Mutation Analysis and Constraint-Based Criteria: Results from an Empirical Evaluation in the Context of Software Testing

2004 ◽  
Vol 20 (4) ◽  
pp. 439-445 ◽  
Author(s):  
Inali Wisniewski Soares ◽  
Silvia Regina Vergilio
Keyword(s):  
Software Testing ◽  
Mutation Analysis ◽  
Empirical Evaluation

Test Suite Optimization Using Firefly and Genetic Algorithm

2022 ◽  
pp. 1635-1651
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Software Engineering ◽  
Software Testing ◽  
Hybrid Approach ◽  
Empirical Evaluation ◽  
Data Generation ◽  
Testing Environment ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

scholarly journals Mutation Analysis of Specification-Based Contracts in Software Testing

2021 ◽  
Author(s):  
Abbas Khalilov ◽  
Tugkan Tuglular ◽  
Fevzi Belli
Keyword(s):  
Software Testing ◽  
Mutation Analysis

scholarly journals Mutation Based Testing of P Systems

2009 ◽  
Vol 4 (3) ◽  
pp. 253 ◽  
Author(s):  
Florentin Ipate ◽  
Marian Gheorghe
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Mutation Analysis ◽  
Mutation Testing ◽  
P Systems ◽  
P System ◽  
System Specification ◽  
System Testing ◽  
Testing Method ◽  
Context Free

Although testing is an essential part of software development, until re- cently, P system testing has been completely neglected. Mutation testing (mutation analysis) is a structural software testing method which involves modifying the pro- gram in small ways. In this paper, we provide a formal way of generating mutants for systems specified by context-free grammars. Furthermore, the paper shows how the proposed method can be used to construct mutants for a P system specification.

Test Suite Optimization Using Firefly and Genetic Algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Software Engineering ◽  
Software Testing ◽  
Hybrid Approach ◽  
Empirical Evaluation ◽  
Data Generation ◽  
Testing Environment ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

scholarly journals Parallel Mutant Execution Techniques in Mutation Testing Process for Simulink Models

2017 ◽  
Vol 4 ◽  
pp. 90-100
Author(s):  
Le Thi My Hanh ◽  
Nguyen Thanh Binh ◽  
Khuat Thanh Tung
Keyword(s):  
Parallel Computing ◽  
Parallel Processing ◽  
Distributed Computing ◽  
Software Testing ◽  
Mutation Analysis ◽  
Execution Time ◽  
Mutation Testing ◽  
Parallel Execution ◽  
Computationally Expensive ◽  
Computationally Intensive

Mutation testing – a fault-based technique for software testing – is a computationally expensive approach. One of the powerful methods to improve the performance of mutation without reducing effectiveness is to employ parallel processing, where mutants and tests are executed in parallel. This approach reduces the total time needed to accomplish the mutation analysis. This paper proposes three strategies for parallel execution of mutants on multicore machines using the Parallel Computing Toolbox (PCT) with the Matlab Distributed Computing Server. It aims to demonstrate that the computationally intensive software testing schemes, such as mutation, can be facilitated by using parallel processing. The experiments were carried out on eight different Simulink models. The results represented the efficiency of the proposed approaches in terms of execution time during the testing process.

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