scholarly journals A Hybrid Intelligent Search Algorithm for Automatic Test Data Generation

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Ying Xing ◽  
Yun-Zhan Gong ◽  
Ya-Wen Wang ◽  
Xu-Zhou Zhang
Keyword(s):  
Local Search ◽  
Software Testing ◽  
Test Data ◽  
Search Algorithm ◽  
Solution Space ◽  
Test Data Generation ◽  
Search Efficiency ◽  
Data Generation ◽  
Search Methods ◽  
Monotonicity Analysis

The increasing complexity of large-scale real-world programs necessitates the automation of software testing. As a basic problem in software testing, the automation of path-wise test data generation is especially important, which is in essence a constraint optimization problem solved by search strategies. Therefore, the constraint processing efficiency of the selected search algorithm is a key factor. Aiming at the increase of search efficiency, a hybrid intelligent algorithm is proposed to efficiently search the solution space of potential test data by making full use of both global and local search methods. Branch and bound is adopted for global search, which gives definite results with relatively less cost. In the search procedure for each variable, hill climbing is adopted for local search, which is enhanced with the initial values selected heuristically based on the monotonicity analysis of branching conditions. They are highly integrated by an efficient ordering method and the backtracking operation. In order to facilitate the search methods, the solution space is represented as state space. Experimental results show that the proposed method outperformed some other methods used in test data generation. The heuristic initial value selection strategy improves the search efficiency greatly and makes the search basically backtrack-free. The results also demonstrate that the proposed method is applicable in engineering.

AUTOMATIC PRODUCTION OF TEST DATA WITH A MULTIPLE BATCH-OPTIMISTIC METHOD

2009 ◽  
Vol 18 (01) ◽  
pp. 61-80 ◽  
Author(s):  
ANASTASIS A. SOFOKLEOUS ◽  
ANDREAS S. ANDREOU
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Test Data ◽  
Automatic Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Generation System ◽  
Automatic Production ◽  
Coverage Criterion ◽  
Testing Coverage

Recent research on software testing focuses on integrating techniques, such as computational intelligence, with special purpose software tools so as to minimize human effort, reduce costs and automate the testing process. This work proposes a complete software testing framework that utilizes a series of specially designed genetic algorithms to generate automatically test data with reference to the edge/condition testing coverage criterion. The framework utilizes a program analyzer, which examines the program's source code and builds dynamically program models for automatic testing, and a test data generation system that utilizes genetic algorithms to search the input space and determine a near to optimum set of test cases with respect to the testing coverage criterion. The performance of the framework is evaluated on a pool of programs consisting of both standard and random-generated programs. Finally, the proposed test data generation system is compared against other similar approaches and the results are discussed.

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.

Towards Migrating Genetic Algorithms for Test Data Generation to the Cloud

2012 ◽  
pp. 113-135 ◽  
Author(s):  
Sergio Di Martino ◽  
Filomena Ferrucci ◽  
Valerio Maggio ◽  
Federica Sarro
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Test Data ◽  
Optimization Problems ◽  
Research Area ◽  
Test Data Generation ◽  
Data Generation ◽  
Cloud Infrastructure ◽  
Computational Resources ◽  
Mapreduce Paradigm

Search-Based Software Testing is a well-established research area, whose goal is to apply meta-heuristic approaches, like Genetic Algorithms, to address optimization problems in the testing domain. Even if many interesting results have been achieved in this field, the heavy computational resources required by these approaches are limiting their practical application in the industrial domain. In this chapter, the authors propose the migration of Search-Based Software Testing techniques to the Cloud aiming to improve their performance and scalability. Moreover, they show how the use of the MapReduce paradigm can support the parallelization of Genetic Algorithms for test data generation and their migration in the Cloud, thus relieving software company from the management and maintenance of the overall IT infrastructure and developers from handling the communication and synchronization of parallel tasks. Some preliminary results are reported, gathered by a proof-of-concept developed on the Google’s Cloud Infrastructure.

scholarly journals Using Artificial Bee Colony Algorithm for Test Data Generation and Path Testing Coverage

2018 ◽  
Vol 12 (7) ◽  
pp. 99
Author(s):  
Faten Hamad
Keyword(s):  
Software Testing ◽  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Development Lifecycle ◽  
Path Testing

Software testing is a significant stage in software development lifecycle. There are different sorts of' structural software testing methodologies that may be generally utilized and moved forward through enhancing the traverse of all of the conceivable code software paths. The interest for automating data testing is growing; however, manual testing strategies utilization would be expensive and costly. Heuristic measure is being applied to; detect how better the result might be (solution fitness); result development mechanism; and suitableness criteria with stop search mechanism depending on wither a result is found or not. Testing experience could be exploited for finding a solution to the optimization problem by utilizing Meta heuristic procedures. The presented approach might have been tested for five programs to demonstrate the distinctive tests issues. This paper proposes an automatic test data generation approach that use artificial bee colony algorithm for software structural testing, particularly, path testing. This is brought on moving the centralization of data generation testing, as opposed to the automation of the whole testing operation. It executes artificial bee colony algorithm by creating testing data for the criteria of path coverage testing, and then applying the strategy to a group of test programs. 

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