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.

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.

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.

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.

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.

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.

scholarly journals Fault detection technique for test cases in software engineering

2018 ◽  
Vol 7 (1) ◽  
pp. 53
Author(s):  
Jagatjot Singh ◽  
Sumit Sharma
Keyword(s):  
Software Engineering ◽  
Fault Detection ◽  
Back Propagation ◽  
Test Case ◽  
Test Cases ◽  
Engineering Process ◽  
Different Types ◽  
Software Engineering Process ◽  
Propagation Technique ◽  
Types Of Faults

The processing of software and performing various operations on it is known as a software engineering process. The application of test cases for detecting the faults within the software is done through the testing process. There are various types of faults that occur within a software or test case which are to be identified and preventive approaches are to be applied to prevent them. In this paper, the Learn-to-rank algorithm is utilized which helps in detecting the faults from the software. The Back-Propagation technique is included with the LRA approach for enhancing its performance and improving the detection of fault rate. 10 test cases of different types are used for running various experiments and the MATLAB tool is utilized for performing various simulations. It is seen through the various simulation results that the fault detection rate is increased as well as the execution time is minimized with the help of this approach. 

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.

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.

Functional Test Case Generator for Contiki Operating System and Cooja Simulator

2019 ◽  
Vol 10 (1) ◽  
pp. 1251-1257
Author(s):  
Abhinandan H Patil
Keyword(s):  
Operating System ◽  
Functional Test ◽  
Test Suite ◽  
Target System ◽  
Test Case ◽  
Test Cases ◽  
Code Coverage ◽  
Regression Test ◽  
Run Time ◽  
Configuration Systems

Evolving multi-parameter, multi-configuration systems require regression test suite that can be customized. This is in terms of run time. Run time can be customized by generating the combinations using combinatorial techniques. For systems like Contiki operating system, the test cases need to be executed in its simulator Cooja. Executing test cases in a simulator requires functional test cases to be generated from the combinatorial parameter combinations obtained. In this work we present a methodology to generate the functional test cases. We present Functional Test Case Generator for Contiki and Cooja (FTCGCC), which is a tool developed using our methodology. We demonstrate use of our tool by generating customizable regression test suite for Contiki and Cooja using code coverage as criteria. FTCGCC is developed for the test case generation when target System Under Test is IoT operating system Contiki and its simulator Cooja.

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