Test Data Generation for Mutation Testing Using Genetic Algorithm

2018 ◽  
pp. 857-867 ◽  
Author(s):  
Deepti Bala Mishra ◽  
Rajashree Mishra ◽  
Arup Abhinna Acharya ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Mutation Testing ◽  
Test Data Generation ◽  
Data Generation

TEST DATA GENERATION FOR SOFTWARE TESTING BASED ON QUANTUM-INSPIRED GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1350004 ◽  
Author(s):  
CHENGYING MAO ◽  
XINXIN YU
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Test Data ◽  
Optimal Solution ◽  
Search Space ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Quantum Bit

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

scholarly journals RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation

2016 ◽  
Vol 76 ◽  
pp. 19-30 ◽  
Author(s):  
Shunkun Yang ◽  
Tianlong Man ◽  
Jiaqi Xu ◽  
Fuping Zeng ◽  
Ke Li
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Software Test ◽  
Test Data Generation ◽  
Data Generation
Sign in / Sign up

Export Citation Format

Share Document