ITI42120 Advanced Topics in Information Systems (Autumn 2021)

The course is connected to the following study programs

Elective course in the master programme in applied computer science, full-time and part-time.

Recommended requirements

Knowledge about:

• DevOps tools and frameworks
• Machine learning

Lecture Semester

First or third semester (autumn) in the full-time programme.

First, third or seventh semester (autumn) in the part-time programme.

The student's learning outcomes after completing the course

Knowledge

The student

• knows the basics of Digital Transformation
• understands the role of Information Systems in Digital Transformation initiatives
• is familiar with principles of IT Governance
• is familiar with global trends in business software with regards to its deployment and development
• has a good overview of DevOps tools and approaches
• knows how to measure costs in software development in DevOps and traditional settings
• understands the role and importance of Software Engineering in Artificial Intelligence-based solutions

Skills

The student

• is able to place and articulate Information Systems function in a Digital Transformation initiative
• is able to identify and design a basic IT Governance plan
• knows the different approaches and trends in business software
• knows how to measure business value and costs in software developments
• is able to identify the need to use a set of DevOps software tools
• is capable of using a set of DevOps software tools for business needs
• knows how to use and justify the use of software engineering techniques in Artificial Intelligence-based solutions

General competence

The student is able to apply

• scientific theories and methodologies in a practical business setting.
• technologies in a practical business setting.

Content

1. Digital Transformation and IT Governance
2. Trends in Business Software
3. DevOps and Continuous Software Engineering
4. Software Engineering for Artificial Intelligence

Forms of teaching and learning

Teaching will be based on blended learning approaches. There will be recorded lectures of the topics of the course and in a weekly or bi-weekly basis, physical meetings will take place to mentor the development of the paper and guide students in the course.

Workload

Approx. 280 hours.

Examination

Scientific paper and individual oral exam

The students need to develop a scientific paper on a selected topic. The topic is chosen by the students and agreed with the course responsible. The paper can be developed individually or in groups two students. The students are given an individual tentative grade on the paper using the A - F grading scale. This grade can be adjusted up to 2 stages at the oral exam.

The individual oral exam is based on regular topics in the course, aspects of the paper developed and a case. Duration approx. 20-30 min. No supporting materials allowed.

If the student decides to challenge the assessment, the scientific paper must be re-assessed. If the new assessment affects the tentative grading of the paper, a new oral exam will be arranged.

Examiners

External and internal examiner, or two internal examiners.

Conditions for resit/rescheduled exams

Upon re-examination, both parts of the examination must be retaken.

Course evaluation

This course is evaluated by a:

• Mid-term evaluation (compulsory)

The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.

Literature

Last updated 21.10.2020. The reading list may be subject to changes before 1st of June 2021.

DIGITAL TRANSFORMATION & IT GOVERNANCE

Andriole, S. J. (2017). Five myths about digital transformation. MIT sloan management review, 58(3).

Ebert, C., & Duarte, C. H. C. (2018). Digital transformation. Ieee Software, (4), 16-21.

Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2).

Hinings, B., Gegenhuber, T., & Greenwood, R. (2018). Digital innovation and transformation: An institutional perspective. Information and Organization, 28(1), 52-61.

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144.

De Haes, S., & Van Grembergen, W. (2009). An exploratory study into IT governance implementations and its impact on business/IT alignment. Information Systems Management, 26(2), 123-137.

Joshi, A., Bollen, L., Hassink, H., De Haes, S., & Van Grembergen, W. (2018). Explaining IT governance disclosure through the constructs of IT governance maturity and IT strategic role. Information & Management, 55(3), 368-380.

Mohan, K., Cao, L., Sarkar, S., & Ramesh, B. (2019). Adapting IT Governance Practices for the Changing IT Function. IT Professional, 21(1), 27-33.

Juiz, C., & Toomey, M. (2015). To govern IT, or not to govern IT?. Communications of the ACM, 58(2), 58-64.

Vejseli, S., Proba, D., Rossmann, A., & Jung, R. (2018). The agile strategies in IT Governance: Towards a framework of agile IT Governance in the banking industry. Twenty-Sixth European Conference on Information Systems (ECIS2018), Portsmouth,UK, 2018

https://www.pmi.org/disciplined-agile/process/it-governance

https://www.projectmanagement.com/blog-post/61871/What-is-Lean-IT-Governance-

TRENDS IN BUSINESS SOFTWARE

Berberat, S., & Baudet, C. (2019). Assessing a Business Software Application using Strategic IT Alignment Factors: A New Way for IS Evaluation?.

De Lauretis, L. (2019). From Monolithic Architecture to Microservices Architecture. In 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (pp. 93-96). IEEE.

Jansen, S., Cusumano, M., & Popp, K. M. (2019). Managing software platforms and ecosystems. IEEE Software, 36(3), 17-21.

Jason, G., Nikolay, M., & Guohua, W. The Partner Ecosystem Evolution from On-premises Software to Cloud Services: a case study of SAP.

Loukis, E., Janssen, M., & Mintchev, I. (2019). Determinants of software-as-a-service benefits and impact on firm performance. Decision Support Systems, 117, 38-47.

Raghavan R., S., K.R., J. & Nargundkar, R.V. (2020). Impact of software as a service (SaaS) on software acquisition process, Journal of Business & Industrial Marketing, 35(4), 757-770. https://doi.org/10.1108/JBIM-12-2018-0382

Yrjönkoski, T., & Systä, K. (2019, August). Productization levels towards whole product in SaaS business. In Proceedings of the 2nd ACM SIGSOFT International Workshop on Software-Intensive Business: Start-ups, Platforms, and Ecosystems (pp. 42-47).

DEVOPS AND CONTINUOUS SOFTWARE ENGINEERING

Fitzgerald, B., & Stol, K. J. (2017). Continuous software engineering: A roadmap and agenda. Journal of Systems and Software, 123, 176-189.

O'Connor, R. V., Elger, P., & Clarke, P. M. (2017). Continuous software engineering—A microservices architecture perspective. Journal of Software: Evolution and Process, 29(11), e1866.

Johanssen, J. O., Kleebaum, A., Paech, B., & Bruegge, B. (2018, May). Practitioners' eye on continuous software engineering: an interview study. In Proceedings of the 2018 International Conference on Software and System Process (pp. 41-50).

Ebert, C., Gallardo, G., Hernantes, J., & Serrano, N. (2016). DevOps. Ieee Software, 33(3), 94-100.

Zhu, L., Bass, L., & Champlin-Scharff, G. (2016). DevOps and its practices. IEEE Software, 33(3), 32-34.

Humble, J., & Molesky, J. (2011). Why enterprises must adopt devops to enable continuous delivery. Cutter IT Journal, 24(8), 6.

https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=527148

https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=638576

SOFTWARE ENGINEERING FOR ARTIFICIAL INTELLICENCE

Menzies, T. (2019). The Five Laws of SE for AI. IEEE Software, 37(1), 81-85.

McDermott, T., DeLaurentis, D., Beling, P., Blackburn, M., & Bone, M. (2020). AI4SE and SE4AI: A Research Roadmap. INSIGHT, 23(1), 8-14.

Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., ... & 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.