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 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.

STUDI ALGORITMA babelRTS UNTUK PELAKSANAAN REGRESSION TEST SELECTION (RST)

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Ani - Rahmani
Keyword(s):  
Execution Time ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Selection ◽  
Test Execution ◽  
Regression Test Selection ◽  
Regression Test ◽  
Long Time ◽  
Testing Techniques

Software testing (testing) is a crucial stage in software development. The success of the testing process will ensure the quality of the software. In the regression testing process, one issue is that not all test cases (retest all) in the test suite need to be executed. Retest all will consume massive resources, as well as a long time. Regression testing techniques seek to find ways to reduce test execution time. One of the regression testing techniques is test case selection, also known as regression test selection (RTS). This paper describes a study on babelRTS, an RTS algorithm, to see its effectiveness. Effectiveness is measured by comparing the execution time of the execution retest all and babelRTS. Experiments were carried out on five software under tests (SUT) that had some faults. Test cases are prepared by designing for each SUT. The results showed a reduction in time so that the effectiveness reached a maximum of 32%, and average of 23% .

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 Regression Test Selection for C# Programs

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Nashat Mansour ◽  
Wael Statieh
Keyword(s):  
Regression Testing ◽  
Solid Base ◽  
Second Phase ◽  
Test Cases ◽  
Class Diagram ◽  
Test Selection ◽  
Selection Technique ◽  
C Programs ◽  
Regression Test Selection ◽  
Regression Test

We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG) from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.

ComboRT: A New Approach for Generating Regression Test Cases for Evolving Programs

2016 ◽  
Vol 26 (06) ◽  
pp. 1001-1026 ◽  
Author(s):  
Xiaobing Sun ◽  
Xin Peng ◽  
Hareton Leung ◽  
Bin Li
Keyword(s):  
Impact Factor ◽  
Regression Testing ◽  
Software Systems ◽  
Test Case ◽  
Test Cases ◽  
Test Case Selection ◽  
Case Selection ◽  
Testing Techniques ◽  
The Impact ◽  
Ranked List

Regression testing is essential to ensure software quality during software evolution. Two widely-used regression testing techniques, test case selection and prioritization, are used to maximize the value of the continuously enlarging test suite. However, few works consider both these two techniques together, which decreases the usefulness of the independently studied techniques in practice. In the presence of changes during program evolution, regression testing is usually conducted by selecting the test cases that cover the impact results of the changes. It seldom considers the false-positives in the information covered. Hence, the effectiveness of such regression testing techniques is decreased. In this paper, we propose an approach, ComboRT, which combines test case selection and prioritization together to directly generate a ranked list of test cases. It is based on the impact results predicted by the change impact analysis (CIA) technique, FCA–CIA, which generates a ranked list of impacted methods. Test cases which cover these impacted methods are included in the new test suite. As each method predicted by FCA–CIA is assigned with an impact factor value corresponding to the probability of this method to be impacted, test cases are then ordered according to the impact factor values of the impacted methods. Empirical studies on four Java based software systems demonstrate that ComboRT can be effectively used for regression testing in object-oriented Java-based software systems during their evolution.

scholarly journals Efficient Regression Testing of Software Product Lines by Reducing Redundant Test Executions

2020 ◽  
Vol 10 (23) ◽  
pp. 8686
Author(s):  
Pilsu Jung ◽  
Sungwon Kang ◽  
Jihyun Lee
Keyword(s):  
Product Family ◽  
Software Product Lines ◽  
Product Line ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Product Lines ◽  
Test Selection ◽  
Software Product ◽  
Regression Test

Regression testing for software product lines (SPLs) is challenging because it must ensure that all the products of a product family work correctly whenever changes are made. One approach to reducing the cost of regression testing is the regression test selection (RTS), which selects a subset of regression test cases. However, even when RTS is applied, SPL regression testing can still be expensive because, in the product line context, each test case can be executed on more than one product that reuses the test case, which would typically result in a large number of test executions. A promising direction is to eliminate redundant test executions of test cases. We propose a method that, given a test case, identifies a set of products, on which the test case will cover the same sequence of source code statements and produce the same testing results, and then excludes these products from products to apply the test case to. The evaluation results showed that when the full selection approach and the approach of repetitively applying an RTS method for a single software system are used for test selection, our method reduced, respectively, 59.3% and 40.0% of the numbers of test executions of the approaches.

scholarly journals Test Case Selection and Prioritization for Regression Testing using Fault Severity

2012 ◽  
Vol 2 (3) ◽  
pp. 1171-1177
Author(s):  
Uma Sharma ◽  
Vedant Rastogi
Keyword(s):  
Fault Detection ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Performance Goal ◽  
Test Case Selection ◽  
Case Selection ◽  
Fault Severity

Regression testing is a significant but a very expensive testing process .Test case prioritization is a technique to schedule and execute the test cases in such an order that results in increasing their ability to meet some performance goal. One of the main goal is to increase the rate of fault detection –i.e. to detect the faults as early as possible during the testing process. Test case prioritization is used to minimize the expenses of regression testing. This paper  proposes  a technique  to select and prioritize the test cases and  results in   improving  the rate of fault detection.

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 Framework for Optimizing Test Cases in Regression Testing

2018 ◽  
Vol 7 (3.12) ◽  
pp. 444
Author(s):  
R P Mahapatra ◽  
Aparna Ranjith ◽  
A Kulothungan
Keyword(s):  
Fault Detection ◽  
Detection Rate ◽  
Selection Process ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Selection ◽  
Case Selection ◽  
Efficient Test ◽  
Software Engineers

Software once developed is subject to continuous changes. Software Regression Testing thus can be used to reduce the efforts of testing the software by selecting only the required number of test cases and ordering them to test the software after changes have been made to it. In order to improve the fault detection rate, the selection of efficient test cases and order of execution of these tests is important. Here is when the test case selection comes into picture where in, the fault detection rate during the working of any software has to be improved. The test case selection process will find the most efficient test cases which can fully functionally test the software that has been modified. This indeed will contribute to an improved fault detection rate which can provide faster feedback on the system under test and let software engineers begin correcting faults as early as possible. In this paper, an approach for test case selection is proposed which takes into consideration the effect of three parameters History, Coverage and Requirement all together in order to improve the selection process. This will also ensure that the rejection of some efficient test cases is reduced when compared to the selection process in conventional methods, most of them making use of a single parameter for test case selection. These Test cases are further optimized using Genetic Algorithm. Results indicate that the proposed technique is much more efficient in terms of selecting the test cases when compared to conventional techniques, thereby improving fault detection rate.  

Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GA

2018 ◽  
Vol 9 (3) ◽  
pp. 88-104
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Hybrid Approach ◽  
Regression Testing ◽  
Minimum Time ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Ant Colony Optimization Algorithm ◽  
Test Case Selection ◽  
Case Selection

This article describes about the application of search-based techniques in regression testing and compares the performance of various search-based techniques for software testing. Test cases tend to increase exponentially as the software is modified. It is essential to remove redundant test cases from the existing test suite. Regression testing is very costly and must be performed in restricted ways to ensure the validity of the existing software. There exist different methods to improve the quality of test cases in terms of the number of faults covered, opposed to the number of statements covered in a minimum time. Different methods exist for this purpose, such as minimization, test case selection, and test case prioritization. In this article, search-based methods are applied to improve the quality of the test suite in order to select a minimum set of test cases which covers all the statements in a minimum time. The whole approach is named search based regression testing. In this paper, the performance of different metaheuristics for test suite minimization problem is also compared with a hybrid approach of ant colony optimization algorithm and genetic algorithm.

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