scholarly journals Regression Test Selection for C# Programs

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Nashat Mansour ◽  
Wael Statieh

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.

Author(s):  
MATS SKOGLUND ◽  
PER RUNESON

One regression test selection technique proposed for object-oriented programs is the Class firewall regression test selection technique. The selection technique selects test cases for regression test, which test changed classes and classes depending on changed classes. However, in empirical studies of the application of the technique, we observed that another technique found the same defects, selected fewer tests and required a simpler, less costly, analysis. The technique, which we refer to as the Change-based regression test selection technique, is basically the Class firewall technique, but with the class firewall removed. In this paper we formulate a hypothesis stating that these empirical observations are not incidental, but an inherent property of the Class firewall technique. We prove that the hypothesis holds for Java in a stable testing environment, and conclude that the effectiveness of the Class firewall regression testing technique can be improved without sacrificing the defect detection capability of the technique, by removing the class firewall.


2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Ani - Rahmani

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% .


2021 ◽  
Vol 7 (1) ◽  
pp. 59
Author(s):  
Asri Maspupah ◽  
Akhmad Bakhrun

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.


2020 ◽  
Vol 10 (23) ◽  
pp. 8686
Author(s):  
Pilsu Jung ◽  
Sungwon Kang ◽  
Jihyun Lee

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.


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