Prediction of rate of improvement of software quality and development effort on the basis of Degreeof excellence with respect to number of lines of code

TL;DR

This research proposes a method to predict the rate of improvement in software quality based on the degree of excellence and lines of code, aiming to better estimate development effort and enhance software quality management.

Contribution

It introduces a system to predict software quality improvement rate using error levels and degree of excellence at different development stages.

Findings

Predicted rate of quality improvement correlates with development effort.

Used indigenous tools for quality prediction and data visualization.

Provides a new parameter for assessing software development progress.

Abstract

The objective of this research work is to improve the degree of excellence by removing the number of exceptions from the software. The modern age is more concerned with the quality of software. Extensive research is being carried out in this direction. The rate of improvement of quality of software largely depends on the development time. This development time is chiefly calculated in clock hours. However development time does not reflect the effort put in by the developer. A better parameter can be the rate of improvement of quality level or the rate of improvement of the degree of excellence with respect to time. Now this parameter needs the prediction of error level and degree of excellence at a particular stage of development of the software. This paper explores an attempt to develop a system to predict rate of improvement of the software quality at a particular point of time with respect to the number of lines of code present in the software. Having calculated the error level and degree of excellence at two points in time, we can move forward towards the estimation of the rate of improvement of the software quality with respect to time. This parameter can estimate the effort put in while development of the software and can add a new dimension to the understanding of software quality in software engineering domain. In order to obtain the results we have used an indigenous tool for software quality prediction and for graphical representation of data, we have used Microsoft office 2007 graphical chart.