A Novel Test Data Generation Approach Based Upon Mutation Testing by Using Artificial Immune System for Simulink Models

2015 ◽  
pp. 169-181 ◽  
Author(s):  
Le Thi My Hanh ◽  
Nguyen Thanh Binh ◽  
Khuat Thanh Tung
Keyword(s):  
Immune System ◽  
Test Data ◽  
Artificial Immune System ◽  
Mutation Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Artificial Immune

Assessing Android Test Data Generation Tools via Mutation Testing

2019 ◽  
Author(s):  
Henrique Neves da Silva ◽  
Paulo Roberto Farah ◽  
Willian Douglas Ferrari Mendonça ◽  
Silvia Regina Vergilio
Keyword(s):  
Test Data ◽  
Mutation Testing ◽  
Test Data Generation ◽  
Data Generation

A New Approach to Automated Test Data Generation in Mutation Testing

2009 ◽  
Vol 31 (6) ◽  
pp. 1025-1034 ◽  
Author(s):  
Jin-Hui SHAN ◽  
You-Feng GAO ◽  
Ming-Hao LIU ◽  
Jiang-Hong LIU ◽  
Lu ZHANG ◽  
...  
Keyword(s):  
Test Data ◽  
Mutation Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
New Approach ◽  
Automated Test ◽  
Automated Test Data Generation

scholarly journals Search-based Test Data Generation for Mutation Testing: a tool for Python programs

2020 ◽  
Author(s):  
Matheus Ferreira ◽  
Lincoln Costa ◽  
Francisco Carlos Souza
Keyword(s):  
Test Data ◽  
Mutation Testing ◽  
Hill Climbing ◽  
Data Sets ◽  
Test Data Generation ◽  
Data Generation ◽  
Mutation Score ◽  
Automated Test Data Generation ◽  
The Cost ◽  
The Hill

Test data generation for mutation testing consists of identifying a set of inputs that maximizes the number of mutants killed. Mutation Testing is an excellent test criterion for detecting faults and measuring the effectiveness of test data sets. However, it is not widely used in practice due to the cost and complexity to perform some activities as generating test data. Although test suites can be produced and selected manually by a tester this practice is susceptible to errors and tools are needed to facilitate it. Several tools have been developed to automate mutation testing, but, only a few address the test data generation. The present paper proposes an automated test data generation tool based on weak mutation for Python programming language using the Hill Climbing algorithm. For evaluation, we performed an experiment concerning the effectiveness and cost computational of the tool in a database composed of 348 mutants and we compare it with random generation. Overall, the experiment achieved an average mutation score of 86% for our proposed tool and random testing 64% on average.

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