Hybrid Regression Testing Based on Path Pruning

2015 ◽  
Vol 5 (1) ◽  
pp. 35-55 ◽  
Author(s):  
Varun Gupta ◽  
Durg Singh Chauhan ◽  
Kamlesh Dutta
Keyword(s):  
Fault Detection ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Testing Time ◽  
Ripple Effect ◽  
Web Based ◽  
Testing Approach ◽  
Globally Distributed

Regression testing has been studied by various researchers for developing and testing the quality of software. Regression testing aims at re-execution of evolved software code to ensure that no new errors had been introduced during the process of modification. Since re-execution of all test cases is not feasible, selecting manageable number of test cases to execute modified code with good fault detection rate is a problem. In past few years, various hybrid based regression testing approaches have been proposed and successfully employed for software testing, aiming at reduction in the number of test cases and higher fault detection capabilities. These techniques are based on sequence of selections, prioritizations and minimization of test suite. However, these techniques suffer from major drawbacks like improper consideration of control dependencies, neglection of unaffected fragments of code for testing purpose. Further, these techniques have been employed on hypothetical or simple programs with test suites of smaller size. Present paper proposes hybrid regression testing, a combination of test case selections, test case prioritizations and test suite minimization. The technique works at statement level and is based on finding the paths containing statements that affects or gets affected by the addition/deletion or modification (both control and data dependency) of variables in statements. The modification in the code may cause ripple effect thereby resulting into faulty execution of the code. The hybrid regression testing approach is aimed at detecting such faults with lesser number of test cases. Reduction in number of test cases is possible because of the decreased number of paths to be tested. A web based framework to automate and parallelize this testing technique to maximum extend, making it well suited for globally distributed environments is also proposed in the present paper. Framework when implemented as a tool can handle large pool of test cases and will make use of parallel MIMD architectures like multicore systems. Technique is applied on prototype live system and results are compared with recently proposed hybrid regression testing approach against parameters of interest. Obtained optimized results are indicators of effectiveness of approach in terms of reduction in effort, cost as well as testing time in general and increment delivery time in particular.

An Optimal Hybrid Regression Testing Approach Based on Code Path Pruning

2018 ◽  
pp. 265-286
Author(s):  
Varun Gupta
Keyword(s):  
Fault Detection ◽  
Detection Rate ◽  
Source Code ◽  
Hybrid Approach ◽  
Regression Testing ◽  
Test Suite ◽  
Test Cases ◽  
Hybrid Approaches ◽  
Testing Approach ◽  
Better Than

Hybrid regression testing approaches involve the combinations of test suite selections, prioritizations, and minimizations. The hybrid approaches must reduce size of test suite to minimal level and enhance fault detection rate. The chapter proposes a new hybrid regression testing approach that reduces the number of test cases by reducing the paths of source code on the basis of the dependency between the statements and the changes. The proposed technique is evaluated to be better than the existing hybrid approach in terms of percentage savings in test cases and fault detection rate.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

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 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 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.

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.

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.

COST-COGNIZANT COMBINATORIAL TEST CASE PRIORITIZATION

2011 ◽  
Vol 21 (06) ◽  
pp. 829-854 ◽  
Author(s):  
ZIYUAN WANG ◽  
LIN CHEN ◽  
BAOWEN XU ◽  
YAN HUANG
Keyword(s):  
Physical Meaning ◽  
Resource Constraints ◽  
Regression Testing ◽  
Experimental Results ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Combinatorial Testing

Combinatorial testing has been widely used in practice. People usually assume all test cases in combinatorial test suite will run completely. However, in many scenarios where combinatorial testing is needed, for example the regression testing, the entire combinatorial test suite is not run completely as a result of test resource constraints. To improve the efficiency of testing, combinatorial test case prioritization technique is required. For the scenario of regression testing, this paper proposes a new cost-cognizant combinatorial test case prioritization technique, which takes both combination weights and test costs into account. Here we propose a series of metrics with physical meaning, which assess the combinatorial coverage efficiency of test suite, to guide the prioritization of combinatorial test cases. And two heuristic test case prioritization algorithms, which are based on total and additional techniques respectively, are utilized in our technique. Simulation experimental results illustrate some properties and advantages of proposed technique.

TEST AGENT SYSTEM

2002 ◽  
Vol 12 (03) ◽  
pp. 269-290 ◽  
Author(s):  
JEONGEUN CHOI ◽  
BYOUNGJU CHOI
Keyword(s):  
Fault Detection ◽  
Test Case ◽  
Test Cases ◽  
Testing Time ◽  
Agent System ◽  
Social Ability ◽  
Test Agent ◽  
Regression Test ◽  
Agent Characteristics ◽  
Active Assistance

In this paper, we propose a Test Agent System (TAS) that employs intelligent agent characteristics of autonomy, social ability, and intelligence to provide active assistance to the tester. It consists of the 'User Interface Agent', the 'Test Case Selection & Testing Agent', and the 'Regression Test Agent'. These agents carry out the tests autonomously by employing their intelligent rules in the object-oriented test processes. This system has 2 advantages. Firstly, since the tests are carried out autonomously, it minimizes tester interference and secondly, since redundant-free and consistent effective test cases are intellectually selected, the testing time is reduced while the fault detection effectiveness improves. In this paper, we show the cut-down rate on the testing time and improvement in the fault detection effectiveness through experiments.

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.

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