Genetik algoritma kullanarak java dilinde dinamik birim testi için test verisi üretimi

Software testing is an essential and fundamental part of the software development lifecycle. Testing helps delivering a higher quality system to end users. However, it is costly as it needs to be written and updated as the process continues to ensure that it does its job in detecting faults and bugs in the system. One of the problems in maintaining test cases is the input data used to run the tests such a way that it covers each path and line of code of the software product. Generation of these test data is a typical activity which has to be accomplished through any standard automated test data generation tool. Random test data generators are among the most widely used tools to generate input data for the tests. However, the data types and parameters has to be manually tailored into the tools and needs to be updated manually once the source code or the test cases are changed. It is a costly process and takes a lot of time and effort to generate and update these data. There are various test data generator tools are available such as: random test data generator, symbolic evaluator, function minimization methods. In recent years some more advanced heuristic search techniques have been applied to software testing. In this study, we propose a model which automates the test data generation process. It significantly reduces the time required to generate the input data. At the same time, the data generated by our model outperforms the data generated randomly in terms of accuracy and sensibility of the input data. It is based on the most widely used heuristic algorithms which is genetic algorithm. We run the model on a sample class with 6 independent public methods of different method signature, return type and number of arguments. It takes 5 seconds to generate10 possible inputs for each method with a mean standard deviation of 0.15 and best candidate fitness average of 8.82 and mean fitness of 9.79. The results will be further discussed in results section of the study. Keywords: Software Testing, Unit Testing, Test Data, Evolutionary Algorithm, Genetic Algorithm