Abstract
This chapter introduces a novel learning scheme based on chemical reaction optimization (CRO) for training functional link artificial neural network (FLANN) to improve the accuracy of software reliability prediction. The best attributes of FLANN such as capturing the inner association between software failure time and the nearest ‘m’ failure time have been harnessed in this work. Hence, this article combines the best attributes of these two methodologies known as CRO and FLANN to assess the potency in predicting time-to-next failure of software products. The extensive experimental study on a few benchmarking software reliability datasets reveals that the proposed approach fits the historical fault datasets better and more accurately predicts the remaining number of faults than traditional approaches.
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Behera, A.K., Nayak, S.C., Dash, C.S.K., Dehuri, S., Panda, M. (2019). Improving Software Reliability Prediction Accuracy Using CRO-Based FLANN. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 32. Springer, Singapore. https://doi.org/10.1007/978-981-10-8201-6_24
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DOI: https://doi.org/10.1007/978-981-10-8201-6_24
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