Estimating software reliability in maintenance phase through ann and statistics

TL;DR

This paper proposes a method using Artificial Neural Networks and statistical analysis to predict software reliability during the maintenance phase, focusing on time to fix errors and implement enhancements.

Contribution

It introduces a neural network-based approach combined with statistical methods to accurately forecast maintenance time, highlighting the complexity of maintenance data analysis.

Findings

ANN predictions align closely with statistical results

Neural networks effectively model maintenance data relationships

Maintenance data analysis is inherently complex

Abstract

Maintenance is the last and the most critical phase of the software development life cycle. It involves debugging of errors and different types of enhancements which are requested by the user. Software reliability regarding maintenance is the most crucial part as it depends upon the time and cost to correct the errors and make enchantements. It is often felt that software errors or correction takes time to be removed. The maintenance time depends upon the nature of the occurred errors and requested enhancements. In this research work we predict the software reliability in terms of time taken to maintain the errors and enhancements. Artificial Neural Network is used to analyze and predict the software reliability of the maintenance phase. At the end statistical results and proposed neural network results are also compared to make sure that forecasted results are equal to the output results. These results are compared to show that the ANN can understand the relationship between data in a better way. This research work also shows how much it is difficult to understand the nature of maintenance data.