A survey in the area of machine learning and its application for software quality prediction

This paper explores software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The objective of this research is to apply the various machine learning approaches, such as Case-Based Reasoning and Fuzzy logic, to predict software quality. The system predicts the error after accepting the values of certain parameters of the software. This paper advocates the use of case-based reasoning (i.e., CBR) to build a software quality prediction system with the help of human experts. The prediction is based on analogy. We have used different similarity measures to find the best method that increases reliability. This software is compiled using Turbo C++ 3.0 and hence it is very compact and standalone. It can be readily deployed on any configuration without affecting its performance.

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