scholarly journals CIPC: A Change Impact Propagation Computing Based Technique for Microservice Regression Testing Prioritization

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lizhe Chen ◽  
Xiang Yu ◽  
Ji Wu ◽  
Haiyan Yang
Keyword(s):  
Input Data ◽  
Detection Rate ◽  
Belief Propagation ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Main Method ◽  
Change Impact ◽  
And Control ◽  
Execution Order

Regression testing is the optimal technique that can be used in each iteration of microservice systems. However, regression testing prioritization is the only main method that gives better results. These techniques directly involve the processes of artifacts, data acquisition, analysis, and maintenance. The microservice systems have input data, which are difficult to obtain and control, while such processes are of high costs with impractical design. This paper gives a detailed study on testing prioritization technique, which is referred to as CIPC. As there are dependencies between services from API gateway logs, a novel CIPC algorithm is proposed, which is based on belief propagation. There are some rules that are directly affected by service changes. Therefore, the higher execution order of test case prioritizes CIPC, which is based on impact changes. Multiobjective prioritization algorithm is based on heuristic searching, in which sequence test cases are done by coverage. By evaluating the effectiveness of CIPC, the empirical study presents five microservice systems and four different techniques. The results describe that CIPC has improved fault detection rate with acceptable time and cost. The technique is more practical than typical artifacts, which are based on increments of system scales.

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 Efficiency Enhancement in Regression Test Case Prioritization Technique

2019 ◽  
Vol 8 (12) ◽  
pp. 5445-5451
Keyword(s):  
Fault Detection ◽  
Software Quality ◽  
Detection Rate ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Efficiency Enhancement ◽  
Average Percentage ◽  
Regression Test ◽  
Expensive Process

Regression testing is an important, but expensive, process that has a powerful impact on software quality. Unfortunately all the test cases, existing and newly added, cannot be re-executed due to insufficient resources. In this scenario, prioritization of test case helps in improving the efficacy of regression testing by arranging the test cases in such a way that the most beneficial (that has the potential to detect the more number of faults) are executed first. Previous work and existing prioritization techniques, though detects faults, but there is a need of improved techniques to enhance the process of regression testing by improving the fault detection rate. The new technique, proposed in this paper, gives improved result than the existing ones. The comparison of the effectiveness of the proposed approach is done with other prioritization and non-prioritization orderings. The result of the proposed approach shows higher average percentage of faults detected (APFD) values. Also, the performance is evaluated and it is observed that the capability of the proposed method outperforms other algorithms by enhancing the fault detection rate.

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.  

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

A Microservice Regression Testing Selection Approach Based on Belief Propagation

2021 ◽  
Author(s):  
Lizhe Chen ◽  
Ji Wu ◽  
Haiyan Yang ◽  
Kui Zhang
Keyword(s):  
Directed Graph ◽  
Belief Propagation ◽  
Regression Testing ◽  
Change Propagation ◽  
Test Cases ◽  
Testing Time ◽  
Dependency Matrix ◽  
System Models ◽  
Selection Approach ◽  
Service Change

Abstract Regression testing is required in each iteration of microservice systems. Regression testing selection, which reduces testing costs by selecting a subset from the original test cases, is one of the main techniques to optimize regression testing. Existing techniques mainly rely on the information retrieved from artifacts such as code files and system models. For microservice systems with service autonomy, development method diversity and a large amount of services, such artifacts are too difficultly obtained and costly processed to apply those approaches. This paper presents a regression testing selection approach called MRTS-BP, which needs the API gateway layer logs instead of code files and system models as inputs. By parsing the API gateway layer logs, our approach establishes the service dependency matrix, which in further is transformed into a directed graph with the services as nodes. Then, to find out which test cases are affected by service changes, an algorithm based on belief propagation is presented to compute the quantitative results of service-change propagation from the directed graph. Finally, the relationships between original test cases and service-change propagation results are established to select test cases with three strategies. To evaluate the efficiency of MRTS-BP, the empirical study based on four microservice systems is presented. A typical technique RTS-CFG is compared with MRTS-CFG and four experiments are setup to investigate four research questions. The results show that MRTS-BP can not only reduce the number of test cases by half compared with the retest-all strategy while ensuring the safety, but also save at least 20% testing time costs more than that of RTS-CFG. MRTS-BP is more practical than the techniques relying on the artifacts when the latter cannot be implemented due to the artifacts are difficult to obtain and process.

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.

Search for Prioritized Test Cases during Web Application Testing

2019 ◽  
Vol 10 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Munish Khanna ◽  
Naresh Chauhan ◽  
Dilip Kumar Sharma
Keyword(s):  
Web Application ◽  
Web Applications ◽  
Greedy Algorithms ◽  
Regression Testing ◽  
Test Sequence ◽  
Test Case ◽  
Test Cases ◽  
Average Percentage ◽  
Heuristic Approaches ◽  
Better Than

Regression testing of evolving software is a critical constituent of the software development process. Due to resources constraints, test case prioritization is one of the strategies followed in regression testing during which a test case that satisfies predefined objectives the most, as the tester perceives, would be executed the earliest. In this study, all the experiments were performed on three web applications consisting of 65 to 100 pages with lines of code ranging from 5000 to 7000. Various state-of-the-art approaches such as, heuristic approaches, Greedy approaches, and meta heuristic approaches were applied so as to identify the prioritized test sequence which maximizes the value of average percentage of fault detection. Performance of these algorithms was compared using different parameters and it was concluded that the Artificial Bee Colony algorithm performs better than all. Two novel greedy algorithms are also proposed in the study, of which the goal is to smartly manage the state of a tie, where a tie exhibits the condition that all the test cases participating in the tie are of equal significance in achieving the objective. It has also been validated that the performance of these novel proposed algorithm(s) is better than that of traditionally followed greedy approach, most of the time.

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.

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