Threshold estimation from software metrics by using evolutionary techniques and its proposed algorithms, models

Abstract

The software metrics play the important role in the software industry. As the software industry growing in size and complexity enhanced support is mandatory for computing and managing the software quality. Quality measurement is one of the key features of the manager in the software industry; where threshold plays the crucial role. Software measurement is necessary by means for evaluating different quality attributes and characteristics, such as size, complexity, maintainability, and usability. Instead of that effective and efficient software system is straightforward dependent on the meaning of suitable thresholds. The objective of this paper is to estimate the threshold values from software metrics by using novel evolutionary intelligence techniques. The threshold and aging software design optimization algorithms and models to prevent software aging by using machine learning (evolutionary algorithms). Apart from the above-mentioned techniques, this paper also proposed a novel threshold estimation, aging, and survivability aware (sensitive) reusability optimization model of an object-oriented software system. To expand firmness, aging and survivability aware (sensitive) optimization threshold scheme aging prediction and software rejuvenation model and algorithms proposed.