TY - GEN
T1 - A Machine Learning Model for Predicting Performance of Gamified Software Test Specialist
AU - Odevci, Bahadir Baran
AU - Ozdem, Mehmet
AU - Emsen, Ebru
AU - Bilen, Tugce
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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).
AB - 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).
KW - gamification
KW - machine learning
KW - performance
KW - software tester
UR - http://www.scopus.com/inward/record.url?scp=85139594104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139594104&partnerID=8YFLogxK
U2 - 10.1109/INISTA55318.2022.9894269
DO - 10.1109/INISTA55318.2022.9894269
M3 - Conference contribution
AN - SCOPUS:85139594104
T3 - 16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022
BT - 16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022
A2 - Chbeir, Richard
A2 - Yildirim, Tulay
A2 - Bellatreche, Ladjel
A2 - Manolopoulos, Yannis
A2 - Papadopoulos, Apostolos
A2 - Chaaya, Karam Bou
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022
Y2 - 8 August 2022 through 12 August 2022
ER -