Artificial intelligence meets software engineering in the classroom

We aimed to assess the reliability of teaching Artificial Intelligencefor Software Engineering master students. We propose a semi-interactive course where the students have to develop applications for solving real world problems by using various intelligent tools. We try to integrate these two disciplines, since both deal with modeling of the real case studies, sharing some common elements.We report on a study that we conducted on observing student teams as they develop AI-based applications. We validate the proposed semi-interactive course by using various criteria. In addition, we checked if some best practices from industrial teams are followed by our students.

[1]  S. Wolfram Statistical mechanics of cellular automata , 1983 .

[2]  C. Ghezzi,et al.  The challenges of software engineering education , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[3]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Laurie A. Williams,et al.  Have Agile Techniques been the Silver Bullet for Software Development at Microsoft? , 2013, 2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement.

[5]  J. Ross Quinlan,et al.  Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .

[6]  Miryung Kim,et al.  Data Scientists in Software Teams: State of the Art and Challenges , 2018, IEEE Transactions on Software Engineering.

[7]  Miryung Kim,et al.  The Emerging Role of Data Scientists on Software Development Teams , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[8]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[9]  John R. Koza,et al.  Genetic Programming III - Darwinian Invention and Problem Solving , 1999, Evolutionary Computation.

[10]  Benjamin S. Bloom,et al.  Taxonomy of Educational Objectives: The Classification of Educational Goals. , 1957 .

[11]  D. Sculley,et al.  Hidden Technical Debt in Machine Learning Systems , 2015, NIPS.

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  D. Kolb,et al.  Experiential learning in teams , 2005 .