Closing the Gap: A Comprehensive Analysis of Software Engineering Curriculum and Industry Requirements

Abstract: This paper investigates the gap between software engineering education and the industry needs by suggesting solutions to close that gap. The implication is that classrooms agendas are to be more flexible in nature to meet the dynamism in technology needs by the tech sector, focusing on the inclusion of current data science technologies in education programs. This study is backed by data-driven inferences which help to identify the linkage between academia and industry, and with the use of the predictive model of the regression one can estimate the graduation success. The results have proved the importance of practical skills such as research abilities, critical thinking, and problem-solving skills over traditional metrics like GPA. Thus it should be the need of the hour to develop the industry-relevant training in order to provide vocational education to students. The coordination between academia and industry fields by merging student-centric projects that have modern technologies would aid improving adapting software engineering education to the variable industrial sector. The research results emphasize the significance of an active learning process and practical application of the learned concepts that should be employed to get students ready for the challenges awaiting them at the workplace. Finally, a paper that proposes permanent developing of the engineering curriculum and close collaboration between industry and academic institutions, so that the students receive key competences to be prosperous in software engineering. Python has been implemented to analyse the skills of the software engineering curriculum for achieving the requirements of the industry. Data visualisation, data pre-processing, and predictive models have been implemented to gather data based on industrial requirements. 

Curricula, Industrial Requirements, Industry Academia Collaboration, Software Engineering Education

1. Yu, W., Patros, P., Young, B., Klinac, E. and Walmsley, T.G., 2022. Energy digital twin technology for industrial energy management: Classification, challenges and future. Renewable and Sustainable Energy Reviews, 161, p.112407. [Google Scholar] [Crossref]

2. Fan, C., Chen, M., Wang, X., Wang, J. and Huang, B., 2021. A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in energy research, 9, p.652801. [Google Scholar] [Crossref]

3. Cico, O., Jaccheri, L., Nguyen-Duc, A. and Zhang, H., 2021. Exploring the intersection between software industry and Software Engineering education-A systematic mapping of Software Engineering Trends. Journal of Systems and Software, 172, p.110736. [Google Scholar] [Crossref]

4. Raji, I.D., Smart, A., White, R.N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D. and Barnes, P., 2020, January. Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 33-44). [Google Scholar] [Crossref]

5. Shneider man, B., 2020. Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), pp.1-31. [Google Scholar] [Crossref]

6. Neumann, W.P., Winkel haus, S., Grosse, E.H. and Glock, C.H., 2021. Industry 4.0 and the human factor–A systems framework and analysis methodology for successful development. International journal of production economics, 233, p.107992. [Google Scholar] [Crossref]

7. Zhao, X., Cornish, K. and Vodovotz, Y., 2020. Narrowing the gap for bioplastic use in food packaging: an update. Environmental science & technology, 54(8), pp.4712-4732. [Google Scholar] [Crossref]

8. Wang, B., Tao, F., Fang, X., Liu, C., Liu, Y. and Freiheit, T., 2021. Smart manufacturing and intelligent manufacturing: A comparative review. Engineering, 7(6), pp.738-757. [Google Scholar] [Crossref]

9. Ouhbi, S. and Pombo, N., 2020, April. Software engineering education: Challenges and perspectives. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 202-209). IEEE. [Google Scholar] [Crossref]

10. Bedué, P. and Fritzsche, A., 2022. Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management, 35(2), pp.530-549. [Google Scholar] [Crossref]

11. Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F.A. and Brendel, W., 2021. Partial success in closing the gap between human and machine vision. Advances in Neural Information Processing Systems, 34, pp.23885-23899. [Google Scholar] [Crossref]

12. Kasauli, R., Knauss, E., Hork off, J., Liebel, G. and de Oliveira Neto, F.G., 2021. Requirements engineering challenges and practices in large-scale agile system development. Journal of Systems and Software, 172, p.110851. [Google Scholar] [Crossref]

13. Lu, Y., Xu, X. and Wang, L., 2020. Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, pp.312-325. [Google Scholar] [Crossref]

14. Giray, G., 2021. A software engineering perspective on engineering machine learning systems: State of the art and challenges. Journal of Systems and Software, 180, p.111031. [Google Scholar] [Crossref]

15. Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N. and Alazab, M., 2020. Blockchain for industry 4.0: A comprehensive review. IEEE Access, 8, pp.79764-79800. [Google Scholar] [Crossref]

16. Zhang, A., Wang, J.X., Farooque, M., Wang, Y. and Choi, T.M., 2021. Multi-dimensional circular supply chain management: A comparative review of the state-of-the-art practices and research. Transportation Research Part E: Logistics and Transportation Review, 155, p.102509. [Google Scholar] [Crossref]

17. Hutchinson, B., Smart, A., Hanna, A., Denton, E., Greer, C., Kjartansson, O., Barnes, P. and Mitchell, M., 2021, March. Towards accountability for machine learning datasets: Practices from software engineering and infrastructure. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 560-575). [Google Scholar] [Crossref]

18. Mian, S.H., Salah, B., Ameen, W., Moiduddin, K. and Alkhalefah, H., 2020. Adapting universities for sustainability education in industry 4.0: Channel of challenges and opportunities. Sustainability, 12(15), p.6100. [Google Scholar] [Crossref]

19. Kumar, R.K., Antunes, M.J., Beaton, A., Mirabel, M., Nkomo, V.T., Okello, E., Regmi, P.R., Reményi, B., Sliwa-Hähnle, K., Zühlke, L.J. and Sable, C., 2020. Contemporary diagnosis and management of rheumatic heart disease: implications for closing the gap: a scientific statement from the American Heart Association. Circulation, 142(20), pp. e337-e357. [Google Scholar] [Crossref]

• Green Human Resources Management Practices and Tertiary Institution Productivity in South West Nigeria
• Evaluation of the Organoleptic Properties of Calabash Fruit (Crescentia Cujete) Jam: A Comprehensive Sensory analysis
• Equilibrium Solution of Two – Dimensional Non-Homogeneous Equations in the Theory of Elastic Mixtures
• Optimisation of Some Conditions for the Biodegradation of Low-Density Polyethylene Strips by Fungi Isolated from Parts of North Central Nigeria
• Increasing the Income of Coconut Farmers Through Community Empowerment in Selayar Regency, Indonesia