Prediction of Software Reliability using Bio Inspired Soft Computing Techniques

Abstract

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

Relationship Between CBS Quality Parameters for Assessment of Computational Intelligence Techniques

“…Some of the previous work of researchers is shown below based on computational techniques like Fuzzy Logic, ACO, ABC, PSO, GA, etc. Diwaker et al [3] assessed different computational techniques such as Neural-Network, Fuzzy Logic, Particle Swarm Optimization, Genetic Algorithm, Support Vector Machine, Ant Colony Optimization, and Artificial Bee Colony and their ability to predict reliability using various parameters. According to the author, the concepts discussed can be used to predict the reliability of software as well as hardware.…”

Multicriteria Cuckoo search optimized latent Dirichlet allocation based Ruzchika indexive regression for software quality management

“…But the algorithms failed to perform the scalability analysis. The soft computing techniques were introduced in [14] for predicting software reliability. A new framework was developed in [15] for detecting the error in the software system.…”

Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

“…Earlier researches focused on to reduce complexities and the failure rate is high in the system. It is a difficult task to compute the best cost in a large area with a population at the random movement of many components [5,6]. At present, the software testing takes more time and cost and makes the software development process an expensive task.…”