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Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature

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Abstract

To ensure software quality, software defect prediction plays a prominent role for the software developers and practitioners. Software defect prediction can assist us with distinguishing software defect modules and enhance the software quality. In present days, many supervised machine learning algorithms have proved their efficacy to identify defective modules. However, those are limited to prove their major significance due to the limitations such as the adaptation of parameters with the environment and complexity. So, it is important to develop a key methodology to improve the efficiency of the prediction module. In this paper, an ensemble learning technique called Bootstrap aggregating has been proposed for software defect prediction object-oriented modules. The proposed method's accuracy, recall, precision, F-measure, and AUC-ROC efficiency were compared to those of many qualified machine learning algorithms. Simulation results and performance comparison are evident that the proposed method outperformed well compared to other approaches.

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Authors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, India

    P. Suresh Kumar & H. S. Behera

  2. Department of CSE, Aditya Institute of Technology and Management (AITAM), Tekkali, AP, 532201, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, 768018, India

    Bighnaraj Naik

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  1. P. Suresh Kumar
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Correspondence to P. Suresh Kumar.

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The authors declare that this manuscript has no conflict of interest with any other published source and has not been published previously (partly or in full). No data have been fabricated or manipulated to support our conclusions.

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Suresh Kumar, P., Behera, H.S., Nayak, J. et al. Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature. Innovations Syst Softw Eng 17, 355–379 (2021). https://doi.org/10.1007/s11334-021-00399-2

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