Hybrid Approaches to Software Reliability: Evaluating and Enhancing Prediction Models Mercedes Benz Research and Development India

Format:

Book

Conference/Event

Author/Creator:

Mahdev, Akash Ravishankar, author.

Contributor:

Lal, Vinayak

Mathur, Rachit

Muralimohan, Pramod

Reddy, Hemanjaneya

Conference Name:

Automotive Technical Papers (2025-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Software reliability prediction involves predicting future failure rates or expected number of failures that can happen in the operational timeline of the software. The time-domain approach of software reliability modeling has received great emphasis and there exists numerous software reliability models that aim to capture the underlying failure process by using the relationship between time and software failures. These models work well for one-step prediction of time between failures or failure count per unit time. But for forecasting the expected number of failures, no single model will be able to perform the best on all datasets. For making accurate predictions, two hybrid approaches have been developedminimization and neural networkto give importance to only those models that are able to model the failure process with good accuracy and then combine the predictions of them to get good results in forecasting failures across all datasets. These models once trained on the dataset are expected to give better accuracy on average and eliminate the risk of selecting a model which may be performing good only on some part of the dataset

Notes:

Vendor supplied data

Publisher Number:

2025-01-5024

Access Restriction:

Restricted for use by site license