New Trends in Learning for Software Engineering
- Alaa Hamouda
- Corresponding Author
- Alaa Hamouda
Available Online November 2015.
- https://doi.org/10.2991/racs-15.2016.8How to use a DOI?
- machine learning, software engineering, learning algorithms
- Software is nowadays a critical component of our lives and everyday-work working activities. However, as the technological infrastructure of the modern world evolves a great challenge arises for developing high quality software systems with increasing size and complexity. Software engineers and researchers are striving to meet this challenge by developing and implementing software engineering methodologies able to deliver software products of high quality, within budget and time constraints. The field of machine learning in software engineering has recently emerged to provide means for addressing, studying, analyzing, and understanding critical software development issues and at the same time to offer mature machine learning techniques such as artificial neural network, Bayesian networks, decision trees, fuzzy logic, genetic algorithms, and rule induction. Machine learning algorithms have proven to be of great practical value to software engineering. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems and approached in terms of learning algorithms. In this paper, we first take a look at the characteristics and applicability of some frequently utilized machine learning algorithms. We then present the application of machine learning in the different phases of software engineering that include project planning, requirements
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Alaa Hamouda PY - 2015/11 DA - 2015/11 TI - New Trends in Learning for Software Engineering BT - International Conference on Recent Advances in Computer Systems PB - Atlantis Press UR - https://doi.org/10.2991/racs-15.2016.8 DO - https://doi.org/10.2991/racs-15.2016.8 ID - Hamouda2015/11 ER -