An empirical study on clustering approach combining fault prediction for test case prioritization

2017 ◽  
Author(s):  
Lei Xiao ◽  
Huaikou Miao ◽  
Weiwei Zhuang ◽  
Shaojun Chen
Keyword(s):  
Empirical Study ◽  
Fault Prediction ◽  
Test Case ◽  
Test Case Prioritization ◽  
Clustering Approach

A TEST CASE PRIORITIZATION BASED ON DEGREE OF RISK EXPOSURE AND ITS EMPIRICAL STUDY

2011 ◽  
Vol 21 (02) ◽  
pp. 191-209 ◽  
Author(s):  
HOIJIN YOON ◽  
BYOUNGJU CHOI
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Empirical Study ◽  
Risk Exposure ◽  
Test Case ◽  
Test Case Prioritization ◽  
Management Process ◽  
Testing Method ◽  
Risk Management Process ◽  
Prioritization Strategy

We propose a test case prioritization strategy for risk based testing, in which the risk exposure is employed as the key criterion of evaluation. Existing approaches to risk based testing typically employ risk exposure values as assessed by the tester. In contrast, we employ exposure values that have been determined by experts during the risk assessment stage of the risk management process. If a given method produces greater accuracy in fault detection, that approach is considered more valuable for software testing. We demonstrate the value of our proposed risk based testing method in this sense through its application.

Building Ant System for Multi-Faceted Test Case Prioritization

2022 ◽  
pp. 671-686
Author(s):  
Manoj Kumar Pachariya
Keyword(s):  
Experimental Study ◽  
Empirical Study ◽  
Execution Time ◽  
Optimal Solution ◽  
Time Constraints ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Ant System ◽  
Software Infrastructure

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.

Building Ant System for Multi-Faceted Test Case Prioritization

2020 ◽  
Vol 8 (2) ◽  
pp. 23-37
Author(s):  
Manoj Kumar Pachariya
Keyword(s):  
Experimental Study ◽  
Empirical Study ◽  
Execution Time ◽  
Optimal Solution ◽  
Time Constraints ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Ant System ◽  
Software Infrastructure

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document