Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)

Abstract

Purpose

Software reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.

Design/methodology/approach

As testing improves quality and reliability of the application by identifying the bugs in it. Also, it defines the behavior and state of the product based on the defined principles and mechanisms. Conventional reliability models use statistical distributions to attain realistic features.

Findings

The ability to predict the bugs in the application during development phase itself is a proper testing practice which saves the time and increases the efficiency of the application. Efficient management and timely release of the product is based on this reliability testing and ant colony optimization (ACO)-based testing is an important optimization model which is available for testing the application.

Originality/value

Conventional ant colony optimization used test case generation as its common approach for testing the reliability of the application. ACO uses pheromone activity and it is related in testing of application and provides a simple positive mechanism by identifying the inactivity and precociousness.

Citation

D, S. and Dr, P. (2020), "Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)", International Journal of Intelligent Unmanned Systems, Vol. 8 No. 2, pp. 129-140. https://doi.org/10.1108/IJIUS-09-2019-0052