Benefits and Challenges of Applying Machine Learning in Adaptive Systems

Adaptive software systems have the ability to configure their process based on the input. This means that, they can change their behavior based on internal changes, or changes in the running environment. Different user requirement, big data inputs, large scale services, and always available processes made adaptive systems evolve even more. However, there is always a gap that can be filled in order to improve them further. Machine learning is one of the techniques that is widely used to improve different software systems. Its main idea is to use previous knowledge, train it, and predict new results. There exist a bunch of algorithms that are being used in this evolving area, and adaptive systems are good targets where they can have a great effect. This paper reviewed the main machine learning algorithms that are being used for adaptive systems, and further it considered the impact and challenge of these techniques in the usability of the systems