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.
There are different life cycle models available for developing various types of software. Every Software Development Life Cycle (SDLC) model has some advantages and some limitations. In that case software developers decide which SDLC model is suitable for their product. Further, we need development of software in a systematic and disciplined manner. This is advantage of using a life cycle model. A life cycle model forms a common understanding of the activities among the software engineers and helps to develop software in a proper manner, so that time can be reduced. The objective of this paper is to compare all traditional or existing SDLC model with our Proposed SDLC model for development of software in effective and efficient manner.
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