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  • Open Access


    Graph-Based Feature Learning for Cross-Project Software Defect Prediction

    Ahmed Abdu1, Zhengjun Zhai1,2, Hakim A. Abdo3, Redhwan Algabri4,*, Sungon Lee5,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 161-180, 2023, DOI:10.32604/cmc.2023.043680

    Abstract Cross-project software defect prediction (CPDP) aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects. The existing CPDP approaches rely on static metrics or dynamic syntactic features, which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties, such as complex design patterns, relationships between multiple functions, and dependencies in different software projects, that are important for CPDP. This paper introduces a novel approach, a graph-based feature learning model for CPDP (GB-CPDP), that utilizes NetworkX to extract features and learn representations of program entities… More >

  • Open Access


    Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

    Sagheer Abbas1, Shabib Aftab1,2, Muhammad Adnan Khan3,4, Taher M. Ghazal5,6, Hussam Al Hamadi7, Chan Yeob Yeun8,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6083-6100, 2023, DOI:10.32604/cmc.2023.037933

    Abstract The software engineering field has long focused on creating high-quality software despite limited resources. Detecting defects before the testing stage of software development can enable quality assurance engineers to concentrate on problematic modules rather than all the modules. This approach can enhance the quality of the final product while lowering development costs. Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team. This process is known as software defect prediction, and it can improve end-product quality while reducing the cost… More >

  • Open Access


    Software Defect Prediction Based Ensemble Approach

    J. Harikiran1,*, B. Sai Chandana1, B. Srinivasarao1, B. Raviteja2, Tatireddy Subba Reddy3

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2313-2331, 2023, DOI:10.32604/csse.2023.029689

    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,… More >

  • Open Access


    Compiler IR-Based Program Encoding Method for Software Defect Prediction

    Yong Chen1, Chao Xu1,*, Jing Selena He2, Sheng Xiao3, Fanfan Shen1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5251-5272, 2022, DOI:10.32604/cmc.2022.026750

    Abstract With the continuous expansion of software applications, people's requirements for software quality are increasing. Software defect prediction is an important technology to improve software quality. It often encodes the software into several features and applies the machine learning method to build defect prediction classifiers, which can estimate the software areas is clean or buggy. However, the current encoding methods are mainly based on the traditional manual features or the AST of source code. Traditional manual features are difficult to reflect the deep semantics of programs, and there is a lot of noise information in AST, which affects the expression of… More >

  • Open Access


    Defect Prediction Using Akaike and Bayesian Information Criterion

    Saleh Albahli1,*, Ghulam Nabi Ahmad Hassan Yar2

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1117-1127, 2022, DOI:10.32604/csse.2022.021750

    Abstract Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To… More >

  • Open Access


    Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

    Zuhaira Muhammad Zain1,*, Sapiah Sakri1, Nurul Halimatul Asmak Ismail2, Reza M. Parizi3

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1521-1546, 2022, DOI:10.32604/cmc.2022.022085

    Abstract Developing successful software with no defects is one of the main goals of software projects. In order to provide a software project with the anticipated software quality, the prediction of software defects plays a vital role. Machine learning, and particularly deep learning, have been advocated for predicting software defects, however both suffer from inadequate accuracy, overfitting, and complicated structure. In this paper, we aim to address such issues in predicting software defects. We propose a novel structure of 1-Dimensional Convolutional Neural Network (1D-CNN), a deep learning architecture to extract useful knowledge, identifying and modelling the knowledge in the data sequence,… More >

  • Open Access


    Machine Learning Empowered Software Defect Prediction System

    Mohammad Sh. Daoud1, Shabib Aftab2,3, Munir Ahmad2, Muhammad Adnan Khan4,5,*, Ahmed Iqbal3, Sagheer Abbas2, Muhammad Iqbal2, Baha Ihnaini6,7

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1287-1300, 2022, DOI:10.32604/iasc.2022.020362

    Abstract Production of high-quality software at lower cost has always been the main concern of developers. However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible. This issue can be resolved by identifying defects at the early stages of the development lifecycle. As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality. An artificial neural network is considered as one of… More >

  • Open Access


    Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction

    Bushra Mumtaz1, Summrina Kanwal2,*, Sultan Alamri2, Faiza Khan1

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 669-684, 2021, DOI:10.32604/iasc.2021.018405

    Abstract Software Defect Prediction (SDP) is a dynamic research field in the software industry. A quality software product results in customer satisfaction. However, the higher the number of user requirements, the more complex will be the software, with a correspondingly higher probability of failure. SDP is a challenging task requiring smart algorithms that can estimate the quality of a software component before it is handed over to the end-user. In this paper, we propose a hybrid approach to address this particular issue. Our approach combines the feature selection capability of the Optimized Artificial Immune Networks (Opt-aiNet) algorithm with benchmark machine-learning classifiers… More >

  • Open Access


    Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

    Faseeha Matloob1, Shabib Aftab1,2, Munir Ahmad2, Muhammad Adnan Khan3,*, Areej Fatima4, Muhammad Iqbal2, Wesam Mohsen Alruwaili5, Nouh Sabri Elmitwally5,6

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 403-421, 2021, DOI:10.32604/iasc.2021.017562

    Abstract Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are association, estimation, clustering, classification, and… More >

  • Open Access


    Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

    Kun Zhu1, Nana Zhang1, Qing Zhang2, Shi Ying1, *, Xu Wang3

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1467-1486, 2020, DOI:10.32604/cmc.2020.011415

    Abstract Software defect prediction plays a very important role in software quality assurance, which aims to inspect as many potentially defect-prone software modules as possible. However, the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features. In addition, software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques. To address these two issues, we propose the following two solutions in this paper: (1) We leverage a novel non-linear manifold learning method - SOINN Landmark Isomap (SLIsomap) to extract the… More >

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