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.

Test Case Prioritization based on Early Fault-Detection Technique

2019 ◽  
Vol 12 ◽  
Author(s):  
Dharmveer Kumar Yadav ◽  
Sandip Dutta
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Object Oriented ◽  
Regression Testing ◽  
Object Oriented Programming ◽  
Test Case ◽  
Test Cases ◽  
Novel Approach ◽  
Fuzzy Logic Technique

egression testing is time consuming and expensive activity in software testing. In Regression testing when any changes made to already tested program it should not affect to other part of program. When some part of code is modified then it is necessary to validate the modified code. Throughout regression testing test case from test suite will be re-executed and re-execution of all the test case will be very expensive. We present fault based prioritization using fuzzy logic approach for object oriented software. We developed fuzzy expert model helps to takes better decision than other expert system for regression testing. Proposed work focus on concept of fault detection rate, execution time and coverage to select the test cases for prioritization purpose.We have taken case study and evaluated our work which shows proposed new framework gives better result than other approach. We present a novel approach for prioritization of test cases for object oriented programming using fuzzy logic technique during regression testing. We developed the proposed methodology, we apply fuzzy logic method for effective prioritization of test case. We have used case study of various programs, and the results are promising compared to other approach.

Test case prioritization and selection technique in continuous integration development environments: a case study

2018 ◽  
Vol 7 (2.28) ◽  
pp. 332 ◽  
Author(s):  
Lei Xiao ◽  
Huaikou Miao ◽  
Ying Zhong
Keyword(s):  
Execution Time ◽  
Regression Testing ◽  
Time Constraints ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Selection Technique ◽  
Continuous Integration ◽  
Study Results

Regression testing is a very important activity in continuous integration development environments. Software engineers frequently integrate new or changed code that involves in a new regression testing. Furthermore, regression testing in continuous integration development environments is together with tight time constraints. It is also impossible to re-run all the test cases in regression testing. Test case prioritization and selection technique are often used to render continuous integration processes more cost-effective. According to multi objective optimization, we present a test case prioritization and selection technique, TCPSCI, to satisfy time constraints and achieve testing goals in continuous integration development environments. Based on historical failure data, testing coverage code size and testing execution time, we order and select test cases. The test cases of the maximize code coverage, the shorter execution time and revealing the latest faults have the higher priority in the same change request. The case study results show that using TCPSCI has a higher cost-effectiveness comparing to the manually prioritization.  

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.

scholarly journals Empirical Study of the Effects of Different Similarity Measures on Test Case Prioritization

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Rongcun Wang ◽  
Shujuan Jiang ◽  
Deng Chen ◽  
Yanmei Zhang
Keyword(s):  
Euclidean Distance ◽  
Similarity Measures ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Distance Information ◽  
The Common ◽  
Global Similarity ◽  
Execution Order

Similarity-based test case prioritization algorithms have been applied to regression testing. The common characteristic of these algorithms is to reschedule the execution order of test cases according to the distances between pair-wise test cases. The distance information can be calculated by different similarity measures. Since the topologies vary with similarity measures, the distances between pair-wise test cases calculated by different similarity measures are different. Similarity measures could significantly influence the effectiveness of test case prioritization. Therefore, we empirically evaluate the effects of six similarity measures on two similarity-based test case prioritization algorithms. The obtained results are statistically analyzed to recommend the best combination of similarity-based prioritization algorithms and similarity measures. The experimental results, confirmed by a statistical analysis, indicate that Euclidean distance is more efficient in finding defects than other similarity measures. The combination of the global similarity-based prioritization algorithm and Euclidean distance could be a better choice. It generates not only higher fault detection effectiveness but also smaller standard deviation. The goal of this study is to provide practical guides for picking the appropriate combination of similarity-based test case prioritization techniques and similarity measures.

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 Improving Fault Detection Rate using Similarity-based Test Case Prioritization in Regression Testing

2019 ◽  
Vol 9 (1) ◽  
pp. 1874-1880
Keyword(s):  
Rapid Evolution ◽  
Regression Testing ◽  
Maximum Efficiency ◽  
Test Case ◽  
Test Cases ◽  
Customer Requirements ◽  
Average Percentage ◽  
Software Developer ◽  
Complete Test

Rapid evolution in software requires regression testing to be performed as an essential activity which validates the software before the next release. Where software developer may add or removes intended features to maintain the software according to the customer requirements. In that case, complete test cases execution is nearly infeasible due to limited time and resources. So, the main aim of prioritization is to test any software with minimal time and maximum efficiency in terms of fault coverage rate. This paper proposed different similarity-based prioritization techniques to provide ranking to the test cases based on their influence level which is computed as similarity degree in three levels for the software to be tested. Each level represents the integration of selected coverage criteria’s. In order to validate our proposed technique, we have conducted a case study to measure its effectiveness in prioritizing the test cases. We experimentally observed that by incorporating a similarity-based approach with more than one coverage criteria; results for similarity-based prioritization are promising than any other conventional coverage based approaches in terms of Average Percentage of Faults Detected.

scholarly journals Cuckoo Search Algorithm for Test Case Prioritization in Regression Testing

2019 ◽  
Vol 8 (3) ◽  
pp. 6004-6009
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Aggressive Approach ◽  
Traditional Approaches

There are countless optimization problems that have been accelerated by Nature Inspired Metaheuristic Optimization Algorithms (NIMOA) in the earlier decades. NIMOA have gained huge popularity owing to their effective results. In this study NIMOA namely, Cuckoo Search Algorithm (CSA) is used to prioritize (order) the test cases for Regression Testing (RT). Prioritizations aids in the execution of higher priority test cases to give early fault detection. This research adopts the aggressive approach of reproduction made by Cuckoos to prioritize the test cases for RT. Average Percentage of Fault Detected (APFD) metrics is used in this paper for validations of results. APFD metrics is used to compare the performance of CSA with Flower Pollination Algorithm (FPA) and traditional approaches for Test Case Prioritization (TCP). Two java applications are used for the study. CSA is implemented in Java on eclipse platform. It is learnt from the study that APFD results of CSA outperformed the FPA for both the applications namely Puzzle Game and AreaandPerimeter. It is inferred from the results that prioritized set of test cases given by NIMOA outperformed the APFD results of traditional approaches and also CSA performed better than the FPA for TCP.

scholarly journals The Application Of Machine Learning In Test Case Prioritization - A Review

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Elinda Kajo Mece ◽  
Kleona Binjaku ◽  
Hakik Paci
Keyword(s):  
Machine Learning ◽  
Regression Testing ◽  
Machine Learning Techniques ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Testing Time ◽  
Engineering Software ◽  
Learning Techniques ◽  
Learning Machine

Regression testing is very important but also a very costly and time-consuming activity that ensures the developers that changes in the application will not bring new errors. Retest all, selection of test cases and prioritization of test cases (TCP)  approaches are used to enhance the efficiency and effectiveness in regression testing. While test case selection techniques decrease testing time and cost, it can exclude some critical test cases that can detect the faults. On the other hand, test case prioritization considers all test cases and execute them until resources are exhausted or all test cases are executed, while always focusing on the most important ones. Over the years, machine learning has found wide usage in solving different problems in software engineering. Software development and maintenance problems can be defined as learning problems and machine learning techniques have shown to be very effective in solving these problems. In the range of application of machine learning, machine learning techniques have also found usage in solving the test case prioritization problem. In this paper, we investigate the application of machine learning techniques in test case prioritization. We survey some of the most recent studies made in this field and provide information like techniques of machine learning used in TCP process, metrics used to measure the effectiveness of the proposed methods, data used to define the priority of test cases and some advantages or limitations of application of machine learning in TCP.

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