Capstone Projects and their Transition into the Software Development Industry: A 10 Year Systematic Review of Literature

Abstract

This study sought to examine the existing software engineering and Information Technology practices covered in final-year capstone projects in higher institutions of learning around the world, and the possibility of commercializing products developed from these projects. The researchers undertook a Systematic Literature Review methodology. The researchers used Artificial Intelligence (A.I.) tools, including Litmaps and Connected Papers for literature identification, while Quillbot and Scholarly A.I. were used for summary writing. Manual full paper review, data extraction and synthesis, and quantitative analyses were done on the final dataset using Microsoft Word, Excel, and Rayyan.ai.  Eighty-eight papers from ACM Library, IEEE, Science Direct, EBSCOHost, Emerald Insight, Taylor & Francis, and Google Scholar were reviewed. Most papers were from Western countries, with only a few from Africa. Findings indicate a remarkable movement from traditional software engineering practices such as Waterfall to modern agile methods such as Scrum and Kaban. There was an increased use of Generative A.I. and Machine learning platforms such as GitHub and Arduino in projects profiled. Common scripting languages used by students were Java and Python. Most projects were undertaken in groups, with no prospective client. Most capstone courses were purely academic endeavours, and only a few were ever commercialized and transformed into industry products. One limitation of the review is that it includes grey literature from conferences and student theses. The study provides insights into State-of-the-Art approaches that can be adopted by higher institutions of learning to improve capstone courses.