Software testing using genetic algorithms
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The paper discusses methods for automatic test-data generation in software testing using genetic algorithms, highlighting their efficiency in solving complex problems with little information. It provides a detailed overview of the algorithm's operations, including selection, crossover, and mutation, along with results supporting their effectiveness in controlled environments. While the paper is well-structured and clear, it lacks a review of related works and a comparative analysis with other algorithms.
Software testing using genetic algorithms
- 1. SOFTWARE TESTING USING GENETIC ALGORITHMS Authored By: Akshat Sharma, Rishon Patani and Ashish Aggarwal Reviewed by: Nurhussen Menza
- 2. Publication details School of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.2, April 2016 SOFTWARE TESTING USING GENETIC ALGORITHMS 2
- 3. Introduction This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in software testing. In addition to this introduction and a conclusion at the end It is organized in to four major sections The problem Methods used Results claimed Critics SOFTWARE TESTING USING GENETIC ALGORITHMS 3
- 4. The research problem The paper presents Software testing is a process in which the runtime quality and quantity of a software is tested to maximum limits. The basic test of software is done in the environment for which it is has been designed. The authors claim that Genetic algorithms are one of the best ways to solve a set of problems for which little information is given. And it is very general algorithm and so they will work well in any search space. SOFTWARE TESTING USING GENETIC ALGORITHMS 4
- 5. Cont… According to the authors the Evolutionary Testing uses a kind of meta-heuristic search technique, the Genetic Algorithm (GA), to convert the task of test case generation into an optimal problem. It’s run through is checked for correct and efficient outputs. The authors stated that different types of genetic algorithms is done on this paper. SOFTWARE TESTING USING GENETIC ALGORITHMS 5
- 6. Cont… Different algorithms have been run on different tools and analyzed for their performance. All these algorithms follow the same basis of evolutionary testing but have different cost functions. On running these cost functions on different tools, observations on how these functions respond are made. SOFTWARE TESTING USING GENETIC ALGORITHMS 6
- 7. Approaches and Methods employed According to the authors Genetic algorithms use the following three operations on its population. Selection Crossover Mutation Let us see them one by one; SOFTWARE TESTING USING GENETIC ALGORITHMS 7
- 8. Cont… Selection:- A selection process is applied to determine a way in which individuals are chosen for mating from a population based on their fitness. Fitness is defined as a characteristic and capability of an individual to survive and reproduce in an environment. SOFTWARE TESTING USING GENETIC ALGORITHMS 8
- 9. Conts… Crossover:- Crossover involves swapping of sequence of bits or genes in the string between two individuals. This process of swapping is carried out and repeated each time with different parent individuals until the next generation has optimum individuals. SOFTWARE TESTING USING GENETIC ALGORITHMS 9
- 10. Conts… Mutation: After the crossover process, the mutate operation is applied to a randomly select subset of the population. Mutation leads to an alteration of chromosomes in small new ways to introduce good traits. The main aim of mutation is to bring diversity in population. SOFTWARE TESTING USING GENETIC ALGORITHMS 10
- 11. Results and discussions According to the authors Genetic algorithms are most efficient and effective in a search space for which little is known. Then again, genetic algorithms can be used to produce solutions to problems working only in the test environment and deviates once you try to use them in the real world. So when put simply, genetic algorithm can be used to create solutions for problems that are not very easy to calculate and analyze. SOFTWARE TESTING USING GENETIC ALGORITHMS 11
- 12. Cont… And the authors listed some implementation of Genetic Algorithm(GA) in software testing like:- Test case generation using GA in Ruby Trust-based system Genetic Algorithm Implementation in C++ Genetic Algorithm Implementation using Matlab SOFTWARE TESTING USING GENETIC ALGORITHMS 12
- 13. Critics Positive aspects The paper is clear, easy to read and understand The logicality of the findings given the problem statement is acceptable The finding and well evaluated and explained deeply The implementation section have details regarding on implementation of Genetic algorithm in different software testing mechanism like MATLAB, Ruby & C++. The paper was figurative and explanatory with examples. SOFTWARE TESTING USING GENETIC ALGORITHMS 13
- 14. Cont… Negative aspects Review of related works is also not mentioned in the paper. The general approach was used instead of comparative approach with other algorithms. SOFTWARE TESTING USING GENETIC ALGORITHMS 14
- 15. Conclusion The work is motivational With all its limitations it can be said that the authors really achieve their objectives Good for further research on this topic as the direction given by the authors SOFTWARE TESTING USING GENETIC ALGORITHMS 15
- 16. Thank You