Open Access

ARTICLE

Software Defect Prediction Based Ensemble Approach

J. Harikiran^1,*, B. Sai Chandana¹, B. Srinivasarao¹, B. Raviteja², Tatireddy Subba Reddy³

1 School of Computer Science and Engineering, VIT-AP University, Amaravathi, 522237, India
2 Department of Computer Science and Engineering, GITAM Deemed to be University, Telangana, 502329, India
3 Department of Computer Science and Engineering, B. V. Raju Institute of Technology Narsapur, Medak, Telangana, 502313, India

* Corresponding Author: J. Harikiran. Email:

Computer Systems Science and Engineering 2023, 45(3), 2313-2331. https://doi.org/10.32604/csse.2023.029689

Received 09 March 2022; Accepted 08 June 2022; Issue published 21 December 2022

Abstract

Software systems have grown significantly and in complexity. As a result of these qualities, preventing software faults is extremely difficult. Software defect prediction (SDP) can assist developers in finding potential bugs and reducing maintenance costs. When it comes to lowering software costs and assuring software quality, SDP plays a critical role in software development. As a result, automatically forecasting the number of errors in software modules is important, and it may assist developers in allocating limited resources more efficiently. Several methods for detecting and addressing such flaws at a low cost have been offered. These approaches, on the other hand, need to be significantly improved in terms of performance. Therefore in this paper, two deep learning (DL) models Multilayer preceptor (MLP) and deep neural network (DNN) are proposed. The proposed approaches combine the newly established Whale optimization algorithm (WOA) with the complementary Firefly algorithm (FA) to establish the emphasized metaheuristic search EMWS algorithm, which selects fewer but closely related representative features. To find the best-implemented classifier in terms of prediction achievement measurement factor, classifiers were applied to five PROMISE repository datasets. When compared to existing methods, the proposed technique for SDP outperforms, with 0.91% for the JM1 dataset, 0.98% accuracy for the KC2 dataset, 0.91% accuracy for the PC1 dataset, 0.93% accuracy for the MC2 dataset, and 0.92% accuracy for KC3.

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Keywords

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