Journal of Engineering Education Transformations

Journal of Engineering Education Transformations

DOI: 10.16920/jeet/2021/v34i4/153757

Year: 2021, Volume: 34, Issue: 4, Pages: 74-89

Original Article

Improved Undergraduate Software Capstone Project Development with Adoption of Industry Practices

Ravindra Mandale *, Ashwini Patil , Amol Adamuthe
  • Computer Science & Information Technology Department, RIT, Rajaramnagar, India
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

To improve cost-effectiveness, the software development processes in the IT industries are changing rapidly. There is a gap in different processes in IT industries and engineering institutes due to a mismatch in industry expectations and academic practices. According to different survey reports, this gap is increasing significantly due to lack of domain knowledge, inabilities of adopting recent technologies, old curriculum contents, poor assessment methodologies, and old project development practices, etc. To make students industry-ready, it is necessary to inculcate recent software development processes in academics. Academic project is one of the important courses in an undergraduate program which inculcates industry required skills among the students to bridge the gap to a greater extent. In this work, the gap in academic software project development practices is identifies through feedback from industry experts, alumni, and previous three years' student projects. This paper presents Industry Oriented Software Engineering Practices (IOSEP) methodology to adopt the recent industry practices in academics for improving students' project quality. The proposed methodology is implemented for the third-year mini project and a final year capstone project for academic year 2018-19. The IOSEP methodology incorporates an agile model, industry coding practices, GitHub platform, modern tools and technologies, testing tools, real-time deployment and, LaTex for documentation. To analyse the effectiveness of the proposed methodology, a new skill-based assessment method and rubrics are designed. K-means clustering is used to analyse students' performance. The Elbow method and silhouette analysis are used to select the number of clusters. Results show that optimal cluster values are three and more than 38% of students are in excellent cluster. The post feedback analysis from faculties and students show that project quality improved compared to previous years on development time, coding practices, technology use, and technical report writing. Industry-sponsored projects, participation in project competitions, student paper publications, and placement statistics are improved than the previous three years.

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  • 01 June 2021
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Year: 2021, Volume: 34, Issue: 4


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