Improving Software Reliability Prediction Accuracy Using CRO-Based FLANN

Behera, Ajit; Nayak, Sarat Chandra; Dash, Ch. Sanjeev Kumar; Dehuri, Satchidananda; Panda, Mrutyunjaya

doi:10.1007/978-981-10-8201-6_24

Cited by 17 publications

(6 citation statements)

References 8 publications

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“…It includes details about failure and its level. The second one is the time interval, which defines the time gap between one failure (Behera et al , 2019) and another failure. Since time management and other factors are essential in testing, the minimum gap has to be maintained for attaining a better software product.…”

Section: Software Quality Modelmentioning

confidence: 99%

Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)

Sudharson

Dr²

2020

IJIUS

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PurposeSoftware reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.Design/methodology/approachAs testing improves quality and reliability of the application by identifying the bugs in it. Also, it defines the behavior and state of the product based on the defined principles and mechanisms. Conventional reliability models use statistical distributions to attain realistic features.FindingsThe ability to predict the bugs in the application during development phase itself is a proper testing practice which saves the time and increases the efficiency of the application. Efficient management and timely release of the product is based on this reliability testing and ant colony optimization (ACO)-based testing is an important optimization model which is available for testing the application.Originality/valueConventional ant colony optimization used test case generation as its common approach for testing the reliability of the application. ACO uses pheromone activity and it is related in testing of application and provides a simple positive mechanism by identifying the inactivity and precociousness.

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Section: Software Quality Modelmentioning

confidence: 99%

Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)

Sudharson

Dr²

2020

IJIUS

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“…The efficiency of the proposed forecasting approach has been observed from above discussions. The average MAPE gain on adopting ICRO-DNM over other forecasts are calculated as in (14) and are shown in Fig. 19 From these figures, it can be seen that the MAPE gain of the proposed approach over others is significant.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Pretraining an extreme learning method with CRO for financial forecasting is proposed by Nayak and Misra [13]. CRO hybridized with functional link ANN is applied for improved software reliability prediction [14]. The study claimed that CRO efficiently adjusted the neural network parameters compared to other learning methods.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

2022

Self Cite

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Chemical reaction optimization (CRO) algorithm is a robust metaheuristic by simulating the process of natural chemical reaction and has been applied for solving numerous realistic problems. However, it is suffering from low population diversity, slow convergence, and deprived from local search ability. To boost the search operation of CRO, this article proposes an improved CRO method termed as ICRO. We achieve the improvements in two phases. First, to start the search operation with a better-quality population, we propose a new method to initialize population which helps in enhancing diversification of the search space. Second, to intensify the local search ability we integrate Nelder-Mead simplex method with CRO. The ICRO is then evaluated on a few benchmark functions for optimization and found apposite. Dendritic neuron model (DNM) using additive and multiplicative-based aggregation functions has been emerging as a machine learning approach and found successful in many engineering applications. This study attempts to advance the predictive accuracy of DNM through maintaining a decent steadiness between exploitation and exploration of its search space with the proposed ICRO, termed as ICRO-DNM. The powerful global search ability of ICRO synergies with better approximation capability of DNM thus, able to overcome the limitations of conventional back propagation learning based DNM. ICRO-DNM is evaluated on closing price prediction for four stock index and net asset values of four mutual fund datasets and found efficient. The consequences of this attempt are: (1) Proposed ICRO shows robust parameter optimization ability for benchmark functions and DNM compared to basic CRO, particle swarm optimization (PSO), and genetic algorithm (GA); (2) The learning paradigm formed due to reasonable amalgamation of ICRO and DNM is pretty able to capture the underlying uncertainties coupled with financial data, produces more precise and steady predictions and significantly different from other forecasts.

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“…[ Behera et al 2019] proposed a hybrid technique based on chemical reaction optimization (CRO) and FLANN. They concluded that their proposed hybrid model was performed better than conventional models for software reliability prediction.…”

Section: Related Workmentioning

confidence: 99%

A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction

Kumar¹

2022

jucs

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Context: Reliability prediction is critical for software engineers in the current challenging scenario of increased demand for high-quality software. Even though various software reliability prediction models have been established so far, there is always a need for a more accurate model in today's competitive environment for producing high-quality software. Objective: This paper proposes a neuro-fuzzy hybridized method by integrating self-organized- map (SOM) and fuzzy time series (FTS) forecasting for the reliability prediction of a software system. Methodology: In the proposed approach, a well-known supervised clustering algorithm SOM is incorporated with FTS forecasting for developing a hybrid model for software reliability prediction. To validate the proposed approach, an experimental study is done by applying proposed neuro-fuzzy method on a software failure dataset. In addition, a comparative study was conducted for evaluating the performance of the proposed method by comparing it with some of the existing FTS models. Results: Experimental outcomes show that the proposed approach performs better than the existing FTS models. Conclusion: The results show that the proposed approach can be used efficiently in the software industry for software reliability prediction.

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Improving Software Reliability Prediction Accuracy Using CRO-Based FLANN

Cited by 17 publications

References 8 publications

Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)

Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction

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