A New Data Mining-Based Framework to Test Case Prioritization Using Software Defect Prediction

2017 ◽  
Vol 8 (1) ◽  
pp. 21-41
Author(s):  
Emad Alsukhni ◽  
Ahmad A. Saifan ◽  
Hanadi Alawneh
Keyword(s):  
Data Mining ◽  
Software Metrics ◽  
Fault Prediction ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Software Defect Prediction ◽  
Average Percentage ◽  
Software Defect ◽  
New Framework

Test cases do not have the same importance when used to detect faults in software; therefore, it is more efficient to test the system with the test cases that have the ability to detect the faults. This research proposes a new framework that combines data mining techniques to prioritize the test cases. It enhances fault prediction and detection using two different techniques: 1) the data mining regression classifier that depends on software metrics to predict defective modules, and 2) the k-means clustering technique that is used to select and prioritize test cases to identify the fault early. Our approach of test case prioritization yields good results in comparison with other studies. The authors used the Average Percentage of Faults Detection (APFD) metric to evaluate the proposed framework, which results in 19.9% for all system modules and 25.7% for defective ones. Our results give us an indication that it is effective to start the testing process with the most defective modules instead of testing all modules arbitrary arbitrarily.

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

scholarly journals A New Framework Consisted of Data Preprocessing and Classifier Modelling for Software Defect Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Haijin Ji ◽  
Song Huang
Keyword(s):  
Software Metrics ◽  
Data Preprocessing ◽  
Defect Prediction ◽  
Data Repository ◽  
Software Defect Prediction ◽  
Software Projects ◽  
Software Defect ◽  
Nonnormal Distribution ◽  
Non Gaussian ◽  
New Framework

Different data preprocessing methods and classifiers have been established and evaluated earlier for the software defect prediction (SDP) across projects. These novel approaches have provided relatively acceptable prediction results for different software projects. However, to the best of our knowledge, few researchers have combined data preprocessing and building robust classifier simultaneously to improve prediction performances in SDP. Therefore, this paper presents a new whole framework for predicting fault-prone software modules. The proposed framework consists of instance filtering, feature selection, instance reduction, and establishing a new classifier. Additionally, we find that the 21 main software metrics commonly do follow nonnormal distribution after performing a Kolmogorov-Smirnov test. Therefore, the newly proposed classifier is built on the maximum correntropy criterion (MCC). The MCC is well-known for its effectiveness in handling non-Gaussian noise. To evaluate the new framework, the experimental study is designed with due care using nine open-source software projects with their 32 releases, obtained from the PROMISE data repository. The prediction accuracy is evaluated using F-measure. The state-of-the-art methods for Cross-Project Defect Prediction are also included for comparison. All of the evidences derived from the experimentation verify the effectiveness and robustness of our new framework.

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.

Multi- Objective Crow Search and Fruit Fly Optimization for Combinatorial Test Case Prioritization

2021 ◽  
Vol 9 (4) ◽  
pp. 0-0
Keyword(s):  
Performance Metrics ◽  
Search Algorithm ◽  
Fitness Function ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Optimal Test ◽  
Fruit Fly Optimization ◽  
Average Percentage ◽  
Multi Objective

This paper proposes a novel test case prioritization technique, namely Multi- Objective Crow Search and Fruitfly Optimization (MOCSFO) for test case prioritization. The proposed MOCSFO is designed by integrating Crow search algorithm (CSA) and Chaotic Fruitfly optimization algorithm (CFOA). The optimal test cases are selected based on newly modelled fitness function, which consist of two parameters, namely average percentage of combinatorial coverage (APCC) and Normalized average of the percentage of faults detected (NAPFD). The test case to be selected is decided using a searching criterion or fitness based on sequential weighed coverage size. Accordingly, the effective searching criterion is formulated to determine the optimal test cases based on the constraints. The experimentation of the proposed MOCSFO method is performed by considering the performance metrics, like NAPFD, and APCC. The proposed MOCSFO outperformed the existing methods with enhanced NAPFD of 0.7, and APCC of 0.837.

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 STUDY OF TEST CASE PRIORITIZATION TECHNIQUE USING APFD

2013 ◽  
Vol 10 (3) ◽  
pp. 1475-1481
Author(s):  
Rahul Gupta ◽  
Akhilesh Kumar Yadav
Keyword(s):  
Fault Detection ◽  
Software Development ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Average Percentage

Regression testing is used to ensure that bugs are fixed and new functionality introduce in a new version of a software that don't adversely affect the original functionality inherited from the previous version.Regression testing is one of the most complaining activities of software development and maintenance.Unluckily, It may have feeble resources to allow for the re-execution of all test cases during regression testing. In this situation the use of test case prioritization is profitable because the best appropriate test cases are executed first. In this paper we are proposing an algorithm to prioritize test cases based on rate of fault detection and impact of fault.The proposed algorithm recognises the exhausting fault at earlier stage of the testing process.We are using an Average Percentage of Faults Detected (APFD) metric to determine the effectiveness of the new test case arrangements.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document