Conference Paper

An Artificial Neural-Network-Based Approach to Software Reliability Assessment

Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu
DOI: 10.1109/TENCON.2005.301242 Conference: TENCON 2005 2005 IEEE Region 10
Source: IEEE Xplore


In this paper, we propose an artificial neural- network-based approach for software reliability estimation and modeling. We first explain the network networks from the mathematical viewpoints of software reliability modeling. That is, we will show how to apply neural network to predict software reliability by designing different elements of neural networks. Furthermore, we will use the neural network approach to build a dynamic weighted combinational model. The applicability of proposed model is demonstrated through four real software failure data sets. From experimental results, we can see that the proposed model significantly outperforms the traditional software reliability models.

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Available from: Yi-Shin Chen
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    • "Result showed that neural network architecture has a great impact on the performance of the network. Yu Shen Su et al.[23]purposed a model that uses the neural network approach to build a dynamic weighted combinational model. Then compared the performances of the neural network models with some conventional SRGMs from three aspects: goodness of fit, prediction ability for short-term prediction and long-term prediction. "

    Preview · Article · Jun 2015
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    • "The software reliability can be defined by the following characteristic:  Correctness  Consistency and precision  Robustness  Simplicity  Traceability Software reliability is one of the important factor been considered while ensuring the software quality. In simple term we can say that software reliability deals with the failure or faults that exist in the system [5]. Failure and fault are two different factors which are generally inbuilt in our software during the development phase. "
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    ABSTRACT: Software reliability is the ability of the software to perform its specified function under some specific condition. Reliability can be associated with both hardware and software. The hardware reliability can easily be evaluated since hardware get wear out but in case of software it be very difficult. In fact we can"t determine or predict the actual reliability of the software by using some specified parameter. The paper summarized the performance of different reliability models till been designed and also reflect the different relationship that exist between different parameters. The paper will also introduce the concept of neural network which is been considered as one of the efficient technique been used for estimation or prediction. Generally unsupervised learning technique is been used for generalizing new optimizing technique. So if we use neural network for calculating the software reliability then it may be possible for us to predict the reliability more effectively. General terms: Neural network, reliability, Exponential model, Logarithm model.
    Preview · Article · Apr 2012 · International Journal of Computer Applications
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    • "Su et al. [12] have proposed a neural network based approach to software reliability assessment combining various existing models into a Dynamic Weighted Combinational Model (DWCM). Kapur et al. [13] have proposed an ANN based Dynamic Integrated Model (DIM), which is an improvement over DWCM given by Su et al. [12]. Kapur et al. [14] have proposed a Generalized Dynamic Integrated Model (GDIM) using ANN approach, which incorporates the concept of n types of faults. "
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    ABSTRACT: Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers in tracking and meas-uring the growth of reliability. Most of the Software Reliability Growth Models, which have been proposed, treat the event of software fault detection in the testing and operational phase as a counting process. Moreover, if the size of software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small com-pared with the initial fault content at the beginning of the testing phase. Therefore in such a situation, we can model the software fault detection process as a stochastic process with a continuous state space. Recently, Artificial Neural Net-works (ANN) have been applied in software reliability growth prediction. In this paper, we propose an ANN based software reliability growth model based on ˆIto type of stochastic differential equation. The model has been validated, evaluated and compared with other existing NHPP model by applying it on actual failure/fault removal data sets cited from real software development projects. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP based model.
    Full-text · Article · Oct 2011 · Journal of Software Engineering and Applications
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