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.

Test Case Selection: A Systematic Literature Review

2014 ◽  
Vol 24 (04) ◽  
pp. 653-676 ◽  
Author(s):  
Everton Note Narciso ◽  
Márcio Eduardo Delamaro ◽  
Fátima De Lourdes Dos Santos Nunes
Keyword(s):  
Resource Constraints ◽  
Test Case ◽  
Test Cases ◽  
Random Testing ◽  
Test Case Selection ◽  
Test Strategies ◽  
Case Selection ◽  
Definition Of ◽  
Selection Of ◽  
Leading Journals

Time and resource constraints should be taken into account in software testing activities, and thus optimizing the test suite is fundamental in the development process. In this context, the test case selection aims to eliminate redundant or unnecessary test data, which is crucial for the definition of test strategies. This paper presents a systematic review on the test case selection conducted through a selection of 449 articles published in leading journals and conferences in Computer Science. We addressed the state-of-art by collecting and comparing existing evidence on the methods used in the different software domains and the methods used to evaluate the test case selection. Our study identified 32 papers that met the research objectives, which featured 18 different selection methods and were evaluated through 71 case studies. The most commonly reported methods are adaptive random testing, genetic algorithms and greedy algorithm. Most approaches rely on heuristics, such as diversity of test cases and code or model coverage. This paper also discusses the key concepts and approaches, areas of application and evaluation metrics inherent to the methods of test case selection available in the literature.

scholarly journals K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

2021 ◽  
Vol 27 (2) ◽  
pp. 170-189
Author(s):  
P. K. Gupta
Keyword(s):  
Genetic Algorithm ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Average Percentage ◽  
Code Coverage ◽  
Test Case Selection ◽  
Case Selection

Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

Test Case Reduction Using Data Mining Technique

2016 ◽  
Vol 4 (4) ◽  
pp. 56-70 ◽  
Author(s):  
Ahmad A. Saifan ◽  
Emad Alsukhni ◽  
Hanadi Alawneh ◽  
Ayat AL Sbaih
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Data Mining Technique ◽  
Code Coverage ◽  
Test Case Selection ◽  
Data Mining Approach ◽  
Case Selection ◽  
Using Data

Software testing is a process of ratifying the functionality of software. It is a crucial area which consumes a great deal of time and cost. The time spent on testing is mainly concerned with testing large numbers of unreliable test cases. The authors' goal is to reduce the numbers and offer more reliable test cases, which can be achieved using certain selection techniques to choose a subset of existing test cases. The main goal of test case selection is to identify a subset of the test cases that are capable of satisfying the requirements as well as exposing most of the existing faults. The state of practice among test case selection heuristics is cyclomatic complexity and code coverage. The authors used clustering algorithm which is a data mining approach to reduce the number of test cases. Their approach was able to obtain 93 unique effective test cases out a total of 504.

Design and Implementation of Test Case Selection Based on Map Reduce

2014 ◽  
Vol 654 ◽  
pp. 378-381
Author(s):  
Yu Lin Liu ◽  
Yan Wang ◽  
Jian Tao Zhou
Keyword(s):  
Cloud Computing ◽  
Software Testing ◽  
Map Reduce ◽  
Test Case ◽  
Test Cases ◽  
Large Collection ◽  
Test Case Selection ◽  
Design And Implementation ◽  
Case Selection ◽  
Hadoop Platform

In the traditional software testing, a large collection of test cases of the tested system automatically generated , in the process of actual execution, all of the test cases are executed is not possible. Normally, we test a certain function of the tested system, so choosing the test cases about a certain function is very important. This paper focuses on solving the problem of choosing test cases about a certain function of the tested system based on CPN model, the method which is based on purpose is used in this process. In the process of test cases selection, there are a whole lot of repeated calculation and operation, this characteristic just can make it combined with the parallel advantage of cloud computing. In summary, this dissertation focus on the test cases selection problem, using MapReduce programming on Hadoop platform, a test case selection tool is designed to improve the efficiency and service capabilities of test selection, the result of the experiment is consistent with the expected result.

scholarly journals AVISAR – An Automated Framework for Test Case Selection & Prioritization using GA for OOS.

2020 ◽  
Vol 9 (4) ◽  
pp. 1556-1563
Keyword(s):  
Large Scale ◽  
Object Oriented ◽  
Test Case ◽  
Test Cases ◽  
Unified Approach ◽  
Test Case Selection ◽  
Case Selection ◽  
Object Oriented Systems ◽  
Efficient Selection ◽  
Selection Of

In this paper we have presented an automated unified approach called AVISAR for the testing of the Object-oriented Systems (OOS) by Test Case Prioritization (TCP) & their selection using Genetic Algorithm for the OOS. The testing of OOS has become a more challenging task as nowadays it has been widely accepted as a paradigm for large-scale system designing. In this research paper we have also studied the Genetic algorithms in relation to their applications for providing solutions to the various aspects of the OO Testing. As a result after implementing the tool AVISAR using GA’s it has proven to be useful in providing effective solutions to resolve the issues related to the OO Testing domain. Thus it can be used for reducing the efforts of the users for testing by efficient selection of effective test cases

scholarly journals Multi-Objective Test Case Selection: Local Search Approaches for the NSGA-II algorithm

2020 ◽  
Author(s):  
Luciano Soares De Souza
Keyword(s):  
Local Search ◽  
Software Testing ◽  
Search Algorithms ◽  
Selection Problem ◽  
Objective Test ◽  
Test Case ◽  
Nsga Ii ◽  
Test Case Selection ◽  
Multi Objective ◽  
Case Selection

The software testing process can be very expensive and it is important to find ways in order to reduce its costs. Test case selection techniques can be used in order to reduce the amount of tests to execute and this way reducing the costs. Search algorithms are very promising approach to deal with the test case selection problem. This work proposes new hybrid algorithms for multiobjective test case selection by adding local search mechanisms into the NSGAII algorithm. The results showed that some of the mechanisms were capable of improve the NSGA-II algorithm.

Test Case Selection Using Feature Extraction and Clustering

2018 ◽  
Vol 8 (2) ◽  
pp. 18-31 ◽  
Author(s):  
Angelin Gladston ◽  
H. Khanna Nehemiah ◽  
P. Narayanasamy ◽  
A. Kannan
Keyword(s):  
Feature Extraction ◽  
Test Case ◽  
Test Cases ◽  
Test Case Selection ◽  
Case Selection ◽  
Function Call ◽  
Clustering Approach ◽  
Execution Pattern ◽  
Profile Approach ◽  
Selection Of

This article explains the selection of important parameters from an execution pattern which brings out the details of the application of test cases. Hence, execution profiles are captured and a new execution profile-based clustering approach is chosen for test case selection, which uses three new features. These are Function frequency, Branches taken and Block percentage. The test cases are clustered using the extracted features. The experiments show that the proposed FBB selects smaller size of more relevant test cases which are more fault revealing compared to the existing Function Call Profile approach.

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.

Optimal Test Case Selection Using Ant Colony and Rough Sets

2020 ◽  
Vol 11 (2) ◽  
pp. 1-14
Author(s):  
Angelin Gladston ◽  
Niranjana Devi N.
Keyword(s):  
Optimization Technique ◽  
Computational Effort ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Optimal Test ◽  
Average Percentage ◽  
Test Case Selection ◽  
Case Selection

Test case selection helps in improving quality of test suites by removing ambiguous, redundant test cases, thereby reducing the cost of software testing. Various works carried out have chosen test cases based on single parameter and optimized the test cases using single objective employing single strategies. In this article, a parameter selection technique is combined with an optimization technique for optimizing the selection of test cases. A two-step approach has been employed. In first step, the fuzzy entropy-based filtration is used for test case fitness evaluation and selection. In second step, the improvised ant colony optimization is employed to select test cases from the previously reduced test suite. The experimental evaluation using coverage parameters namely, average percentage statement coverage and average percentage decision coverage along with suite size reduction, demonstrate that by using this proposed approach, test suite size can be reduced, reducing further the computational effort incurred.

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