scholarly journals DEO: A Dynamic Event Order Strategy for t-way Sequence Covering Array Test Data Generation

2020 ◽  
Vol 17 (2) ◽  
pp. 0575
Author(s):  
Mohammed Issam Younis
Keyword(s):  
Research Area ◽  
Test Case Generation ◽  
Test Case ◽  
Generation Process ◽  
Data Generation ◽  
Covering Array ◽  
Covering Arrays ◽  
Dynamic Event ◽  
Active Research ◽  
Event Order

Sequence covering array (SCA) generation is an active research area in recent years. Unlike the sequence-less covering arrays (CA), the order of sequence varies in the test case generation process. This paper reviews the state-of-the-art of the SCA strategies, earlier works reported that finding a minimal size of a test suite is considered as an NP-Hard problem. In addition, most of the existing strategies for SCA generation have a high order of complexity due to the generation of all combinatorial interactions by adopting one-test-at-a-time fashion. Reducing the complexity by adopting one-parameter- at-a-time for SCA generation is a challenging process. In addition, this reduction facilitates the supporting for a higher strength of coverage. Motivated by such challenge, this paper proposes a novel SCA strategy called Dynamic Event Order (DEO), in which the test case generation is done using one-parameter-at-a-time fashion. The details of the DEO are presented with a step-by-step example to demonstrate the behavior and show the correctness of the proposed strategy. In addition, this paper makes a comparison with existing computational strategies. The practical results demonstrate that the proposed DEO strategy outperforms the existing strategies in term of minimal test size in most cases. Moreover, the significance of the DEO increases as the number of sequences increases and/ or the strength of coverage increases. Furthermore, the proposed DEO strategy succeeds to generate SCAs up to t=7. Finally, the DEO strategy succeeds to find new upper bounds for SCA. In fact, the proposed strategy can act as a research vehicle for variants future implementation.

scholarly journals Bio Inspired Approach for Generating Test data from User Stories

2021 ◽  
Vol 12 (2) ◽  
pp. 412-419
Author(s):  
A.Tamizharasi , Et. al.
Keyword(s):  
Limited Resources ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Major Research ◽  
User Stories ◽  
Research Challenge ◽  
Agile Model ◽  
Business Requirements

In Agile model where the software prototypes are developed frequently and also rapidly, testing becomes more critical. Generating an effective Test case for complex system is a challenging task involved in software testing. The major research challenge in this area includes the test case generation with limited resources, identifying the essential functional requirement that plays a crucial role and automation of the test case generation process. To solve this issue, a hybridized bio inspired approach is proposed to generate test cases from the user stories which accepts the business requirements as input, processed using NLP and develop functional test cases from it. The proposed algorithm is compared with other existing algorithms and the experimental results proved that the proposed algorithm is more efficient in many cases.  

A Test Case Generation Process and Technique

2010 ◽  
Vol 4 (4) ◽  
pp. 265-287 ◽  
Author(s):  
Nicha Kosindrdec ◽  
Jirapun Daengdej
Keyword(s):  
Test Case Generation ◽  
Test Case ◽  
Generation Process

Software test data generation technology based on polymorphic particle swarm evolutionary algorithm

2021 ◽  
pp. 1-13
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Test Case Generation ◽  
Particle Swarm Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Learning Factor ◽  
Swarm Algorithm

Combinatorial testing is a statute-based software testing method that aims to select a small number of valid test cases from a large combinatorial space of software under test to generate a set of test cases with high coverage and strong error debunking ability. However, combinatorial test case generation is an NP-hard problem that requires solving the combinatorial problem in polynomial time, so a meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, the particle swarm algorithm is more competitive in terms of coverage table generation scale and execution time. In this paper, we systematically review and summarize the existing research results on generating combinatorial test case sets using particle swarm algorithm, and propose a combinatorial test case generation method that can handle arbitrary coverage strengths by combining the improved one-test-at-a-time strategy and the adaptive particle swarm algorithm for the variable strength combinatorial test problem and the parameter selection problem of the particle swarm algorithm. To address the parameter configuration problem of the particle swarm algorithm, the four parameters of inertia weight, learning factor, population size and iteration number are reasonably set, which makes the particle swarm algorithm more suitable for the generation of coverage tables. For the inertia weights.

EFSM-Based Test Case Generation: Sequence, Data, and Oracle

2015 ◽  
Vol 25 (04) ◽  
pp. 633-667 ◽  
Author(s):  
Rui Yang ◽  
Zhenyu Chen ◽  
Zhiyi Zhang ◽  
Baowen Xu
Keyword(s):  
Sequence Data ◽  
Research Work ◽  
Test Case Generation ◽  
Test Sequence ◽  
Test Case ◽  
Data Generation ◽  
Sequence Generation ◽  
Research Areas ◽  
Coverage Criterion ◽  
Finite State

Model-based testing has been intensively and extensively studied in the past decades. Extended Finite State Machine (EFSM) is a widely used model of software testing in both academy and industry. This paper provides a survey on EFSM-based test case generation techniques in the last two decades. All techniques in EFSM-based test case generation are mainly classified into three parts: test sequence generation, test data generation, and test oracle construction. The key challenges, such as coverage criterion and feasibility analysis in EFSM-based test case generation are discussed. Finally, we summarize the research work and present several possible research areas in the future.

scholarly journals A Proposal for Automatic Testing of GUIs Based on Annotated Use Cases

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Pedro Luis Mateo Navarro ◽  
Diego Sevilla Ruiz ◽  
Gregorio Martínez Pérez
Keyword(s):  
Automatic Testing ◽  
Use Cases ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Test Coverage ◽  
Initial Test ◽  
New Approach ◽  
Coverage Analysis

This paper presents a new approach to automatically generate GUI test cases and validation points from a set of annotated use cases. This technique helps to reduce the effort required in GUI modeling and test coverage analysis during the software testing process. The test case generation process described in this paper is initially guided by use cases describing the GUI behavior, recorded as a set of interactions with the GUI elements (e.g., widgets being clicked, data input, etc.). These use cases (modeled as a set of initial test cases) are annotated by the tester to indicate interesting variations in widget values (ranges, valid or invalid values) and validation rules with expected results. Once the use cases are annotated, this approach uses the new defined values and validation rules to automatically generate new test cases and validation points, easily expanding the test coverage. Also, the process allows narrowing the GUI model testing to precisely identify the set of GUI elements, interactions, and values the tester is interested in.

scholarly journals Model-Based Test Suite Generation Using Mutation Analysis for Fault Localization

2019 ◽  
Vol 9 (17) ◽  
pp. 3492 ◽  
Author(s):  
Choi ◽  
Lim
Keyword(s):  
Mutation Analysis ◽  
Fault Localization ◽  
Test Suite ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
State Chart ◽  
Generation Cost ◽  
Test Suite Generation

Fault localization techniques reduce the effort required when debugging software, as revealed by previous test cases. However, many test cases are required to reduce the number of candidate fault locations. To overcome this disadvantage, various methods were proposed to reduce fault-localization costs by prioritizing test cases. However, because a sufficient number of test cases is required for prioritization, the test-case generation cost remains high. This paper proposes a test-case generation method using a state chart to reduce the number of test suites required for fault localization, minimizing the test-case generation and execution times. The test-suite generation process features two phases: fault-detection test-case generation and fault localization in the test cases. Each phase uses mutation analysis to evaluate test cases; the results are employed to improve the test cases according to the objectives of each phase, using genetic algorithms. We provide useful guidelines for application of a search-based mutational method to a state chart; we show that the proposed method improves fault-localization performance in the test-suite generation phase.

scholarly journals Test Case Generation Process using Soft Computing Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 4824-4831
Keyword(s):  
Software Testing ◽  
Soft Computing ◽  
Test Data ◽  
Important Task ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Soft Computing Techniques ◽  
Case Development

Software testing is the SDLC's important and most expensive step. Software testing is difficult and time-consuming work requiring a great deal of money for software development. Testing is both an operation that is static and adaptive. Software testing process deals with the creation of test cases, checking and validating either passed or failed test cases. It is unidealistic to check only the discerning parts of the material as a whole at once. It is not possible to test the whole system once, so selected parts of the code are considered for analysis. Since the input space of the Product Under Test (PUT) can be very large, it is important to analyze a representative subset of test cases. During software testing, the most important task is to build appropriate test cases. An effective set of test cases can detect more errors. Software testing always requires high deficiencies. Test cases are constructed using the test data. In the automation of software testing, the important task is to generate test data according to a given level of competence. The improved test data are determined using the test case development methodology and the test data adequacy criterion being applied. For increase the level of automation and performance, these aspects of test case development need to be studied. This paper studies the various test case generation techniques using soft computing techniques like Genetic Algorithm, Artificial Bee colony methods. Further an evaluation criterion for the test case generation process, empirical study of Code Coverage and its importance is discussed.

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