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

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

A method of multiobjective optimization using a genetic algorithm and an artificial immune system

2009 ◽  
Vol 223 (5) ◽  
pp. 1243-1252 ◽  
Author(s):  
H Park ◽  
N-S Kwak ◽  
J Lee
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Immune System ◽  
Artificial Immune System ◽  
Affinity Maturation ◽  
Pareto Optimal ◽  
Mathematical Function ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Artificial Immune

The immune system has pattern recognition capabilities based on reinforced learning, memory, and affinity maturation interacting between antigens (Ags) and antibodies (Abs). This article deals with an adaptation of artificial immune system (AIS) into genetic-algorithm (GA)-based multi-objective optimization. The present study utilizes the pattern recognition from an AIS and the evolution from a GA. Using affinity measures between Ags and Abs, GA-based immune simulation discovers a generalist Ab that represents the common pattern among Ags. Non-dominated Pareto-optimal solutions are obtained via GA-based immune simulation in which dominated designs are considered as Ags, whereas non-dominated designs are assigned to Abs. This article discusses the procedure of identifying Pareto-optimal solutions through the immune system-based pattern recognition. A number of mathematical function problems that are described by discontinuity or disconnection in the shape of Pareto surface are first examined as test examples. Subsequently, engineering optimization problems such as rotating flywheel disc and ten-bar planar truss are explored to support the present study.

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.

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