1. SS-WDRN: sparrow search optimization-based weighted dual recurrent network for software fault prediction.
- Author
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Brundha Elci, J. and Nandagopalan, S.
- Subjects
COMPUTER software quality control ,OPTIMIZATION algorithms ,SYSTEMS software ,COMPUTER software ,SOFTWARE engineers ,SOFTWARE engineering - Abstract
Predicting software faults at the primary stage is a challenging role for software engineers and tech industries. During the development of software projects, it is necessary to predict the number of probable faults to have occurred on software rather than detecting whether the software modules are faulty or not. Discovering the number of expected faults helps software professionals to develop more reliable and high-quality software systems. However, the prediction model's performance gets affected while dealing with complicated software projects with increased cost factors such as time, effort, and resources. Therefore, to address the issue associated with handling complex software projects, a novel weighted dual cross-recurrent network-based levy sparrow search (WDCRN-LSS) model is proposed in this paper. The WDCRN-LSS approach by learning the data features with optimal hyperparameters accurately predicts the expected software faults in an earlier phase. Here, 17 PROMISE datasets containing 20 features each are utilized as input data for the proposed WDCRN-LSS model. The data inconsistencies are eliminated and then transformed to a suitable format for training through normalization, data transformation, and label encoding procedures. The preprocessed data are then trained using the proposed WDCRN-LSS model for the prediction of the expected number of software faults in the projects. With the excellent learning capability of feature representations, the proposed WDCRN-LSS model predicts software faults on upcoming/under-development software projects precisely. Thus, the proposed WDCRN-LSS model enhances software quality and minimizes cost factors such as time, resources, and effort that are depleted in developing software. The proposed WDCRN-LSS model's efficiency is investigated by utilizing evaluation measures namely error rate, precision, recall, F1-measure, the area under the curve, accuracy, and specificity. The experimental result manifests the efficiency of the proposed WDCRN-LSS model with a software fault detection accuracy of about 98.1%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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