Sureka Sivavelu, Venkatesh Palanisamy^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3469-3487, 2024, DOI:10.32604/cmc.2024.047407

Abstract The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric… More >

Xin Fan^1,2, Shuqing Zhang^1,2,*, Kaisheng Wu^1,2, Wei Zheng^1,2, Yu Ge^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187

Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique (SMOTE)… More >

Xin Fan^1,2,3, Jingen Mao^2,3,*, Liangjue Lian^2,3, Li Yu¹, Wei Zheng^2,3, Yun Ge^2,3

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 65-84, 2024, DOI:10.32604/cmc.2023.045522

Abstract The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor. In previous software defect prediction studies, transfer learning was effective in solving the problem of inconsistent project data distribution. However, target projects often lack sufficient data, which affects the performance of the transfer learning model. In addition, the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model. To address these problems, this article propose a software defect prediction method based on stable learning (SDP-SL) that combines code… More >

Ahmed Abdu¹, Zhengjun Zhai^1,2, Hakim A. Abdo³, Redhwan Algabri^4,*, Sungon Lee^5,*

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 >

Sagheer Abbas¹, Shabib Aftab^1,2, Muhammad Adnan Khan^3,4, Taher M. Ghazal^5,6, Hussam Al Hamadi⁷, Chan Yeob Yeun^8,*

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 >

J. Harikiran^1,*, 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 >

Yong Chen¹, Chao Xu^1,*, 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 >

Saleh Albahli^1,*, 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 >

Zuhaira Muhammad Zain^1,*, 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 >

Mohammad Sh. Daoud¹, Shabib Aftab^2,3, Munir Ahmad², Muhammad Adnan Khan^4,5,*, Ahmed Iqbal³, Sagheer Abbas², Muhammad Iqbal², Baha Ihnaini^6,7

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 >