Generating Test Data for Path Coverage Based on Genetic Algorithm

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

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Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm

PLoS ONE ◽

10.1371/journal.pone.0242812 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242812

Author(s):

Shayma Mustafa Mohi-Aldeen ◽

Radziah Mohamad ◽

Safaai Deris

Keyword(s):

Genetic Algorithm ◽

Test Data ◽

Hybrid Method ◽

Negative Selection ◽

Test Data Generation ◽

Data Generation ◽

Selection Algorithm ◽

Data Types ◽

Negative Selection Algorithm ◽

Path Coverage

Path testing is the basic approach of white box testing and the main approach to solve it by discovering the particular input data of the searching space to encompass the paths in the software under test. Due to the increasing software complexity, exhaustive testing is impossible and computationally not feasible. The ultimate challenge is to generate suitable test data that maximize the coverage; many approaches have been developed by researchers to accomplish path coverage. The paper suggested a hybrid method (NSA-GA) based on Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) to generate an optimal test data avoiding replication to cover all possible paths. The proposed method modifies the generation of detectors in the generation phase of NSA using GA, as well as, develops a fitness function based on the paths’ prioritization. Different benchmark programs with different data types have been used. The results show that the hybrid method improved the coverage percentage of the programs’ paths, even for complicated paths and its ability to minimize the generated number of test data and enhance the efficiency even with the increased input range of different data types used. This method improves the effectiveness and efficiency of test data generation and maximizes search space area, increasing percentage of path coverage while preventing redundant data.

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Genetic Algorithm Based Test Data Generation for Structured Software Testing

Journal of the Association of Engineers India ◽

10.22485/jaei/2015/v85/i3-4/119867 ◽

2015 ◽

Vol 85 (3-4) ◽

pp. 52

Author(s):

Subhasis Mallick ◽

Dipankar Majumdar

Keyword(s):

Genetic Algorithm ◽

Software Testing ◽

Test Data ◽

Test Data Generation ◽

Data Generation

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A Multi-Objective Genetic Algorithm to Test Data Generation

2010 22nd IEEE International Conference on Tools with Artificial Intelligence ◽

10.1109/ictai.2010.26 ◽

2010 ◽

Cited By ~ 18

Author(s):

Gustavo H.L. Pinto ◽

Silvia R. Vergilio

Keyword(s):

Genetic Algorithm ◽

Test Data ◽

Test Data Generation ◽

Data Generation ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

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Program Test Data Generation for Branch Coverage with Genetic Algorithm: Comparative Evaluation of A Maximization and Minimization Approach

Computer Science Conference Proceedings ◽

10.5121/csit.2012.2140 ◽

2012 ◽

Cited By ~ 1

Author(s):

Ankur Pachauri Gursaran

Keyword(s):

Genetic Algorithm ◽

Test Data ◽

Comparative Evaluation ◽

Test Data Generation ◽

Data Generation ◽

Branch Coverage ◽

Program Test ◽

Minimization Approach

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A Path-Oriented Test Data Generation Approach Hybridizing Genetic Algorithm and Artificial Immune System

Advances in Intelligent Systems and Computing - Computational Intelligence in Data Mining ◽

10.1007/978-981-10-8055-5_58 ◽

2018 ◽

pp. 649-658

Author(s):

Gargi Bhattacharjee ◽

Ashish Singh Saluja

Keyword(s):

Genetic Algorithm ◽

Immune System ◽

Test Data ◽

Artificial Immune System ◽

Test Data Generation ◽

Data Generation ◽

Artificial Immune

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Parameter Identification of Tractor-Semitrailer Model under Steering and Braking

Mathematical Problems in Engineering ◽

10.1155/2019/1345364 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Yiming Li ◽

Qin Shi ◽

Duoyang Qiu

Keyword(s):

Genetic Algorithm ◽

Field Test ◽

Test Data ◽

Performance Testing ◽

Roll Angle ◽

Lookup Table ◽

The Real ◽

Yaw Rate ◽

Roll Rate ◽

Good Agreement

This paper describes a valuable linear yaw-roll tractor-semitrailer (TST) model with five-degree-of-freedom (DOFs) for control algorithm development when steering and braking. The key parameters, roll stiffness, axle cornering stiffness, and fifth-wheel stiffness, are identified by the genetic algorithm (GA) and multistage genetic algorithm (MGA) based on TruckSim outputs to increase the accuracy of the model. Thus, the key parameters of the simplified model can be modified according to the real-time vehicle states by online lookup table and interpolation. The TruckSim vehicle model is built referring to the real tractor (JAC-HFC4251P1K7E33ZTF6×2) and semitrailer (Luyue LHX9406) used in the field test later. The validation of the linear yaw-roll model of a tractor-semitrailer using field test data is presented in this paper. The field test in the performance testing ground is detailed, and the test data of roll angle, roll rate, and yaw rate are compared with the outputs of the model with maps of the key parameters. The results indicate that the error of the tractor’s roll angle and semitrailer’s roll angle between model data and test data is 1.13% and 1.24%, respectively. The roll rate and yaw rate of the tractor and semitrailer are also in good agreement.

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