scholarly journals Test Case Prioritization Algorithm Based Upon Modified Code Coverage in Regression Testing

2012 ◽  
Vol 3 (6) ◽  
pp. 29-37
Author(s):  
Usha Badhera
Keyword(s):  
Regression Testing ◽  
Test Case ◽  
Test Case Prioritization ◽  
Code Coverage

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.

scholarly journals Multiobjective Test Case Prioritization Using Test Case Effectiveness: Multicriteria Scoring Method

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ali Samad ◽  
Hairulnizam Bin Mahdin ◽  
Rafaqat Kazmi ◽  
Rosziati Ibrahim ◽  
Zirawani Baharum
Keyword(s):  
Fault Detection ◽  
Execution Time ◽  
Regression Testing ◽  
Size Reduction ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Detection Ability ◽  
Code Coverage ◽  
Multiobjective Particle Swarm Optimization

Modified source code validation is done by regression testing. In regression testing, the time and resources are limited, in which we have to select the minimal test cases from test suites to reduce execution time. The test case minimization process deals with the optimization of the regression testing by removing redundant test cases or prioritizing the test cases. This study proposed a test case prioritization approach based on multiobjective particle swarm optimization (MOPSO) by considering minimum execution time, maximum fault detection ability, and maximum code coverage. The MOPSO algorithm is used for the prioritization of test cases with parameters including execution time, fault detection ability, and code coverage. Three datasets are selected to evaluate the proposed MOPSO technique including TreeDataStructure, JodaTime, and Triangle. The proposed MOPSO is compared with the no ordering, reverse ordering, and random ordering technique for evaluating the effectiveness. The higher values of results represent the more effectiveness and the efficiency of the proposed MOPSO as compared to other approaches for TreeDataStructure, JodaTime, and Triangle datasets. The result is presented to 100-index mode relevant from low to high values; after that, test cases are prioritized. The experiment is conducted on three open-source java applications and evaluated using metrics inclusiveness, precision, and size reduction of a matrix of the test suite. The results revealed that all scenarios performed well in acceptable mode, and the technique is 17% to 86% more effective in terms of inclusiveness, 33% to 85% more effective in terms of precision, and 17% minimum to 86% maximum in size reduction of metrics.

Regression Test Cases Prioritization Using Clustering and Code Change Relevance

2016 ◽  
Vol 26 (05) ◽  
pp. 733-768 ◽  
Author(s):  
Ramzi A. Haraty ◽  
Nashat Mansour ◽  
Lama Moukahal ◽  
Iman Khalil
Keyword(s):  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Specific Test ◽  
Code Coverage ◽  
Code Change ◽  
Prioritization Method ◽  
Code Complexity ◽  
The Cost

Regression testing is important for maintaining software quality. However, the cost of regression testing is relatively high. Test case prioritization is one way to reduce this cost. Test case prioritization techniques sort test cases for regression testing based on their importance. In this paper, we design and implement a test case prioritization method based on the location of a change. The method consists of three steps: (1) clustering test cases, (2) prioritizing the clusters with respect to the relevance of the clusters to a code change, and (3) test case prioritization within each cluster based on metrics. We propose a metric for measuring test case importance based on Requirement Complexity, Code Complexity, and Code Coverage. To evaluate our method, we apply it on a launch interceptor problem program, and measure the inclusiveness and precision for clusters of test cases with respect to code change in specific test cases. Our results show that our proposed change-based prioritization method increases the likelihood of executing more relevant test cases earlier.

scholarly journals Model-Based Test Case Prioritization Using an Alternating Variable Method for Regression Testing of a UML-Based Model

2020 ◽  
Vol 10 (21) ◽  
pp. 7537
Author(s):  
Ki-Wook Shin ◽  
Dong-Jin Lim
Keyword(s):  
Mutation Testing ◽  
Regression Testing ◽  
Variable Method ◽  
Test Case ◽  
Test Case Prioritization ◽  
Code Coverage ◽  
Model Based ◽  
Mutation Operators ◽  
Alternating Variable Method ◽  
Development Approach

Many test case prioritization (TCP) studies based on regression testing using a code-based development approach have appeared. However, few studies on model-based mutation testing have explored what kind of fault seeding is appropriate or how much the code-based results differ. In this paper, as automatic seeding for the mutation generation, several mutation operators were employed for the UML statechart. Here, we suggest mutation testing employing the model-based development approach and a new TCP method based on an alternating variable method (AVM). We statistically compare the average percentage of the fault detection (APFD) results of the new method to other TCP methods such as a greedy algorithm for code coverage or fault exposure possibility. Finally, in empirical studies, the model-based TCP results for a power window switch module, a body control module, and a passive entry and start system are derived; these are real industrial challenges in the automotive industry.

scholarly journals Using Fuzzy Logic in Test Case Prioritization for Regression Testing Programs with Assertions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ali M. Alakeel
Keyword(s):  
Fuzzy Logic ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Software Developers ◽  
Novel Approach ◽  
History Of ◽  
Supporting Tool

Program assertions have been recognized as a supporting tool during software development, testing, and maintenance. Therefore, software developers place assertions within their code in positions that are considered to be error prone or that have the potential to lead to a software crash or failure. Similar to any other software, programs with assertions must be maintained. Depending on the type of modification applied to the modified program, assertions also might have to undergo some modifications. New assertions may also be introduced in the new version of the program, while some assertions can be kept the same. This paper presents a novel approach for test case prioritization during regression testing of programs that have assertions using fuzzy logic. The main objective of this approach is to prioritize the test cases according to their estimated potential in violating a given program assertion. To develop the proposed approach, we utilize fuzzy logic techniques to estimate the effectiveness of a given test case in violating an assertion based on the history of the test cases in previous testing operations. We have conducted a case study in which the proposed approach is applied to various programs, and the results are promising compared to untreated and randomly ordered test cases.

scholarly journals Evolutionary Cost-cognizant Test Case Selection and Prioritization for Object-Oriented Programs

2019 ◽  
Vol 8 (6S3) ◽  
pp. 149-153
Keyword(s):  
Unit Cost ◽  
Object Oriented ◽  
Arbitrary Point ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Integration Testing ◽  
Maximum Benefit ◽  
Regression Test

Test case prioritization (TCP) is a software testing technique that finds an ideal ordering of test cases for regression testing, so that testers can obtain the maximum benefit of their test suite, even if the testing process is stop at some arbitrary point. The recent trend of software development uses OO paradigm. This paper proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing. Path-based integration testing will identify the possible execution path and extract these paths from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the severity detection per unit cost for each of the dependent faults. The proposed technique was known as Evolutionary Cost-Cognizant Regression Test Case Prioritization (ECRTP) and being implemented as regression testing approach for experiment.

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

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

Test Pair Selection for Test Case Prioritization in Regression Testing for WS-BPEL Programs

2013 ◽  
Vol 10 (1) ◽  
pp. 73-102 ◽  
Author(s):  
Lijun Mei ◽  
Yan Cai ◽  
Changjiang Jia ◽  
Bo Jiang ◽  
W.K. Chan
Keyword(s):  
Web Services ◽  
Structural Similarity ◽  
Regression Testing ◽  
Test Pair ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Empirical Results ◽  
Selection For

Many web services not only communicate through XML-based messages, but also may dynamically modify their behaviors by applying different interpretations on XML messages through updating the associated XML Schemas or XML-based interface specifications. Such artifacts are usually complex, allowing XML-based messages conforming to these specifications structurally complex. Testing should cost-effectively cover all scenarios. Test case prioritization is a dimension of regression testing that assures a program from unintended modifications by reordering the test cases within a test suite. However, many existing test case prioritization techniques for regression testing treat test cases of different complexity generically. In this paper, the authors exploit the insights on the structural similarity of XML-based artifacts between test cases in both static and dynamic dimensions, and propose a family of test case prioritization techniques that selects pairs of test case without replacement in turn. To the best of their knowledge, it is the first test case prioritization proposal that selects test case pairs for prioritization. The authors validate their techniques by a suite of benchmarks. The empirical results show that when incorporating all dimensions, some members of our technique family can be more effective than conventional coverage-based techniques.

Regression Test Case Prioritization Based on Fixed Size Candidate Set ART Algorithm

2020 ◽  
Vol 30 (03) ◽  
pp. 291-320
Author(s):  
Rongcun Wang ◽  
Zhengmin Li ◽  
Shujuan Jiang ◽  
Chuanqi Tao
Keyword(s):  
Fault Detection ◽  
Empirical Studies ◽  
Cost Effective ◽  
Regression Testing ◽  
Test Case ◽  
Test Case Prioritization ◽  
Fixed Size ◽  
C Programs ◽  
Testing Algorithm ◽  
Candidate Set

Regression testing is a very time-consuming and expensive testing activity. Many test case prioritization techniques have been proposed to speed up regression testing. Previous studies show that no one technique is always best. Random strategy, as the simplest strategy, is not always so bad. Particularly, when a test suite has higher fault detection capability, the strategy can generate a better result. Nevertheless, due to the randomness, the strategy is not always as satisfactory as expected. In this context, we present a test case prioritization approach using fixed size candidate set adaptive random testing algorithm to reduce the effect of randomness and improve fault detection effectiveness. The distance between pair-wise test cases is assessed by exclusive OR. We designed and conducted empirical studies on eight C programs to validate the effectiveness of the proposed approach. The experimental results, confirmed by a statistical analysis, indicate that the approach we proposed is more effective than random and the total greedy prioritization techniques in terms of fault detection effectiveness. Although the presented approach has comparable fault detection effectiveness to ART-based and the additional greedy techniques, the time cost is much lower. Consequently, the proposed approach is much more cost-effective.

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