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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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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
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A Proposed Framework for Student’s Skills-Driven Personalization of Cloud-Based Course Content Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-18 Alaa A. Qaffas, Ibraheem Alharbi, Amira M. Idrees, Sherif A. Kholeif
Engaging students’ personalized data in the aspects of education has been on focus by different researchers. This paper considers it vital for exploring the student’s progress, moreover, it could predict the student’s level which consequently leads to identifying the required student material to raise his current education level. Although the topic has been vital before the COVID-19 pandemic, however
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An Empirical Study on Code Smell Introduction and Removal in Deep Learning Software Projects Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-14 Jungil Kim, Eunjoo Lee
With increasing popularity of Deep Learning (DL) software development, code quality issues arise in DL software development. Code smell is one of the factors which reduce the quality of source code. Several previous studies investigated the prevalence of code smell in DL software systems to evaluate the quality of DL source code. However, there is still a lack of understanding of the awareness of individual
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SQL#: A Language for Maintainable and Debuggable Database Queries Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-13 Yamin Hu, Hao Jiang, Hanlin Tang, Xin Lin, Zongyao Hu
Structured Query Language (SQL) is the dominant language for managing relational databases. However, complex SQL queries are hard to write and maintain because of the intricate inter-table and inter-column relations. To this end, we propose a novel query language called SQL#, which allows programmers to construct complex queries module by module and explicitly specify the relations between different
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XL-BPMN Model-Based Service Similarity Measurement Technique Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-12 Cheeyang Song, Eunsook Cho
In service-oriented developments, existing studies do not give lots of efforts on a formalized and systematic method for measuring similarities between services for their reuse in business models. This deteriorates the reusability of the constructed service due to the developers’ intuition and informal service analyses. In this paper, we propose a technique for measuring similarity of services by analyzing
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On the Succinctness of Modal μ-Calculus Based on Covariant–Contravariant Refinement Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-05 Huili Xing
The expressive power of logics is one of the major research topics in mathematical logic and computer science. One way of comparing the complexities of different formalisms (e.g. knowledge representation formalisms) stems from the perspective of representational succinctness. The concept of covariant–contravariant refinement (CC-refinement, for short) generalizes the concepts of refinement, simulation
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A Hierarchical Feature Ensemble Deep Learning Approach for Software Defect Prediction Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-04-05 Shenggang Zhang, Shujuan Jiang, Yue Yan
Software defect prediction can detect modules that may have defects in advance and optimize resource allocation to improve test efficiency and reduce development costs. Traditional features cannot capture deep semantic and grammatical information, which limits the further development of software defect prediction. Therefore, it has gradually become a trend to use deep learning technology to automatically
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Person Event Detection Method in Computer Discipline Domain Based on BiGRU and CNN in Series Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-03-31 Xiaoming Zhang, Xin Yang, Huiyong Wang
The knowledge graph of computer discipline domain plays a critical role in computer education, and the person event is an important part of the discipline knowledge graph. Adding person events to the graph will make the discipline knowledge graph richer and more interesting, and enhance enthusiasm of students for learning. The most crucial step in building the person event knowledge graph is the extraction
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Three Approaches for Detecting Direct Output Cheating in Program Online Judge Systems Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-03-30 Jing Qiu, Chunmei Shi, Yuehua Lv
Program online judge (POJ) systems allow students to view questions, submit solution code, and receive scores automatically via the web. Most POJs use test cases for scoring. When a POJ is scored by test case pass rate or a problem that has only one test case, students can usually score by providing the direct output of the test cases (direct output cheating). Currently, there is only one work on detecting
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Aligning Software Architecture Training with Software Industry Requirements Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-03-23 Wilson Libardo Pantoja Yepez, Julio Ariel Hurtado Alegria, Arvind Kiweleker
The activities of software design, documenting, and evaluating the structure of software systems, referred to as Software Architecture, have been increasingly getting significant attention in industries. This situation is because of the explicit and prominent role assigned to quality attributes while developing software systems. By considering the high relevance of Software Architecture to industry
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Accelerating Multi-Exit BERT Inference via Curriculum Learning and Knowledge Distillation Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-03-13 Shengwei Gu, Xiangfeng Luo, Xinzhi Wang, Yike Guo
The real-time deployment of bidirectional encoder representations from transformers (BERT) is limited by its slow inference caused by its large number of parameters. Recently, multi-exit architecture has garnered scholarly attention for its ability to achieve a trade-off between performance and efficiency. However, its early exits suffer from a considerable performance reduction compared to the final
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Automated Priority Prediction for Bug Reports Using Comment Intensiveness Features and SMOTE Data Balancing Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-03-09 Anh-Hien Dao, Cheng-Zen Yang
The processing priorities for software bug reports are important for software maintenance. Predicting the priorities for bug reports is the subject of many software engineering studies. This study proposes a priority prediction method that uses comment intensiveness features and a Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing scheme. Experiments use datasets for three open-source
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Use of Journey Maps and Personas in Software Requirements Elicitation Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-03-04 Edna Dias Canedo, Angelica Toffano Seidel Calazans, Geovana Ramos Sousa Silva, Pedro Henrique Teixeira Costa, Eloisa Toffano Seidel Masson
Requirements elicitation is a fundamental step in a software development process since it is at this stage that the software begins to be designed. In some situations, the problems related to the failure of the software development project are due to an incomplete requirements elicitation, resulting in solutions that do not understand all the necessary functionalities or do not incorporate innovation
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Which Exceptions Do We Have to Catch in the Python Code for AI Projects? Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-02-15 Mingu Kang, Suntae Kim, Duksan Ryu, Jaehyuk Cho
Recently, Python is the most-widely used language in artificial intelligence (AI) projects requiring huge amount of CPU and memory resources, and long execution time for training. For saving the project duration and making AI software systems more reliable, it is inevitable to handle exceptions appropriately at the code level. However, handling exceptions highly relies on developer’s experience. This
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Automatic Extraction of Ontological Explanation for Machine Learning-Based Systems Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-01-28 Nacha Chondamrongkul, Punnarumol Temdee
Machine learning has been implemented as a part of many software systems to support data-driven decisions and recommendations. The prominent machine learning technique is the artificial neural network, which lacks the explanation of how it produces the output. However, many application domains require algorithmic decision making to be transparent so explainability in these systems has been an important
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KEMA++: A Full Representative Knowledge-Graph Embedding Model (036) Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2023-01-19 Hussein Baalbaki, Hussein Hazimeh, Hassan Harb, Rafael Angarita
Nowadays, representing entities and relations in a machine understandable way through Knowledge graph embedding (KGE) has been proven as an effective approach for predicting missing links in knowledge graphs (KGs). Mainly, the success of such approach depends on the model ability to infer the patterns of the relations. Indeed, most of the existing KG models highly focus on modeling simple relation
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Personalized Learning Path Recommendation for E-Learning Based on Knowledge Graph and Graph Convolutional Network Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2022-12-22 Xiaoming Zhang, Shan Liu, Huiyong Wang
In e-learning, the increasing number of learning resources makes it difficult for learners to find suitable learning resources. In addition, learners may have different preferences and cognitive abilities for learning resources, where differences in learners’ cognitive abilities will lead to different importance of learning resources. Therefore, recommending personalized learning paths for learners
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Matching Logic for Concurrent Programs Based on Rely/Guarantee and Abstract Patterns Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2022-12-21 ShangBei Wang, WeiYu Dong
This paper combines rely/guarantee, abstract patterns and matching logic to reason about concurrent programs in a modular and compositional manner. According to the separation property, the state can be divided into two disjoint parts, the local state and the shared state. We use matching logic to deal with the local state, and use rely/guarantee and abstract patterns to deal with the shared state
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A Comparative Study of Ensemble Techniques Based on Genetic Programming: A Case Study in Semantic Similarity Assessment Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2022-12-21 Jorge Martinez-Gil
The challenge of assessing semantic similarity between pieces of text through computers has attracted considerable attention from industry and academia. New advances in neural computation have developed very sophisticated concepts, establishing a new state of the art in this respect. In this paper, we go one step further by proposing new techniques built on the existing methods. To do so, we bring
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A Hybrid Multiple Models Transfer Approach for Cross-Project Software Defect Prediction Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2022-12-19 Shenggang Zhang, Shujuan Jiang, Yue Yan
For a new project, it is impossible to get a reliable prediction model because of the lack of sufficient training data. To solve the problem, researchers proposed cross-project defect prediction (CPDP). For CPDP, most researchers focus on how to reduce the distribution difference between training data and test data, and ignore the impact of class imbalance on prediction performance. This paper proposes
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Mapping Modern JVM Language Code to Analysis-Friendly Graphs: A Study with Kotlin Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2022-12-17 Lu Li, Yan Liu
Kotlin is a modern JVM language, gaining adoption rapidly and becoming Android official programming language. With its wide usage, the need for code analysis of Kotlin is increasing. Exposing code semantics explicitly with a properly structured format is the first step in code analysis and the construction of such representation is the foundation for downstream tasks. Recently, graph-based approaches
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MIAR: A Context-Aware Approach for App Review Intention Mining Int. J. Softw. Eng. Knowl. Eng. (IF 0.9) Pub Date : 2022-12-17 Jinwei Lu, Yimin Wu, Jiayan Pei, Zishan Qin, Shizhao Huang, Chao Deng
Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users. To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective. Although