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

  • Neelamadhab PadhyEmail author
  • Rasmita Panigrahi
  • K. Neeraja
Special Issue
  • 6 Downloads

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.

Keywords

Software metrics Proposed threshold models and algorithms Machine learning Identification of threshold Derivation of thresholds Performance measurement 

Notes

Acknowledgements

This research was partially supported by our own published patent. The below mentioned patent was published in the month of December 2018 having application no-201831041970. We are thankful to our colleagues who provided expertise that greatly assisted the research, although they may not agree with all of the interpretations provided in this paper. We are also grateful to Professor Suresh Chandra Satapathy for assistance with novel evolutionary techniques and moderated this paper and in that line improved the manuscript significantly. I have to express my appreciation to the co-authors Mrs. Rasmita Panigrahi (GIET, University) and K. Neeraja (MLR Institute of Technology, Hyderabad) for sharing their pearls of wisdom with us during the course of this research. We are also immensely grateful to the editors, reviewers for their comments on an earlier version of the manuscript.

Supplementary material

12065_2019_201_MOESM1_ESM.docx (12 kb)
Supplementary material 1 (DOCX 12 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.GIET University, GunupurOdishaIndia
  2. 2.MLR Institute of TechnologyHyderabadIndia

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