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Improving Windows Malware Detection Using the Random Forest Algorithm and Multi-View Analysis Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-04-13 S. Syed Suhaila, K. Sundara Krishnan
Cybercriminals motivated by malign purpose and financial gain are rapidly developing new variants of sophisticated malware using automated tools, and most of these malware target Windows operating systems. This serious threat demands efficient techniques to analyze and detect zero-day, polymorphic and metamorphic malware. This paper introduces two frameworks for Windows malware detection using random
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An Imperfect Debugging Non-Homogeneous Poisson Process Software Reliability Model Based on a 3-Parameter S-Shaped Function Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-04-01 Nguyen Hung-Cuong, Huynh Quyet-Thang
Considering the testing process of the software system as a stochastic process is a primary approach to the software reliability modeling technique. Besides some popular distributions, the Poisson distribution has been considered the best based on its advantage when modeling the times at which arrivals enter a system. In the non-homogeneous Poisson process group of models, the S-shaped function is
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Dialogue Generation Model with Hierarchical Encoding and Semantic Segmentation of Dialogue Context Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-03-23 Xiao Wei, Yidian Lin, Qitao Hu
Dialogue generation, as a crucial subtask of dialogue systems, is garnering increasing attention in the field of Natural Language Processing (NLP). The success of dialogue generation relies on effectively utilizing context information to ensure coherent and diverse responses. However, current approaches heavily rely on external sources rather than leveraging the inherent dialogue content. We propose
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Multi-Intent Inline Code Comment Generation via Large Language Model Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-03-23 Xiaowei Zhang, Zhifei Chen, Yulu Cao, Lin Chen, Yuming Zhou
Code comment generation typically refers to the process of generating concise natural language descriptions for a piece of code, which facilitates program comprehension activities. Inline code comments, as a part of code comments, are also crucial for program comprehension. Recently, the emergence of large language models (LLMs) has significantly boosted the performance of natural language processing
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Modeling and Control of Drinking Water Supply Infrastructures Through Multi-Agent Systems for Sustainability Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-03-19 Carlos Calatayud Asensi, José Vicente Berná Martínez, Lucía Arnau Muñoz, Francisco Maciá Pérez
Traditionally, drinking water supply infrastructures have been designed to store as much water as possible and to do so during the energy cheap hours. This approach is unsustainable today. The use of digital systems capable of modeling the behavior of infrastructures and the creation of intelligent control systems can help to make drinking water supply systems more efficient and effective, while still
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Sentiment-Time Heterogeneous Residual Graph Attention Transformer for Session-Based Recommendation Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-03-19 Jun Wang, Shuang Zhang
Session-Based Recommendation (SBR) systems are facing considerable challenges, with their primary objective being to implement precise recommendations based on users’ historical behavior sequences. Graph Neural Networks (GNNs) have emerged as powerful tools for processing graph-structured data in recommendation systems. Although recent research has advanced in this area, a significant gap remains in
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A Novel Entity and Relation Joint Interaction Learning Approach for Entity Alignment Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-03-19 Di Wu, Tong Li, Yiran Zhao, Junrui Liu, Zifang Tang, Zhen Yang
Entity alignment (EA) aims to find equivalent entities in knowledge graphs (KGs) from multiple data sources and is a crucial step in integrating KGs. Recent studies learn the similarity of entity embeddings by aggregating neighboring entities. However, these methods solely compare neighboring entities and do not incorporate the connected relation between an entity and its neighbors. In this paper,
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Applying Scrum to Knowledge Transfer Among Software Developers Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-02-22 Fernando Ibarra-Torres, Matias Urbieta, Nuria Medina-Medina
In the teaching-learning processes, research and continuous innovation are encouraged. Now, when talking about innovation, the concept of adapting methodologies that have been successfully applied to speed up and increase the quality of software projects begins to emerge, is the case of agile software development methodologies. Scrum is one of the most widely used methodologies in the software development
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Toward Pointer-Analysis-Based Vulnerability Discovery in Human–Machine Pair Programming Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-02-22 Pingyan Wang, Shaoying Liu
Pointer analysis is the underlying technique of many static analysis tools for vulnerability discovery. It has proved to be effective in identifying a variety of vulnerabilities, such as buffer overflow vulnerabilities and injection vulnerabilities. However, most existing pointer analysis approaches require whole-program availability, i.e. the program to be analyzed should be complete, which may hinder
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EFSP: An Enhanced Full Scrum Process Model Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-02-01 Naglaa A. Eldanasory, Amira M. Idrees, Engy Yehia
Scrum has emerged as the most widely used and desired Agile approach for providing corporate strategic competency by establishing a solid foundation for project management. However, there are several issues confronted during its implementation. Some researchers tried to solve specific areas of Scrum issues except only research that covers several aspects without resolving all of them. So, this study
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Exploring the Impact of Vocabulary Techniques on Code Completion: A Comparative Approach Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2024-01-13 Yasir Hussain, Zhiqiu Huang, Yu Zhou, Izhar Ahmed Khan
Integrated Development Environments (IDEs) are pivotal in enhancing productivity with features like code completion in modern software development. Recent advancements in Natural Language Processing (NLP) have empowered neural language models for code completion. In this study, we present an extensive investigation of the impact of open and closed vocabulary systems on the task of code completion.
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MOID: Many-to-One Patent Graph Embedding Base Infringement Detection Model Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-12-28 Weidong Liu, Fei Li, Senjun Pei, Chunming Cheng
With the increasing number of patent applications over the years, instances of patent infringement cases have become more frequent. However, traditional manual patent infringement detection models are no longer suitable for large-scale infringement detection. Existing automated models mainly focus on detecting one-to-one patent infringements, but neglect the many-to-one scenarios. The many-to-one patent
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EFSM Model-Based Testing for Android Applications Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-12-20 Weiwei Wang, Junxia Guo, Beite Li, Ying Shang, Ruilian Zhao
Model-based testing provides an effective means for ensuring the quality of Android apps. Nevertheless, existing models that focus on event sequences and abstract them into Finite State Machines (FSMs) may lack precision and determinism because of the different data values of events that can result in various states of Android applications. To address this issue, a novel model based on Extended Finite
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Function-Level Code Obfuscation Detection Through Self-Attention-Guided Multi-Representation Fusion Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-12-11 Zhenzhou Tian, Ruikang He, Hongliang Zhao, Lingwei Chen
Malware developers often employ code obfuscation techniques to conceal their malicious functionality, making it challenging to detect and analyze such software. While various de-obfuscation techniques exist, the majority of them require prior knowledge of the obfuscation tools and techniques in use. Identifying the specific obfuscation tools or algorithms applied to the obfuscated code is thus of vital
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SCR-LIBM: A Correctly Rounded Elementary Function Library in Double-Precision Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-12-07 Yang Qu, Jinchen Xu, Bei Zhou, Jiangwei Hao, Fei Li, Zuoyan Zhang
The MPFR and CR-LIBM math libraries are frequently utilized due to their ability to generate correctly rounded results for all double-precision inputs. However, it is worth noting that MPFR has a slower average performance, while CR-LIBM achieves correct rounding over two iterations, rendering it less stable. In addition, CR-LIBM has a poor performance in handling the worst-case of correct rounding
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A Dual Decision-Making Continuous Reinforcement Learning Method Based on Sim2Real Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-11-22 Wenwen Xiao, Xinzhi Wang, Xiangfeng Luo, Shaorong Xie
Continuous reinforcement learning carries potential security risks when applied in real-world scenarios, which could have significant societal implications. While its field of application is expanding, the majority of applications still remain confined to virtual environments. If only a single continuous learning method is applied to an unmanned system, it will still forget previously learned experiences
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OdegVul: An Approach for Statement-Level Defect Prediction Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-11-20 Guoqiang Yin, Wei Wang, Haiyan Li
Defect prediction research has been conducted for more than 40 years, with the goal of estimating the defect-prone blocks of source code. Prior studies, however, had two major limitations: (1) coarse-grained defect prediction results and (2) weak long-term dependencies modeling. As a result, developers need to review the prediction results to figure out which function or even which line of code produced
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Review and Application of Knowledge Graph in Crisis Management Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-11-18 Xinzhi Wang, Mengyue Li, Weiwang Chen, Yige Yao, Zhennan Li, Yi Liu, Hui Zhang
In the contemporary social environment, social crisis events occur frequently with significant impacts. Effective management of these events requires comprehensive group intention mining, which encompasses intention detection and intention attribution. Knowledge graph inference facilitates the detection of group intention in crisis events. This is supported by the construction of crisis knowledge graphs
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The Allocation Scheme of Software Development Budget with Minimal Conflict Attributes Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-11-08 Yanfang Ma, Wei Zhou
During the process of software development, a significant challenge revolves around accurately estimating the associated costs. The primary goal of project managers is to ensure the delivery of a highly trustworthiness product that aligns with the designated budgetary constraints. Nonetheless, the trustworthiness of software hinges upon a range of distinct attributes. When implementing a budget allocation
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An Empirical Study on Model-Agnostic Techniques for Source Code-Based Defect Prediction Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-11-04 Yi Zhu, Yuxiang Gao, Qiao Yu
Interpretation is important for adopting software defect prediction in practice. Model-agnostic techniques such as Local Interpretable Model-agnostic Explanation (LIME) can help practitioners understand the factors which contribute to the prediction. They are effective and useful for models constructed on tabular data with traditional features. However, when they are applied on source code-based models
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DTester: Diversity-Driven Test Case Generation for Web Applications Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-10-26 Shumei Wu, Zexing Chang, Zhanwen Zhang, Zheng Li, Yong Liu
Search-based Test Case Generation (TCG) for web applications suffers from unstable performance and suboptimal test suite problems due to diversity loss. However, previous diversity metrics mainly only focus on client-side models or server-side code, which are prone to low robustness and poor generalization in practical applications. We propose a diversity-driven TCG method DTester, which can maximize
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ICG: A Machine Learning Benchmark Dataset and Baselines for Inline Code Comments Generation Task Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-10-20 Xiaowei Zhang, Lin Chen, Weiqin Zou, Yulu Cao, Hao Ren, Zhi Wang, Yanhui Li, Yuming Zhou
As a fundamental component of software documentation, code comments could help developers comprehend and maintain programs. Several datasets of method header comments have been proposed in previous studies for machine learning-based code comment generation. As part of code comments, inline code comments are also crucial for code understanding activities. However, unlike method header comments written
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GTFP: Network Fault Prediction Based on Graph and Time Series Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-10-18 Zhongliang Li, Junjun Ding, Zongming Ma
With the explosion of 5G network scale, the network structure becomes increasingly complex. During the operation of the network devices, the probability of anomalies or faults increases accordingly. Network faults may lead to the disappearance of important information and cause unpredictable losses. The prediction of network faults can enhance the quality of network services and reduce economic loss
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NNTBFV: Simplifying and Verifying Neural Networks Using Testing-Based Formal Verification Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-10-17 Haiyi Liu, Shaoying Liu, Guangquan Xu, Ai Liu, Dingbang Fang
Neural networks are extensively employed in safety-critical systems. However, these critical systems incorporating neural networks continue to pose risks due to the presence of adversarial examples. Although the security of neural networks can be enhanced by verification, verifying neural networks is an NP-hard problem, making the application of verification algorithms to large-scale neural networks
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Formalization and Verification of Enhanced Group Communication CoAP Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-10-12 Sini Chen, Ran Li, Huibiao Zhu
With the flourish of the Internet of Things (IoT), the group communication Constrained Application Protocol (CoAP) emerged at the historic moment, enabling homogeneous devices with constrained computing ability to communicate with ease. CoAP is widely used in transportation, health care and many other aspects. Hence, it is prominent to propose a flexible and efficient architecture for usage in such
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Unifying Behavioral and Feature Modeling for Testing of Software Product Lines Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-09-30 Fevzi Belli, Tugkan Tuglular, Ekincan Ufuktepe
Existing software product line (SPL) engineering testing approaches generally provide positive testing that validates the SPL’s functionality. Negative testing is commonly neglected. This research aims to unify behavioral and feature models of an SPL, enable testing before and after variability binding for domain-centric and product-centric testing, and combine positive and negative testing for a holistic
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A Business-Oriented Methodology to Evaluate the Security of Software Architecture Quantitatively Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-09-26 Hao Chen, Shengyang Zhou, Chen Chen, Zheng Dai, Bixin Li
Software architecture security design is a key stage in developing business-oriented system, such as business-critical system, ICT system and AI system. Many typical accidents also remind us that the security of software architecture even plays a more important role than the code security in most software systems. However, there are very few researches which focus on the security of software architecture
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EVaDe: Efficient and Lightweight Mirai Variants Detection via Approximate Largest Submatrix Search Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-25 Xuguo Wang, Ligeng Chen, Yuyang Wang, Hao Huang, Bing Mao
The Mirai botnet, notorious for launching significant Distributed Denial of Service (DDoS) attacks and crippling portions of internet services in late 2016, has emerged as a significant threat. Its threat is magnified by the open-source nature of the original Mirai code, which enables a propagation and evolution rate that surpasses traditional malware and frequently defies common sense. As the primary
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Enhancing Accessibility to Data in Data-Intensive Web Applications by Using Intelligent Web Prefetching Methodologies Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-23 Tolga Buyuktanir, I. Onur Sigirci, Mehmet S. Aktas
Data-intensive Web Applications built using client–server architectures usually provide prefetching mechanisms to enhance data accessibility. Prefetching is a strategy of retrieving data before it is requested so that it can be ready when the user requests it. Prefetching reduces the load on the web server by making data available before the user requests it. Prefetching can be used for static content
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NISe: Non-Invasive Secure Framework for Multi-Access Edge Computing Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-23 Xuguo Wang, Ligeng Chen, Yu Liang, Hao Huang
To address the emerging security challenges in Multi-Access Edge Computing (MEC), it is imperative that solutions go beyond the current infrastructure-centric measures. These methods, including authentication and access control, are insufficient to combat malware that conceals itself within ME applications. The acknowledged flaws in the ME application layer necessitate an immediate call for creative
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Nimbus++: Revisiting Efficient Function Signature Recovery with Depth Data Analysis Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-23 Ligeng Chen, Yi Qian, Yuyang Wang, Bing Mao
Function signature recovery is vital for many binary analysis tasks, led by control-flow integrity enhancement. To minimize human effort, existing works attempt to replace rule-based methods with learning-based methods. These works put a lot of work into improving the system’s performance, but this had the unintended consequence of increasing resource usage. However, recovering the function signature
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An Optimal WSN Coverage Based on Adapted Transit Search Algorithm Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-21 Thi-Kien Dao, Trong-The Nguyen, Truong-Giang Ngo, Trinh-Dong Nguyen
The wireless sensor network (WSN) coverage is one of the most significant impacts on the quality of service that directly determines the efficiency reality of applications. The distribution of sensor nodes in the WSN determines the size of the network monitoring coverage area, whether there is duplicate coverage, and monitoring blind regions. This study introduces an optimal coverage strategy for the
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An Empirical Study on GitHub Sponsor Mechanism Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-19 Ziyuan Zhang, Yiqian Yang, Haolan He, Jie Chen
From May 2019, GitHub launched sponsor mechanism indicating that GitHub is moving towards deeper integration of open source development and economic support. It will bring more comprehensive and diversified support to the open source community. However, the number of developers profiting from the sponsor mechanism follows a long tail distribution. Our study found that only 31% of developers who started
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An Approach Based on Machine Learning for the Cybersecurity of Blockchain-Based Smart Internet of Medical Things (IoMT) Networks Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-14 Mohammed Naif Alatawi
This paper presents a hybrid blockchain architecture for Internet of Medical Things (IoMT) systems, aiming to enhance their security and performance. The proposed approach combines artificial intelligence (AI) models with blockchain technology to create a safe and efficient healthcare system. The study focuses on addressing the challenges related to data storage, data management, real-time medical
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Boosting Just-In-Time Code Comment Updating Via Programming Context and Refactor Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-14 Xiangbo Mi, Jingxuan Zhang, Yixuan Tang, Yue Ju, Jinpeng Lan
Comments are summary descriptions of code snippets. When analyzing and maintaining programs, developers tend to read tidy comments rather than lengthy code. To prevent developers from misunderstanding the program or leading to potential bugs, ensuring the consistency and co-evolution of comments and the corresponding code is an integral development activity in practice. Nevertheless, when modifying
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An Adaptive Semantic Annotation Tool for Teachers Based on Context-Aware and Internet of Things Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-07 Aissa Bensattalah, Rachid Chalal, Fahima Nader
Annotation has demonstrated its importance in several areas, notably in the modeling of annotation activity in the automation and adaptation phase. However, the context sensor is commonly manual or semi-automatic. The use of the Internet of Things with annotation gives a qualitative leap in the field of higher education and universities. In this field, teachers, during their pedagogical activities
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Python API Misuse Mining and Classification Based on Hybrid Analysis and Attention Mechanism Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-08-07 Xincheng He, Xiaojin Liu, Lei Xu
APIs play a crucial role in contemporary software development, streamlining implementation and maintenance processes. However, improper API usage can result in significant issues such as unexpected outcomes, security vulnerabilities and system crashes. To detect API misuses, current methods primarily rely on comparing established API usage patterns with target points for automated detection, mainly
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A Combined Usage of NLP Libraries Towards Analyzing Software Documents Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-07-29 Xianglong Kong, Hangyi Zhuo, Zhechun Gu, Xinyun Cheng, Fan Zhang
Software documents are commonly processed by natural language processing (NLP) libraries to extract information. The libraries provide similar functional APIs to achieve NLP tasks, numerous toolkits result in a problem of selection. In this work, we propose a method to combine the strengths of different NLP libraries to avoid the subjective selection of a specific NLP library. The combined usage is
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Bug Localization with Features Crossing and Structured Semantic Information Matching Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-07-29 Guoqing Xu, Xingqi Wang, Dan Wei, Yanli Shao, Bin Chen
Bug localization techniques aim to locate the relevant buggy source files according to the bug described by the given bug report, so as to improve the localization efficiency of developers and reduce the cost of software maintenance. The traditional bug localization techniques based on Information Retrieval (IR) usually use the classical text retrieval model and combines the specific domain knowledge
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Authentication and Authorization Management in SOA with the Focus on RESTful Services Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-07-21 Arbër Beshiri
SOA is an architectural style that enables providing applications as services. Following the authentication procedure, most Web services-based applications use application-specific access control mechanisms to make authorization decisions. Services can interact with one another, sometimes relying on a trust-based relationship. However, if unauthorized access is gained to a particular service, it could
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Graph-Based Root Cause Localization in Microservice Systems with Protection Mechanisms Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-07-19 Wei Tian, Haitao Zhang, Neng Yang, Yepeng Zhang
Service anomalies are difficult to locate accurately due to their propagation through service dependencies in microservice systems. Besides, the protection mechanisms are introduced into the microservice systems to ensure the stable operation of services. However, the existing approaches ignore the impact of protection mechanisms on the root cause localization of abnormal services. Specifically, the
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Spectral Test Generation for Boolean Expressions Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-07-12 Tolga Ayav
This paper presents a novel method for testing Boolean expressions. It is based on spectral, aka Fourier analysis of Boolean functions which is exploited to generate test inputs. The approach has three important contributions: (i) It generates a relatively small test suite with a high capability of fault detection, (ii) The test suite is prioritized such that expected fault detection time is shorter
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Supervised Classification of UML Class Diagrams Based on F-KNB Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-07-10 Zhongchen Yuan, Zongmin Ma
Often most software development doesn’t start from scratch but applies previously developed artifacts. These reusable artifacts are involved in various phases of the software life cycle, ranging from requirements to maintenance. Software design as the high level of software development process has an important impact on the following stages, so its reuse is gaining more and more attention. Unified
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Cross-Project Transfer Learning on Lightweight Code Semantic Graphs for Defect Prediction Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-07-06 Dingbang Fang, Shaoying Liu, Yang Li
A deep learning system (DLS) developed based on one software project for defect prediction may well be applied to the related code on the same project but is usually difficult to be applied to new or unknown software projects. To address this problem, we propose a Transferable Graph Convolutional Neural Network (TGCNN) that can learn defects from the lightweight semantic graphs of code and transfer
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A Case Study of Software Project Replacement: A Time Series Analysis Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-06-30 Alexandre L’Erario, Thiago Arahn Detoni, Alessandro Silveira Duarte
Enterprise software requires constant updates to keep it usable. These updates originate in correcting errors and mainly in new organizational demands. Over time, these demands generate a significant workload that becomes increasingly complex than the first requirements. For this reason, the organization providing the software may choose to continue updating the old product or make it obsolete and
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Software Testing Integration-Based Model (I-BM) Framework for Recognizing Measure Fault Output Accuracy Using Machine Learning Approach Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-06-28 Zulkifli Zulkifli, Ford Lumban Gaol, Agung Trisetyarso, Widodo Budiharto
In software development, the software testing phase is an important process in determining the quality level of the software. Software testing is a process of executing a program aimed at finding errors in module access, units, and involves the execution of the system being tested on a number of test inputs, and determining whether the output produced is correct. In this study, a model-based testing
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Understanding the Role of Stack Overflow in Supporting Software Development Tasks: A Research Perspective Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-06-26 Wenhua Yang, Chaochao Shen
Stack Overflow is a Q&A website that is popular among developers and extensively used in software engineering (SE) research. A significant body of research has examined how Stack Overflow can assist with software development tasks, such as recommending APIs. However, while researchers have recognized the importance of Stack Overflow in SE research related to software development tasks, the specific
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Semantic Clone Detection Based on Code Feature Fusion Learning Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-06-22 Qianjin Zhang, Dahai Jin, Yawen Wang, Yunzhan Gong
Code clones are duplicated code snippets that significantly threaten software maintenance and the public corpora of code representation learning. Traditionally, code context and its structure information abstract syntax tree (AST), control flow graph (CFG) are typical representations of source code, and context-based models and structure-based models contributed significantly to the development of
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CodeLabeller: A Web-Based Code Annotation Tool for Java Design Patterns and Summaries Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-06-16 Najam Nazar, Norman Chen, Chun Yong Chong
While constructing supervised learning models, we require labeled examples to build a corpus and train a machine learning model. However, most studies have built the labeled dataset manually, which, on many occasions, is a daunting task. To mitigate this problem, we have built an online tool called CodeLabeller. CodeLabeller is a web-based tool that aims to provide an efficient approach to handling
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Studying the Co-Evolution of Source Code and Acceptance Tests Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-06-08 Ali Görkem Yalçın, Tugkan Tuglular
Testing is a vital part of achieving good-quality software. Deploying untested code can cause system crashes and unexpected behavior. To reduce these problems, testing should evolve with coding. In addition, test suites should not remain static throughout the software versions. Since whenever software gets updated, new functionalities are added, or existing functionalities are changed, test suites
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TC4MT: A Specification-Driven Testing Framework for Model Transformations Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-05-29 Thi-Hanh Nguyen, Duc-Hanh Dang
Model transformation is a core mechanism for Model-Driven Engineering (MDE). Writing complex programs such as model transformations (MT) is error-prone, and efficient testing techniques are required for their quality assurance. There are several challenges when it comes to testing MT, including the automatic generation of suitable input test models and the construction of test oracles based on verification
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Exact Learning of Qualitative Constraint Networks from Membership Queries Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-05-17 Malek Mouhoub, Hamad Al Marri, Eisa Alanazi
A Qualitative Constraint Network (QCN) is a constraint graph representing problems under qualitative temporal or spatial relations. More formally, a QCN includes a set of entities and a list of qualitative constraints defining the possible scenarios between these entities. Qualitative constraints are expressed as disjunctions of binary relations capturing the (incomplete) knowledge between the involved
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Mutation-Based Minimal Test Suite Generation for Boolean Expressions Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-05-17 Tolga Ayav, Fevzi Belli
Boolean expressions are highly involved in control flows of programs and software specifications. Coverage criteria for Boolean expressions aim at producing minimal test suites to detect software faults. There exist various testing criteria, efficiency of which is usually evaluated through mutation analysis. This paper proposes an integer programming-based minimal test suite generation technique relying
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Verification of Safety for Synchronous-Reactive System Using Bounded Model Checking Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-05-17 Xiaozhen Zhang, Zhaoming Yang, Hui Kong, Weiqiang Kong
Real-time embedded systems are increasingly applied in safety-critical areas, so guaranteeing the correctness of such systems by means of formal methods becomes particularly important. In this paper, we propose an optimized bounded model checking (BMC)-based formal verification approach for the verification of safety for synchronous-reactive (SR) models, which are often used to design systems with
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A Methodology to Analyze and Estimate the Software Development Process Using Machine Learning Techniques Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-05-15 R. Lalitha, P. Sreelekha
Analyzing the software development process and estimating the effort required for its completion is an essential task. In the case of Agile methodology, the values of the parameters used for estimation vary frequently as the scope of the project changes with changes in the requirements of the clients. Hence, the estimation done at the initial phase will not be appropriate until the completion of the
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Enhancing Answer Selection via Ad-Hoc Knowledge Extraction from Unstructured Web Texts Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-05-13 Shengwei Gu, Xiangfeng Luo, Hao Wang
Answer selection aims to identify the most relevant answers to a given question from a set of candidates. It is the fundamental component of intelligent question answering system. To improve performance, it gradually becomes an effective strategy to integrate external structured knowledge bases (KBs) into the answer selection model. Due to expensive cost of construction and maintenance of such KBs
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Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-Analysis Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-20 Yuanqing Mei, Yi Rong, Shiran Liu, Zhaoqiang Guo, Yibiao Yang, Hongmin Lu, Yutian Tang, Yuming Zhou
Many studies have explored the methods of deriving thresholds of object-oriented (i.e. OO) metrics. Unsupervised methods are mainly based on the distributions of metric values, while supervised methods principally rest on the relationships between metric values and defect-proneness of classes. The objective of this study is to empirically examine whether there are effective threshold values of OO metrics
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Path Generation for a Given Performance Evaluation Value Interval by Modifying Bat Algorithm with Heuristic Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-20 Fujun Wang, Zining Cao, Zhen Li, Chao Xing, Hui Zong
Path generation means generating a path or a set of paths so that the generated path meets specified properties or constraints. To our knowledge, generating a path with the performance evaluation value of the path within a given value interval has received scant attention. This paper subtly formulates the path generation problem as an optimization problem by designing a reasonable fitness function
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A Formal Approach for Consistency Management in UML Models Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-19 Hao Wen, Jinzhao Wu, Jianmin Jiang, Guofu Tang, Zhong Hong
Consistency is a significant indicator to measure the correctness of a software system in its lifecycle. It is inevitable to introduce inconsistencies between different software artifacts in the software development process. In practice, developers perform consistency checking to detect inconsistencies, and apply their corresponding repairs to restore consistencies. Even if all inconsistencies can
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Identifying Notable Tuples in Multi-Concept Web Tables Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-18 Yihai Xi, Ning Wang
Identifying notable tuples in a web table is of great help for table understanding and table summarization. However, existing document-internal feature-based methods are inappropriate for identifying notable tuples in web tables. Additionally, for the web table describing multiple concepts, the notability evaluation of a tuple needs to take into account multiple entities as well as their importance