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Today, several methods have been presented to automate test data generation; because of the low maturity level of automatic methods, this is still widely carried out by humans in industry. Hence, the challenge is nding approaches in which humans could generate test data through more attractive, faster, and cheaper ways. To achieve this, one approach is using a Game With A Purpose (GWAP) in test data generation. In our previous work, we introduced two games called Rings and Greenify, by which many inexpensive players with no special technical abilities become engaged in test data generation. This paper presents an entirely new GWAP for test data generation called Quest Of Treasure Explorer (QOTE). QOTE provides a di erent gameplay and has certain advantages compared to prior games, including faster generation of test data, easier puzzles, narration, etc. Experimental results have shown that QOTE outperforms prior games in two aspects: game quality and capability of test data generation. We have also conducted an experiment based on mutation analysis to further evaluate the test data generation capabilities of QOTE compared to four automatic approaches. According to this experiment, QOTE outperforms the four competitors regarding average mutation scores.

Now-a-days software has a great impact on different aspects of human life. Software systems are responsible for safety of major critical tasks. To prevent catastrophic malfunctions, promising quality testing techniques should be used during software development. Software testing is an effective technique to catch defects, but it significantly increases the development cost. Therefore, automated testing is a major issue in software engineering. Search-Based Software Testing (SBST), specifically genetic algorithm, is the most popular technique in automated testing for achieving appropriate degree of software quality. In this paper TLBO, a swarm intelligence technique, is proposed for automatic test data generation as well as for evaluation of test results. The algorithm is implemented in EvoSuite, which is a reference tool for search-based software testing. Empirical studies have been carried out on the SF110 dataset which contains 110 java projects from the online code repository SourceForge and the results show that the TLBO provides competitive results in comparison with major genetic based methods.

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One of the most important, but tedious and costly tasks of software testing process is test data generation. Several methods for automating this task have been presented, yet due to their practical drawbacks, test data generation is still widely performed by humans in industry.In our previous work, we employed the notion of Game With A Purpose (GWAP) and introduced Rings as a GWAP to reduce time and costs of human-based test data generation and increase its appeal to engage even nontechnical people. In this paper, we propose a new game, called Greenify, with the purpose of test data generation so that it solves the main issues of Rings. The environment of this game is built based on a program's control flow graph. To evaluate the proposed approach, we designed several game levels based on six different C++ programs and gave them to volunteering players. The results show that in comparison to both conventional human-based approach and Rings, Greenify generates test data with less rime for all feasible paths of the given benchmark programs. In addition, Greenify identifies the smaller set of likely infeasible paths.