Application of Fuzzy Time Series in Prediction of Time Between Failures & Faults in Software Reliability Assessment

Abstract

Since last seventies, various software reliability growth models (SRGMs) have been developed to estimate different measures related to quality of software like: number of remaining faults, software failure rate, reliability, cost, release time, etc. Most of the exiting SRGMs are probabilistic. These models have been developed based on various assumptions. The entire software development process is performed by human being. Also, a software can be executed in different environments. As human behavior is fuzzy and the environment is changing, the concept of fuzzy set theory is applicable in developing software reliability models. In this paper, two fuzzy time series based software reliability models have been proposed. The first one predicts the time between failures (TBFs) of software and the second one predicts the number of errors present in software. Both the models have been developed considering the software failure data as linguistic variable. Usefulness of the models has been demonstrated using real failure data.

Soft kompütinq metodları əsasında proqram təminatının etibarlılığının təmin edilməsi

“…Lakin genetik alqoritmlər (genetic algorithm), genetik proqramlaşdırma (genetic programming), süni arı sürüsü alqoritmi (artificial bee colony algorithm ), ququ quşu (cuckoo search) alqoritminin tətbiqi ilə də modellər işlənilmişdir [12][13][14][15][16][17].…”

Software Reliability Modeling using Soft Computing Techniques: Critical Review

“…S. Chatterjee et al [40] two fuzzy time series based software reliability models have been proposed. The first one predicts the time between failures of software and the second one predicts the number of errors present in software.…”

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“…After identifying and removing the outliers, the universe of discourse is defined by (5), where ‫ܦ‬ is the min value in the sample data, ‫ܦ‬ ௫ is the max and ߪ is the standard deviation of the data.…”