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Abstract

The role of software reliability and quality improvement is becoming more important than any other issues related to software development. To date, we have various techniques that give a prediction of software reliability like neural networks, fuzzy logic, and other evolutionary algorithms.  A genetic algorithm has been explored for predicting software reliability.  One of the important aspects of software quality is called software reliability, thus, software engineering is of a great place in the software industry. To increase the software reliability, it is mandatory that we must design a model that predicts the fault and error in the software program at early stages, rectify them and then increase the functionality of the program within a minimum time and in a low cost. There exist numerous algorithms that predict software errors such as the Genetic Algorithm, which has a very high ability to predict software bugs, failure and errors rather than any other algorithm. The main purpose of this paper is to predict software errors with so precise, less time-consuming and cost-effective methodology. The outcome of this research paper is showing that the rates of applied methods and strategies are more than 96 percent in ideal conditions. 

Keywords

Evolutionary algorithms Genetic algorithm Faulty /non-faulty data analysis Software reliability engineering.

Article Details

How to Cite
Jain, R., & Sharma, A. (2019). ASSESSING SOFTWARE RELIABILITY USING GENETIC ALGORITHMS. The Journal of Engineering Research [TJER], 16(1), 11–17. https://doi.org/10.24200/tjer.vol16iss1pp11-17
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