Factors Influencing Performance of Students in Software Automated Test Tools Course

Authors

Susmita Haldar, Western UniversityFollow
Mary Pierce, Fanshawe College of Applied Arts and TechnologyFollow
Luiz Fernando Capretz, Western UniversityFollow

Document Type

Article

Publication Date

5-2024

Journal

17th IEEE International Conference of Software Testing, Verification and Validation Workshops (ICSTW-2024)

First Page

331

URL with Digital Object Identifier

10.1109/ICSTW60967.2024.00064

Last Page

338

Abstract

Formal software testing education is important for building efficient QA professionals. Various aspects of quality assurance approaches are usually covered in courses for training software testing students. Automated Test Tools is one of the core courses in the software testing post-graduate curriculum due to the high demand for automated testers in the workforce. It is important to understand which factors are affecting student performance in the automated testing course to be able to assist the students early on based on their needs. Various metrics that are considered for predicting student performance in this testing course are student engagement, grades on individual deliverables, and prerequisite courses. This study identifies the impact of assessing students based on individual vs. group activities, theoretical vs. practical components, and the effect of having taken prerequisite courses in their final grade. To carry out this research, student data was collected from the automated test tools course of a community college-based postgraduate certificate program in software testing. The dataset contained student records from the years 2021 to 2022 and consisted of information from five different semesters. Various machine learning algorithms were applied to develop an effective model for predicting students’ performance in the automated software testing tools course, and finally, important features affecting the students’ performance were identified. The predictive performance model of the automated test tools course that was developed by applying the logistic regression technique, showed the best performance, with an accuracy score of 90%.

Notes

10.1109/ICSTW60967.2024.00064

Citation of this paper:

Haldar S., Pierce M., Capretz L.F., Factors Influencing Performance of Students in Software Automated Test Tools Course, 17^th IEEE International Conference of Software Testing, Verification and Validation Workshops (ICSTW-2024) , Toronto, Canada, pp. 331-338, IEEE Express, DOI: https://doi.org/10.1109/ICSTW60967.2024.00064, May 2024.