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A Package Level Regression Test Selection Approach for Java Software Systems

IEEE Access 2025年 13卷 87848-87861页

作者： Al-Refai, Mohammed Hammad, Mahmoud M. Jordan University of Science and Technology Faculty of Computer and Information Technology Computer Science Department Irbid22110 Jordan Faculty of Engineering and Information Technology Ajman United Arab Emirates Jordan University of Science and Technology Faculty of Computer and Information Technology Software Engineering Department Irbid22110 Jordan

Regression testing of software systems is an important and critical activity yet expensive and resource-intensive. An approach to enhance its efficiency is Regression Test Selection (RTS), which selectively re-executes a subset of relevant tests that are impacted by code modifications. Previous studies on static and dynamic RTS for Java software have shown that selecting tests at the class level is more effective than using finer granularities like methods or statements. Nevertheless, RTS at the package level, which is a coarser granularity than class level, has not been thoroughly investigated or evaluated for Java projects. To address this gap, we propose PKRTS, a static package-level RTS approach that utilizes the structural dependencies of the software system under test to construct a package-level dependency graph. PKRTS analyzes dependencies in the graph and identifies relevant tests that can reach modified packages, i.e., packages containing altered classes. In contrast to conventional static RTS techniques, PKRTS implicitly considers dynamic dependencies, such as Java reflection and virtual method calls, among classes belonging to the same package by treating all those classes as a single cohesive node in the dependency graph. We evaluated PKRTS on 885 revisions of 9 open-source Java projects, with its performance compared to Ekstazi, a state-of-the-art dynamic class-level approach, and STARTS, a state-of-the-art static class-level approach. We used Ekstazi as the baseline to measure the safety and precision violations of PKRTS and STARTS. The results indicated that PKRTS outperformed static class-level RTS in terms of safety violation, which measures the extent to which relevant test cases are missed. PKRTS showed an average safety violation of 2.29% compared to 5.94% safety violation of STARTS. Despite this, PKRTS demonstrated lower precision violation and lower reduction in test suite size than class-level RTS, as it selects higher number of irrelevant te

关键词： Java programming language

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Predicting Diabetes Disease Occurrence Using Logistic Regression: An Early Detection Approach

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第1期5卷 160-167页

作者： Abdalrada, Ahmad Shaker Neamah, Ali Fahem Murad, Hayder Department of Software Faculty of Computer Science and Information Technology Wasit University Iraq Department of Computer Faculty of Computer Science and Information Technology Wasit University Iraq College of Medicine Wasit University Iraq

Diabetes disease is prevalent worldwide, and predicting its progression is crucial. Several model have been proposed to predict such disease. Those models only determine the disease label, leaving the likelihood of developing the disease unclear. Proposing a model for predicting the progression of disease becomes essential. Therefore, this article proposes a logistic regression model to anticipate the likelihood of Diabetes syndrome incidence. The model exploit capabilities of logistic regression by using sigmoid function. The model's performance was evaluated using the Pima Indians Diabetes dataset and demonstrated high accuracy, sensitivity, and specificity. The prediction accuracy rate was 77.6%, with a sensitivity of 72.4%, specificity of 79.6%, Type I Error of 27.6%, and Type II Error of 20.4%. Furthermore, the model indicates the feasibility of using laboratory tests, such as Pregnancies, Glucose, Blood Pressure, BMI, and DiabetesPedigreeFunction, to predict disease progress. The proposed model can aid patients and physicians in understanding the disease's progression and implementing timely interventions © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Forecasting

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Tackling the Berth Allocation Problem via Harmony Search Algorithm

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 501-512页

作者： Ahmed, Bilal Hamdan, Hazlina Muhammed, Abdullah Husin, Nor Azura Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 UPM Malaysia Department of Communication Technology and Networks Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 UPM Malaysia

Berth Allocation Problem (BAP) is a renowned difficult combinatorial optimization problem that plays a crucial role in maritime transportation systems. BAP is categorized as non-deterministic polynomial-time hard (NP-hard) problems, that is very tough to resolve for optimality within an acceptable timeframe. Many metaheuristic algorithms have been suggested to tackle this problem, and yet, most of these algorithms have some drawbacks such as they have a weak ability to explore the solution space (they struggle escaping from local minima) and they face the difficulties to operate on different datasets. Consequently, the need to either enhance the existing algorithms or utilize a new algorithm is still necessary. Harmony Search Algorithm (HSA) is one of the recent population-based optimization methods which inspired by modern-nature. HSA has confirmed its ability to tackle various difficult combinatorial optimization problems like vehicle routing, exam timetabling to name a few. However, as far as we are concerned, it has never been applied to tackle the BAP problem. The primary objective of this article is to examine the effectiveness of HSA in solving BAP by identifying suitable values for the parameters of the HSA and then applying HSA to tackle BAP. Therefore, in this article, the basic HSA is proposed to tackle the BAP. The suggested HSA is tested on BAP benchmark (I3 dataset) and compared the results with other latest algorithms found in the literature. The trial outcomes evidenced that the HSA is promising, competitive, and that it has surpassed some other algorithms that have solved the same dataset, and the results were very near to the best-known results. Experimental results also prove the suitability and applicability of HSA in tackling the BAP. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Combinatorial optimization

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Optimizing Performance in Wireless Sensor Networks through a Multi-Objective Rendezvous Points Selection Algorithm

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第18期18卷 167-183页

作者： Samara, Ghassan Alzyoud, Wafaa Aljaidi, Mohammad Alazaidah, Raed Qasem, Mais Haj Al Daoud, Essam Faculty of Information Technology Computer Science Department Zarqa University Zarqa Jordan

Wireless sensor networks (WSNs) play a vital role in modern research and applications due to their potential to gather data from various environments. Because sensor nodes (SNs) within WSNs have limited battery life, those in close proximity to the sink often experience rapid power depletion, leading to the emergence of hotspot issues. To address this, the concept of a mobile sink (MS) has emerged as a potential solution, effectively mitigating power usage in SNs and thereby extending the network’s overall lifespan. Furthermore, many sensor-based applications necessitate specific data collection timeframes, underscoring the necessity of effective strategies. Leveraging rendezvous points (RPs) to enhance network efficiency becomes imperative in enabling the MS to efficiently collect data from all SNs within designated time periods. A sophisticated cost function is employed to strategically determine RPs, considering multiple factors that influence the efficacy of each RP. This process culminates in the selection of RPs, optimizing for the longest path with minimal delays. Through the proposed hybrid mobile vehicle (HMV) method, compared against the prevailing MOOVor method, significant enhancements are observed in terms of sensor coverage and reduced hop count within the network. © 2024 by the authors of this article.

关键词： Sensor nodes

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Feature Selection Techniques for Enhancing App User Review Analysis

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IEEE Access 2025年 13卷 85279-85293页

作者： Salleh, Amran Osman, Mohd Hafeez Hassan, Sa'Adah Said, Mar Yah Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang43400 Malaysia

The rapid growth of user-generated content, particularly app user reviews, presents a significant challenge in analyzing and extracting useful insights. The unstructured nature, inconsistent quality, and large volume of these reviews make it difficult to identify relevant information for app maintenance and updates. This study addresses this challenge by evaluating the impact of different feature selection techniques on the performance of machine learning models in multi-label classification tasks for app user review analysis. Our findings indicate that the subset attributes derived from a combination of information Gain, GINI Index, and Correlation Matrix can improve the performance of machine learning models. Using a Support Vector Machine with proposed innovative score-based zero shot technique, promising results were achieved on average with 96.75% precision, 69.39% recall, and 80.81% F1-Score. Additionally, high-quality features, such as the percentage Difference can enhance performance in multi-label classification tasks, providing valuable insights for practitioners and researchers. The research implications and significance highlight the practical applications and strategic value of these findings, contributing to the advancement of knowledge and practice in the field of software engineering, particularly for multi-label classification tasks. © 2013 IEEE.

关键词： Adversarial machine learning

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Understanding technology Acceptance and Use in Social Media Platforms:A Systematic Literature Review and the Development of Research Framework

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Journal of Social Computing 2024年第3期5卷 261-291页

作者： Muhammad Amirul Asyraaf Roslan Nurul Amelina Nasharuddin Masrah Azrifah Azmi Murad the Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSerdang 43400Malaysia

The Systematic Literature Review(SLR)presented in the document focuses on the acceptance of social media(SM)platforms,particularly through the lens of the Unified Theory of Acceptance and Use of technology(UTAUT),technology Acceptance Model(TAM),Theory of Planned Behavior(TPB)and other relevant *** review involves a comprehensive analysis of the most used theories of acceptance models,the methods for selecting studies,and the results and findings from the reviewed *** study also discusses the accomplishment of research goals and creates a research framework to guide future *** geographic scope of the studies is also examined,providing insights into the distribution of studies across different *** review adheres to the PRISMA(Preferred Reporting Items for Systematic Reviews and meta-Analyses)guidelines for reporting systematic reviews and employs a theory-based approach,categorizing selected articles based on their theoretical *** review’s methodology involves the use of empirical evidence that meets predetermined inclusion criteria and addresses specific research *** document provides a detailed account of the steps involved in the systematic review,including the identification,screening,eligibility determination,and selection of *** review also offers implications for theory and practice,addresses limitations,and suggests areas for future research.

关键词： technology acceptance model(TAM) unified theory of acceptance and use of technology(UTAUT) theory of planned behavior(TPB) social media social networking services

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VSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain

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ACM Transactions on Sensor Networks 2024年第2期20卷 1-22页

作者： Tian, Siben Bai, Fenhua Shen, Tao Zhang, Chi Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Applications China Faculty of Information Technology Beijing University of Technology China

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm's complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

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Metaheuristic task offloading approaches for minimization of energy consumption on edge computing: a systematic review

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Discover Internet of Things 2024年第1期4卷 1-30页

作者： Latip, Rohaya Aminu, Jafar Hanafi, Zurina Mohd Kamarudin, Shafinah Gabi, Danlami Faculty of Computer Science and Information Technology Universiti Putra Serdang Malaysia Department of Computer Science Kebbi State University of Science and Technology Aleiro Aleiro Nigeria

A systematic review of metaheuristic Algorithms for energy-efficient task offloading in edge computing is provided. The Grey Wolf Optimizer (GWO) is explored as a promising approach for reducing energy consumption. Future research focuses on hybrid metaheuristic algorithms, machine learning integration, and real-time task offloading © The Author(s) 2024.

关键词： Adversarial machine learning

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Enhancing Hybrid Classification for Plant Diseases With Deep Feature Selection Based on Analytical Entropy and Statistical Method

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IEEE Access 2025年 13卷 86781-86798页

作者： Phiphatkamtorn, Phanthawat Jitanan, Sutasinee Naresuan University Faculty of Science Department of Computer Science and Information Technology Phitsanulok65000 Thailand

A significant portion of research in the hybrid classification area aims to reduce the number of deep features. However, many approaches insufficiently address the relationships between deep features and specific classes or diseases. This study aims to use information entropy to distinguish between valuable and less valuable features in the extracted deep feature set. The architecture of state-of-the-art models influences the characteristics of deep features, which can be categorized into three types: negative, zero, and positive values. Accurate predictions in typical machine learning models entail identifying valuable features, especially those with the most positive values. We perform one-way analysis of variance (ANOVA) and mutual information (MI) analysis to verify the correlation between the remaining deep features and specific classes or diseases. We discard features with weak relations based on predefined thresholds established by the involved f-statistic and MI mean values. This enhanced hybrid classification approach uses the entropy mean threshold, skewness visualization, the standard deviation, and the mean probability of valuable feature threshold to select valuable features and remove less valuable features, as well as one-way ANOVA and MI to evaluate relations and eliminate weakly related deep features. The results of all experiments show that the proposed approach exhibits higher accuracy than traditional hybrid classification and remains effective across all plant species. For tomatoes, ResNet-34 is the best model, achieving an excellent accuracy of 99.94% with 98 features. For sugarcane, ResNet-34 achieves a remarkable accuracy of 99.41% with 121 features. For corn, EfficientNet-B4 attains a significant accuracy of 99.77% with 123 features. © 2025 IEEE.

关键词： Analysis of variance (ANOVA)

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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