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Light Gradient Boosting Machine in Software Defect Prediction: Concurrent Feature Selection and Hyper Parameter Tuning

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Intelligent Sustainable Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 458))

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Abstract

Predicting software defects is critical for ensuring software quality. Many supervised learning approaches have been used to detect defect-prone instances in recent years. However, the efficacy of these supervised learning approaches is still inadequate, and more sophisticated techniques will be required to boost the effectiveness of defect prediction models. In this paper, we present a light gradient boosting methodology based on ensemble learning that uses simultaneous feature selection (Recursive Feature Elimination (RFE)) and hyperparameter tuning (Random search). Our proposed technique LGBM + Randomsearch + RFE method is evaluated using the AEEEM dataset, including Apache Lucene, Eclipse JDT Core, Equinox, Mylyn, and Eclipse PDE UI. The experimental findings demonstrate that the proposed approach outperforms LGBM + Randomsearch, LGBM, and the top classical machine learning algorithms on all performance criteria considered.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, 532201, India

    Suresh Kumar Pemmada

  2. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha, 757003, India

    Janmenjoy Nayak

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

    Suresh Kumar Pemmada & H. S. Behera

  4. Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

Authors
  1. Suresh Kumar Pemmada
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  2. Janmenjoy Nayak
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  3. H. S. Behera
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  4. Danilo Pelusi
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Corresponding author

Correspondence to Suresh Kumar Pemmada .

Editor information

Editors and Affiliations

  1. Gnanmani College of Engineering and Technology, Namakkal, India

    Jennifer S. Raj

  2. Department of Computer Science, Kennesaw State University, Kennesaw, GA, USA

    Yong Shi

  3. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

  4. Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Pemmada, S.K., Nayak, J., Behera, H.S., Pelusi, D. (2022). Light Gradient Boosting Machine in Software Defect Prediction: Concurrent Feature Selection and Hyper Parameter Tuning. In: Raj, J.S., Shi, Y., Pelusi, D., Balas, V.E. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-19-2894-9_32

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