Enhancing Software Reliability Prediction based on Hybrid Fuzzy k-Nearest Neighbor with Glowworm Swarm Optimization (FKNN-GSO) Algorithm
Shailee Lohmor1, B. B. Sagar2

1Shailee Lohmor, Research Scholar, Research & Development Centre, Bharathiar University, Coimbatore, (Tamil Nadu), India.
2Dr. B. B. Sagar, Birla Institute of Technology, Mesra- Ranchi , (Jharkhand) India.

Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 513-522 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2287037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Predicting software reliability means gauging the future occurrences of failures in software in order to align the process of the software maintenance. This paper presents a model based on FKNN (Fuzzy k-Nearest Neighbor) and nature inspired Glowworm swarm optimization (GSO) to understand the relationship between the data of software failure time and the nearest n failure time and finally predict the reliability of the software. Glowworm-Swarm Optimization (GSO) is used to search finest combination of weights aimed to obtain maximum regression accuracy and fuzzy k-nearest neighbor (FKNN) to allocate the degree of membership to various software metrics using fuzzy logic concepts. The performance of the proposed model has been compared with the known existing models to evaluate the prediction efficiency of GSO- FKNN.
Keywords: Fuzzy Membership function, Glowworm swarm Optimization, K-Nearest Neighbor, Software Reliability Prediction,  Mean-Absolute Error, Mean-Squared Error.
Scope of the Article: Software Engineering Methodologies