Abstract
The software engineering industry is an everchanging domain requiring professionals to have a good knowledge base and adaptability skills.Artificial Intelligence (AI) has achieved substantial success in enhancing program analysis techniques and applications, including bug prediction. It is a promising direction by applying advanced Machine Learning techniques into suitable software engineering tasks.
The main goal of this paper is to propose a pedagogical interdisciplinary approach that pave the path for developing an e-learning platform serving to check the quality of the source code that students wrote by means of Artificial Intelligence techniques. By putting into practice this proposal, we are planning to show the students how to combine concepts learned from two different courses. The first step of this approach would be part of the Advanced Programming Methods, a Software Engineering related course, where students learn about the importance of writing good quality code and use software metrics as a mean of software quality assessment. Then, the following steps will be integrated into the Artificial Intelligence course, where students learn about different Machine Learning algorithms and how to apply them to solve practical problems. Thus, as an applicability in this respect, students use the metric values calculated for their projects developed at Advanced Programming Methods course as lab assignments and also to train (at Artificial Intelligence class) a bug detection model able to estimate the quality of new codebases.
The proposed approach is helpful for both students and teachers. On one side, it helps the students understand the importance of writing clean, high-quality code. And on the other side, it helps teachers in their evaluation process by giving them time to focus on different aspects of homework than the code quality.
Citare
@Inproceedings{Cernau2022APA,
author = {L. Cernau and L. Dioşan and C. Serban},
booktitle = {EASEAI@ESEC/SIGSOFT FSE},
title = {A pedagogical approach in interleaving software quality concerns at an artificial intelligence course},
year = {2022}
}