scholarly journals A Systematic Analysis of Regression Test Case Selection: A Multi-Criteria-Based Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Muhammad Rehan ◽  
Norhalina Senan ◽  
Muhammad Aamir ◽  
Ali Samad ◽  
Mujtaba Husnain ◽  
...  
Keyword(s):  
Regression Testing ◽  
Test Case ◽  
Systematic Analysis ◽  
Code Coverage ◽  
Test Case Selection ◽  
Work Related ◽  
Case Selection ◽  
Applied Software ◽  
Regression Test ◽  
Two Parameters

In applied software engineering, the algorithms for selecting the appropriate test cases are used to perform regression testing. The key objective of this activity is to make sure that modification in the system under test (SUT) has no impact on the overall functioning of the updated software. It is concluded from the literature that the efficacy of the test case selection solely depends on the following metrics, namely, the execution cost of the test case, the lines of the code covered in unit time also known as the code coverage, the ability to capture the potential faults, and the code modifications. Furthermore, it is also observed that the approaches for the regression testing developed so far generated results by focusing on one or two parameters. In this paper, our key objectives are twofold: one is to explore the importance of the role of each metric in detail. The secondary objective is to study the combined effect of these metrics in test case selection task that is capable of achieving more than one objective. In this paper, a detailed and comprehensive review of the work related to regression testing is provided in a very distinct and principled way. This survey will be useful for the researchers contributing to the field of regression testing. It is noteworthy that our systematic literature review (SLR) included the noteworthy work published from 2007 to 2020. Our study observed that about 52 relevant studies focused on all of the four metrics to perform their respective tasks. The results also revealed that about 30% of the different categories of regression test case reported the results using metaheuristic regression test selection (RTS). Similarly, about 31% of the literature reported results using the generic regression test case selection techniques. Most of the researchers focus on the datasets, namely, Software-Artefact Infrastructure Repository (SIR), JodaTime, TreeDataStructure, and Apache Software Foundation. For validation purpose, following parameters were focused, namely, the inclusiveness, precision, recall, and retest-all.

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.

An Empirical Comparison of t-GSC and ACO_TCSP Applied to Time Bound Regression Test Selection

2019 ◽  
Vol 12 ◽  
Author(s):  
Nishtha Jatana ◽  
Bharti Suri
Keyword(s):  
Comparative Evaluation ◽  
Regression Testing ◽  
Test Case ◽  
Empirical Comparison ◽  
Test Selection ◽  
Greedy Approach ◽  
Test Case Selection ◽  
Regression Test Selection ◽  
Case Selection ◽  
Regression Test

Background: The paper presents a comparative evaluation of two techniques for test selection. Objective: The objective is to empirically evaluate the performance of a greedy approach and a search based approach for test case selection. Method: We conducted an experiment on 24 programs in different languages. Result: The time bound greedy approach yielded best result for 16 out of the 24 programs. The ACO approach could find the best result 100% times for 11 programs and at least 30-95% times for rest 13 programs by running ACO 10 times on each program. Yet the percentage reductions achieved in the size and execution time of the resultant test suite were almost similar in both the techniques. Conclusion: The results inspire the further use of both the techniques in regression testing.

scholarly journals PERBANDINGAN KEMAMPUAN REGRESSION TESTING TOOL PADA REGRESSION TEST SELECTION: STARTS DAN EKSTAZI

2021 ◽  
Vol 7 (1) ◽  
pp. 59
Author(s):  
Asri Maspupah ◽  
Akhmad Bakhrun
Keyword(s):  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Selection ◽  
Test Case Selection ◽  
Advantages And Disadvantages ◽  
Regression Test Selection ◽  
Case Selection ◽  
Regression Test ◽  
And Performance

Regression testing as an essential activity in software development that has changed requirements. In practice, regression testing requires a lot of time so that an optimal strategy is needed. One approach that can be used to speed up execution time is the Regression Test Selection (RTS) approach. Currently, practitioners and academics have started to think about developing tools to optimize the process of implementing regression testing. Among them, STARTS and Ekstazi are the most popular regression testing tools among academics in running test case selection algorithms. This article discusses the comparison of the capabilities of the STARTS and Ekstazi features by using feature parameter evaluation. Both tools were tested with the same input data in the form of System Under Test (SUT) and test cases. The parameters used in the tool comparisons are platform technology, test case selection, functionality, usability and performance efficiency, the advantages, and disadvantages of the tool. he results of the trial show the differences and similarities between the features of STARTS and Ekstazi, so that it can be used by practitioners to take advantage of tools in the implementation of regression testing that suit their needs. In addition, experimental results show that the use of Ekstazi is more precise in sorting out important test cases and is more efficient, when compared to STARTS and regression testing with retest all.

An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm

2020 ◽  
Vol 11 (1) ◽  
pp. 53-67 ◽  
Author(s):  
Arun Prakash Agrawal ◽  
Ankur Choudhary ◽  
Arvinder Kaur
Keyword(s):  
Cost Reduction ◽  
Test Suite ◽  
Test Case ◽  
Test Cost ◽  
Test Case Selection ◽  
Case Selection ◽  
Test Cost Reduction ◽  
Whale Optimization ◽  
Regression Test ◽  
Test Suite Optimization

Test suite optimization is an ever-demanded approach for regression test cost reduction. Regression testing is conducted to identify any adverse effects of maintenance activity on previously working versions of the software. It consumes almost seventy percent of the overall software development lifecycle budget. Regression test cost reduction is therefore of vital importance. Test suite optimization is the most explored approach to reduce the test suite size to re-execute. This article focuses on test suite optimization as a regression test case selection, which is a proven N-P hard combinatorial optimization problem. The authors have proposed an almost safe regression test case selection approach using a Hybrid Whale Optimization Algorithm and empirically evaluated the same on subject programs retrieved from the Software Artifact Infrastructure Repository with Bat Search and ACO-based regression test case selection approaches. The analyses of the obtained results indicate an improvement in the fault detection ability of the proposed approach over the compared ones with significant reduction in test suite size.

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