Software Engineering



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Research areas-

Software engineering refers to the specific branch of engineering which is based on well-defined scientific principles and related to software product evaluation (Enoiu et al. 2020). Analysing the needs of users, software engineers construct, design and test applications based on scientific principles and programming languages. Data modelling, software modelling software usability and reusability testing are specific areas of research in software engineering (Garcia et al. 2020). On the other hand, SDLC, UML, software maintenance, cloud computing, block chain technologies, big data analytics, mobile computing, and machine learning are new and hot trends in the field of software engineering. Besides, it is found that human-computer interaction, big data analytics, machine learning, and artificial intelligence are intensively being used in different industries which is the main reason behind their increased popularity. – [could you please change this sentence]

Relation to computer science

Software engineering is widely related to computer science which focuses on developing a greater understanding of interconnected aspects of modern computers. Another study has mentioned that logical thinking required in software engineering is related to physics which allows software engineers to troubleshoot problems encountered during the software development or maintenance process (Striuk and Semerikov, 2022). Accordingly, software engineers need basic mathematics skills to handle system load activity, ballparking estimation, A/B split test and many more. Therefore, it can be said that software engineering is a combination of mathematics, physics and computer science.

Research questions and methods


Q1. What are the implications of cloud computing, machine learning as well as artificial intelligence in contemporary business? –[can you please change these two questions and keep software engineering related to like software modelling like that instead of AI,ML.]

Q2. How big data analytics and AI is helping businesses to increase user experience and customer satisfaction?


For the evaluation of these research questions a secondary qualitative method will be followed. Johnson et al. (2020) said that to gain a deeper understanding of a particular phenomenon or research situation, qualitative research is essential as it minimises research time by providing relevant, structured and reliable data. Hence, the use of secondary qualitative information can help researchers to understand how big data analytics and artificial intelligence are improving user experience and ensuring customer satisfaction. More to the context, analysis of relevant literature would help to implicate cloud computing, machine learning and AI in the growth of the contemporary business.( as research question changes this changes)

General relevance

Software engineering increases the functionality, usability, reliability and portability of technologies. Besides, it is required to mention that the field of software engineering focuses on design implementation at a land scale by analysing computer software systems which are based on personalised algorithms (Lwakatare et al. 2020). In a similar context, the field of software engineering cultivates software design and helps to maintain software and drive innovation with programming security skills and algorithms.

Additionally, this field helps the industry to grow by improving design, maintenance and operational activities. Besides, advanced software assists to manage and monitor complex projects and identifies risks and probable areas of improvement. In recent times, it is found that demand for software engineering programmers has increased at the global level by 26% because of the increased involvement of advanced technologies in the business development process (Amershi et al. 2019).

However, extra dependency on the business of technologies might lead to sudden failure of operational processes if they encounter a breakdown of the technological framework. Besides, the rapid advancement of technologies, limited infrastructure, and undefined boundaries can increase ethical issues in software engineering (Obaidi and Klünder, 2021).

Reference List:

Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B. and Zimmermann, T., 2019, May. Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (pp. 291-300). IEEE.

Enoiu, E., Tukseferi, G. and Feldt, R., 2020, December. Towards a model of testers' cognitive processes: Software testing as a problem-solving approach. In 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C) (pp. 272-279). IEEE.

Garcia, I., Pacheco, C., Méndez, F. and Calvo‐Manzano, J.A., 2020. The effects of game‐based learning in the acquisition of “soft skills” on undergraduate software engineering courses: A systematic literature review. Computer Applications in Engineering Education28(5), pp.1327-1354.

Johnson, J.L., Adkins, D. and Chauvin, S., 2020. A review of the quality indicators of rigor in qualitative research. American Journal of pharmaceutical education84(1).

Lwakatare, L.E., Raj, A., Crnkovic, I., Bosch, J. and Olsson, H.H., 2020. Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions. Information and software technology127, p.106368.

Obaidi, M. and Klünder, J., 2021. Development and application of sentiment analysis tools in software engineering: A systematic literature review. Evaluation and Assessment in Software Engineering, pp.80-89.

Striuk, A.M. and Semerikov, S.O., 2022, June. Professional competencies of future software engineers in the software design: teaching techniques. In Journal of Physics: Conference Series (Vol. 2288, No. 1, p. 012012). IOP Publishing.