New Trends in Learning for Software Engineering

DOI

10.2991/racs-15.2016.8How to use a DOI?

Keywords

machine learning, software engineering, learning algorithms

Abstract

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

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Alaa Hamouda
PY  - 2015/11
DA  - 2015/11
TI  - New Trends in Learning for Software Engineering
BT  - Proceedings of the 2015 International Conference on Recent Advances in Computer Systems
PB  - Atlantis Press
SP  - 46
EP  - 53
SN  - 2352-538X
UR  - https://doi.org/10.2991/racs-15.2016.8
DO  - 10.2991/racs-15.2016.8
ID  - Hamouda2015/11
ER  -