Comprehensive Study on Machine Learning Techniques for Software Bug Prediction

Khleel, Nasraldeen Alnor Adam; Nehéz, Károly

doi:10.14569/ijacsa.2021.0120884

Cited by 9 publications

(5 citation statements)

References 32 publications

(49 reference statements)

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“…It offers a variety of drawing functions and customization options that allow users to create high-quality graphs, charts, histograms, scatter plots, and more. With Matplotlib, users can easily visualize data from multiple sources, identify trends, and deliver recommendations [14]. The library offers a MATLABlike interface for speed and simplicity, making it easy for both beginners and experienced users to use.…”

Section: Matplotlibmentioning

confidence: 99%

Empowering Security with Machine Learning for Ransomware and Malware Detection

Ravi1,

2024

IJSREM

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Due to the increase and frequency of network attacks, network security protection needs to be established[1]. The project focuses on using machine learning technology to detect ransomware and malware and aims to improve computer security. This work involves using advanced algorithms and models to analyze different types of data to identify patterns associated with crime. The proposed system extracts relevant features from various data points, including archive behavior, network traffic, and physical interactions. These features are used to train machine learning models to distinguish between good behavior and bad behavior. By constantly learning and updating, the model improves accuracy over time and stays ahead of evolving ransomware and malware threats. This project aims to contribute to the field of cybersecurity by providing efficient and effective methods to detect and mitigate threats. The use of machine learning-based search engines should improve defense against cyber attacks, ultimately protecting sensitive data and ensuring the integrity of computer systems[2]. This research aligns with the growing need for cybersecurity in response to new solutions to combat the evolution of malware. Key Words: Cybersecurity, Ransomware detection, Malware detection, Machine learning techniques, Advanced algorithms, Data analysis, Feature extraction, Model training, Continuous learning, Threat detection, Defence mechanisms

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Section: Matplotlibmentioning

confidence: 99%

Empowering Security with Machine Learning for Ransomware and Malware Detection

Ravi1,

2024

IJSREM

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“…This noteworthy enhancement underscores the potential of this innovative DL-based approach in accurately and efficiently classifying bug severity levels, offering valuable insights for bug management and software quality improvement across diverse projects. Khleel and Nehéz [18] presented a model for SBP using four ML algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…It provides valuable insights into prediction accuracy and guides improvements [23]. A confusion matrix is a tabular representation in binary classification, summarizing model predictions and comparing them to actual labels [18]. It consists of four components: true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) [23], [28].…”

Section: Models Construction and Evaluationmentioning

confidence: 99%

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Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Khleel,

Nehéz

2023

IJEECS

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<span>The process of identifying software bugs is of paramount importance as it ensures software reliability and facilitates maintenance activities. The quality improvement process of software relies heavily on software bug prediction (SBP). In SBP, the task of accurately identifying defective source code poses a significant challenge. Numerous of machine learning (ML) models has been developed specifically to address this challenge in SBP. Nonetheless, the class imbalance issue restricts the potential of these models to predict software bugs accurately. This issue poses a significant hindrance to the efficiency of these models, leading to imbalanced false-positive and false-negative outcomes. Previous studies have paid limited attention to addressing the challenge of class imbalance in SBP. This study aims to fill this research gap by employing a combination of two recurrent neural networks (RNNs), namely <a name="_Hlk141433135"></a>long-short-term memory (LSTM) and gated recurrent unit (GRU), along with an undersampling method (near miss) to effectively tackle this issue. Experiments have been conducted on publicly available benchmark datasets, considering both class-level and file-level metrics. The experimental results lead to the conclusion that our models outperform others and the combination of RNNs models with undersampling methods leads to improved bug prediction performance, particularly for datasets with imbalanced class distributions.</span>

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“…There are several case studies that have been done on object-oriented software projects to determine empirical thresholds for metrics. Several static analysis tools and code restructuring methods have been developed to discover and solve source code problems, and these tools and methods provide various ways of analyzing source codes [4]. Previous studies have classified code smells into three main categories: application, class , and method level smells [5].…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network model for bad code smells detection based on oversampling method

Khleel

Nehéz²

2022

IJEECS

Self Cite

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Code <span>smells refers to any symptoms or anomalies in the source code that shows violation of design principles or implementation. Early detection of bad code smells improves software quality. Nowadays several artificial neural network (ANN) models have been used for different topics in software engineering: software defect prediction, software vulnerability detection, and code clone detection. It is not necessary to know the source of the data when using ANN models but require large training sets. Data imbalance is the main challenge of artificial intelligence techniques in detecting the code smells. To overcome these challenges, the objective of this study is to presents deep convolutional neural network (D-CNN) model with synthetic minority over-sampling technique (SMOTE) to detect bad code smells based on a set of Java projects. We considered four code-smell datasets which are God class, data class, feature envy and long method and the results were compared based on different performance measures. Experimental results show that the proposed model with oversampling techniques can provide better performance for code smells detection and prediction results can be further improved when the model is trained with more datasets. Moreover, more epochs and hidden layers help increase the accuracy of the model.</span>

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Comprehensive Study on Machine Learning Techniques for Software Bug Prediction

Cited by 9 publications

References 32 publications

Empowering Security with Machine Learning for Ransomware and Malware Detection

Empowering Security with Machine Learning for Ransomware and Malware Detection

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Deep convolutional neural network model for bad code smells detection based on oversampling method

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