A Machine Learning Model for Predicting Performance of Gamified Software Test Specialist

• Main Authors: Odevci, Bahadir Baran, Ozdem, Mehmet, Emsen, Ebru, Bilen, Tugce
• Format: Conference Proceeding
• Language: English
• Subjects: gamification; machine learning; software tester
• ISSN: 2768-7295
• DOI: 10.1109/INISTA55318.2022.9894269

Gamification is one of the new trend in software development and it has already gained a well-deserved popularity in finance, healthcare, education and even manufacturing. Software testing is a continuous cycle layered with several stages and spanning across multiple types of testing. Teams need to design test suites and implement test execution methodologies in each stage of development. For this reason, software testing teams comprise many individuals skilled in different aspects of software testing. The inclusion of gamification in this course can lead to positive benefits based on the idea that it is used to influence behavior. This paper presents an preliminary study of Machine Learning (ML) approach for predicting performance gamification of software tester specialists under a gamified testing environment. ImonaGame is a software company that delivers gamification as a service for software testing team of 30 members with different static and dynamic data. User behavior collected in dynamic data sets was classified into categories by deconstructing complex activities into behavior chains using supervision of domain experts. The classification approach was centered on the system's testing processes' performance objectives and potential for encouragement or dissuasion. Motivators and obstacles for the target activity and its behaviors will be found when the model has been developed. After conducting preliminary research, it is possible to determine whether gameful design is an effective and efficient tactic for achieving the desired result by analyzing needs, motives, and obstacles. The source data was classified target data in four categories such as I: Static feature (personal information; 4), II: Daily feature (gamification elements; 14), III: Mission feature (points; 7 sources) and IV: Cumulative futures (Sum of daily and mission features; 13).