult to test the software of embedded system--the resource insufficiency of the embedded system as well as the strict requirement of real-time all contribute to the unavailability of the normal method for software testing. This document introduced a method about testing of embedded software, which based on code coverage and variable watching by using the software of Testbed and RTInsight. By using this method, it’s effective to validate the software testing of embedded system.

scholarly journals A novel algorithm to moderate the cost of scrutinized paths

2017 ◽  
Vol 7 (1.2) ◽  
pp. 225
Author(s):  
Grandhi Prasuna ◽  
O. Naga Raju ◽  
C. Hari Kishan
Keyword(s):  
Software Testing ◽  
Test Generation ◽  
Autonomous Vehicles ◽  
Optimization Method ◽  
System Level ◽  
Code Coverage ◽  
Path Testing ◽  
The Cost ◽  
Time Issue ◽  
Novel Algorithm

Software testing is all too often simply a bug hunt rather than a well-considered exercise in ensuring quality. More methodical models than a simple cycle of system-level test-fail-patch-test will be required to deploy safe autonomous vehicles at scale. There are many types of software testing is used to test software. Efferent systems and procedure are proposed for dealing with these issues. Utilization of transformative calculations for programmed test generation has been a territory of intrigue. This assignment should be possible on a premise of the Ant Colony Optimization method (ACO) of Swarm Intelligence as it isn't profoundly contemplated yet. Intends to locate the most limited way and Resolve the time issue. We are building up extra particular way to deal with testing by concentrating on those parts that are most critical so these ways can be tried first recognizing the most huge ways, the testing productivity can be expanded. Great results are discovered astoundingly expediently when GA is actualized. Producing an improved test suite (TS) is meta-heuristic issue, which can be settled by GA. The only objective of programming is not to determine the algorithm to accomplish a result but relevance and correctness of the result. Also, Furthermore, to be ascertained. Genetic Algorithm is a meta-heuristic algorithm, is employed for optimizing path testing to achieve total code coverage.

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