Machine learning deals with the issue of how to build programs that improve their
performance at some task through experience. Machine learning algorithms have 
proven to be of great practical value in a variety of application domains. 
They are particularly useful for (a) poorly understood problem domains where 
little knowledge exists for the humans to develop effective algorithms; (b) 
domains where there are large databases containing valuable implicit 
regularities to be discovered; or (c) domains where programs must adapt to 
changing conditions. Not surprisingly, the field of software engineering turns 
out to be a fertile ground where many software development and maintenance 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 provide formulations of those software development tasks using learning 
algorithms. Finally, a brief summmary is given of the existing work.