scholarly journals Machine Learning based Test Case Prioritization in Object Oriented Testing

2019 ◽  
Vol 8 (3) ◽  
pp. 700-707
Keyword(s):  
Software Maintenance ◽  
Object Oriented ◽  
Support Vector ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Testing Time ◽  
Design Metrics ◽  
Object Oriented Design ◽  
Number Of Children

Software maintenance is one of the most expensive activities in software life cycle. It costs nearly 70% of the total cost of the software. Either to adopt the new requirement or to correct the functionality, software undergoes maintenance. As a consequent of maintenance activities, software undergoes many reforms. Newly added software components may affect the working of existing components and also may introduce faults in existing components. The regression testing tries to reveal the faults that might have been introduced due to these reformations. Running all the prior existing test cases may not be feasible due to constraints like time, cost and resources. Test case prioritization may help in ordered execution of test cases. Running a faulty or fault prone component early in testing process may help in revealing more faults per unit of time. And hence may reduce the testing time. There have been many different criteria for assigning the priority to test cases. But none of the approaches so far have considered the object oriented design metrics for determining the priority of test cases. Object oriented design metrics have been empirically studied for their impact of software maintainability, reliability, testability and quality but usage of these metrics in test case prioritization is still an open area of research. The research reported in this paper evaluates subset of CK metrics. Metrics considered from CK suite include Coupling between objects (CBO), Depth of Inheritance tree (DIT), weighted methods per class (WMC), Number of children (NOC), and Response for a class (RFC). Study also considers four other metrics namely publically inherited methods (PIM), weighted attributes per class (WAC), number of methods inherited (NMI) and number of methods overridden. A model is built based on these metrics for the prediction of software quality and based on the quality measures software modules are classified with the help of Support Vector Machine (SVM) algorithm. The proposed approach is implemented in WEKA tool and analysed on experimental data extracted from open source software. Proposed work would firstly help the tester in identifying the low quality modules and then prioritize the test cases based on quality centric approach. The work also attempts to automate test case prioritization in object oriented testing. The results obtained are encouraging.

Test Case Prioritization Using Clustering Approach for Object Oriented Software

2019 ◽  
Vol 10 (3) ◽  
pp. 92-109 ◽  
Author(s):  
Dharmveer Kumar Yadav ◽  
Sandip Kumar Dutta
Keyword(s):  
Software Maintenance ◽  
Clustering Algorithm ◽  
Object Oriented ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Clustering Approach ◽  
The Cost ◽  
Effective Result

In the software maintenance activity, regression testing is performed for validing modified source code. Regression testing ensures that the modified code would not affect the earlier tested program. Due to a constraint of resources and time, regression testing is a time-consuming process and it is a very expensive activity. During the regression testing, a set of the test case and the existing test cases are reused. To minimize the cost of regression testing, the researchers proposed a test case prioritization based on clustering techniques. In recent years, research on regression testing has made significant progress for object-oriented software. The empirical results show the importance of K-mean clustering algorithm used to achieve an effective result. They found from experimental results that their proposed approach achieves the highest faults detected value than others.

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 An Integrated Decision Approach with Probabilistic Linguistic Information for Test Case Prioritization

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1857
Author(s):  
A. D. Shrivathsan ◽  
R. Krishankumar ◽  
Arunodaya Raj Mishra ◽  
K. S. Ravichandran ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Integrated Approach ◽  
Systematic Evaluation ◽  
Entropy Measure ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Testing Time ◽  
Linguistic Information ◽  
Software Companies ◽  
Weight Calculation

This paper focuses on an exciting and essential problem in software companies. The software life cycle includes testing software, which is often time-consuming, and is a critical phase in the software development process. To reduce time spent on testing and to maintain software quality, the idea of a systematic selection of test cases is needed. Attracted by the claim, researchers presented test case prioritization (TCP) by applying the concepts of multi-criteria decision-making (MCDM). However, the literature on TCP suffers from the following issues: (i) difficulty in properly handling uncertainty; (ii) systematic evaluation of criteria by understanding the hesitation of experts; and (iii) rational prioritization of test cases by considering the nature of criteria. Motivated by these issues, an integrated approach is put forward that could circumvent the problem in this paper. The main aim of this research is to develop a decision model with integrated methods for TCP. The core importance of the proposed model is to (i) provide a systematic/methodical decision on TCP with a reduction in testing time and cost; (ii) help software personnel choose an apt test case from the suite for testing software; (iii) reduce human bias by mitigating intervention of personnel in the decision process. To this end, probabilistic linguistic information (PLI) is adopted as the preference structure that could flexibly handle uncertainty by associating occurrence probability to each linguistic term. Furthermore, an attitude-based entropy measure is presented for criteria weight calculation, and finally, the EDAS ranking method is extended to PLI for TCP. An empirical study of TCP in a software company is presented to certify the integrated approach’s effectiveness. The strengths and weaknesses of the introduced approach are conferred by comparing it with the relevant methods.

A Multi-Factor Coverage based Test Case Prioritization Technique for Object Oriented Software

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Vedpal ◽  
Naresh Chauhan
Keyword(s):  
Fault Detection ◽  
Case Studies ◽  
Experimental Validation ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
C Language ◽  
The Cost

Test case prioritization technique creates the sequence of test cases for execution in such a way that the test cases with higher rate of fault detection are executed earlier than those test cases which have lower rate of fault detection. In this paper a new algorithm is proposed to prioritize the test cases based on coverage of object oriented programming factors. The factors are considered on the basis of complexity and probability of errors introduced by them. For the experimental validation and analysis the proposed test case prioritization algorithm is applied on two case studies. The analyzed case studies are implemented in C++ language. By using the presented algorithm it helps to reduce the cost and time for testing the software.

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.

scholarly journals A review of object-oriented approach for test case prioritization

2019 ◽  
Vol 16 (1) ◽  
pp. 429
Author(s):  
Umar Farooq ◽  
Hannani Aman ◽  
Aida Mustapha ◽  
Zainuri Saringat
Keyword(s):  
Fault Detection ◽  
Object Oriented ◽  
Primary Objective ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Development Lifecycle ◽  
Class Level ◽  
Software Development Lifecycle ◽  
Object Oriented Approach

<p>The most essential phase in regression testing is Test Case Prioritization (TCP), with its primary objective to increase the fault detection rate at different stages during testing. Prior to achieving the objective, existing evidence of techniques in TCP must be synthesized and analyzed. At present, fault detection for TCP based on object-oriented features only consider statement, module, and class level. The important features of object-oriented (OO) programming like inheritance and polymorphism have not been fully explored for fault detection in TCP. Such OO concepts are important for test case selection and in turn for ranking the test cases (prioritization). This paper reviews various test case prioritization techniques specific to OO systems. This review is hoped to highlight the importance and usage of TCP in relation to object-oriented software development lifecycle.</p>

Test Case Prioritization using Cuckoo Search

2012 ◽  
pp. 113-128 ◽  
Author(s):  
Praveen Ranjan Srivastava ◽  
D. V. Pavan Kumar Reddy ◽  
M. Srikanth Reddy ◽  
Ch. V. B. Ramaraju ◽  
I. Ch. Manikanta Nath
Keyword(s):  
Software Maintenance ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Specific Sequence ◽  
Scarcity Of Resources

Test Case prioritization consists of proper organization and scheduling of the test cases in a specific sequence. Regression testing is an important issue and concept during software maintenance process, but due to scarcity of resources re-execution of all test cases, is not possible during regression testing. Hence in version or revision specific regression testing, it is more important to execute those test cases that are beneficial. In this chapter, a new prioritization technique is proposed for version specific regression testing using Cuckoo Search Algorithm. This technique prioritizes the test cases based on lines of code where the code is modified.

scholarly journals A Slice-Based Change Impact Analysis for Regression Test Case Prioritization of Object-Oriented Programs

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
S. Panda ◽  
D. Munjal ◽  
D. P. Mohapatra
Keyword(s):  
Impact Analysis ◽  
Object Oriented ◽  
Test Suite ◽  
Optimal Order ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Regression Test ◽  
Static Approach ◽  
Change Impact

Test case prioritization focuses on finding a suitable order of execution of the test cases in a test suite to meet some performance goals like detecting faults early. It is likely that some test cases execute the program parts that are more prone to errors and will detect more errors if executed early during the testing process. Finding an optimal order of execution for the selected regression test cases saves time and cost of retesting. This paper presents a static approach to prioritizing the test cases by computing the affected component coupling (ACC) of the affected parts of object-oriented programs. We construct a graph named affected slice graph (ASG) to represent these affected program parts. We determine the fault-proneness of the nodes of ASG by computing their respective ACC values. We assign higher priority to those test cases that cover the nodes with higher ACC values. Our analysis with mutation faults shows that the test cases executing the fault-prone program parts have a higher chance to reveal faults earlier than other test cases in the test suite. The result obtained from seven case studies justifies that our approach is feasible and gives acceptable performance in comparison to some existing techniques.

Test Case Prioritization—ANT Algorithm With Faults Severity

2020 ◽  
Author(s):  
Andreea Vescan ◽  
Camelia-M Pintea ◽  
Petrică C Pop
Keyword(s):  
Selection Criterion ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Testing Time ◽  
Average Percentage ◽  
Prioritization Method ◽  
The Cost ◽  
Run Test

Abstract Regression testing is applied whenever a code changes, ensuring that the modifications fixed the fault and no other faults are introduced. Due to a large number of test cases to be run, test case prioritization is one of the strategies that allows to run the test cases with the highest fault rate first. The aim of the paper is to present an optimized test case prioritization method inspired by ant colony optimization, test case prioritization–ANT. The criteria used by the optimization algorithm are the number of faults not covered yet by the selected test cases and the sum of severity of the faults. The cost, i.e. time execution, for test cases is considered in the computation of the pheromone deposited on the graph’s edges. The average percentage of fault detected metric, as best selection criterion, is used to uncover maximum faults with the highest severity, and reducing the regression testing time. Several experiments are considered, detailed and discussed, comparing various algorithm parameter’s alternatives. A benchmark project is also used to validate the proposed approach. The obtained results are encouraging, being a cornerstone for new perspectives to be considered.

scholarly journals Test Case Prioritization & Selection for an Object Oriented Software using Genetic Algorithm.

2020 ◽  
Vol 9 (4) ◽  
pp. 349-354
Keyword(s):  
Genetic Algorithm ◽  
Object Oriented ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Test Case Selection ◽  
Case Selection ◽  
Selection For ◽  
The Given

In this paper our aim is to propose a Test Case Selection and Prioritization technique for OOP for ordering the test cases as per in accordance with their priority for finding the faults in the OOS. We have used the heuristic Genetic Algorithm, in order to generating the order of these prioritized test cases for a given OOS. The motive is to put a test case first into the ordered sequence that may have the highest prospective of finding an error in the given OOS & then soon..

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