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2010 ASEE Annual Conference and Exposition

作者： Long, James Software Engineering Technology Program Oregon Institute of Technology United States

Outsourcing is gaining an increasing presence in the US software development industry. As the Internet develops in emerging economies, the infrastructure required for effective outsourcing is maturing. The US software development workforce is now faced with viable competition from countries where software developer salaries are lower by an order of magnitude. In technical software engineering education, code development is emphasized as a basis of laboratory exercises and hands-on experience. Outsourcing trends bring several challenges to the technical education table. As software engineering educators, how can we prepare our students for a workforce where a key aspect of the education we provide may prove irrelevant in the near future? This paper studies the issue of outsourcing in the software engineering educational environment as a required component of a software engineering project. The first aspect presented is the outsourcing of a capstone project for the Bachelor of Science in software engineering technology program to a developer or development team in an emerging economy. An operational process is presented showing how this was done to bring outsourcing as a viable activity for student projects. The second aspect examines application of the developed framework for outsourcing as a key component of developing large scale software systems. The junior project sequence is used as a test platform where five student teams are tasked with outsourcing a component of their project given a budget of $50.00 per team. The outcome impacts their final grade. The results of this activity are presented and analyzed. Outsourcing of software development will increase in the software development industry as Internet based support infrastructure matures. The topics explored here deal with how to embrace outsourcing trends to prepare students for globally distributed software development. © American Society for engineering Education, 2010.

关键词： Outsourcing

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PromptFusion:Harmonized Semantic Prompt Learning for Infrared and Visible Image Fusion

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 502-515页

作者： Jinyuan Liu Xingyuan Li Zirui Wang Zhiying Jiang Wei Zhong Wei Fan Bin Xu IEEE the School of Software Technology Dalian University of Technology the School of Mechanical Engineering Beijing Institute of Technology

The goal of infrared and visible image fusion(IVIF)is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene. However, existing methods struggle to effectively handle modal disparities,resulting in visual degradation of the details and prominent targets of the fused images. To address these challenges, we introduce Prompt Fusion, a prompt-based approach that harmoniously combines multi-modality images under the guidance of semantic prompts. Firstly, to better characterize the features of different modalities, a contourlet autoencoder is designed to separate and extract the high-/low-frequency components of different modalities, thereby improving the extraction of fine details and textures. We also introduce a prompt learning mechanism using positive and negative prompts, leveraging Vision-Language Models to improve the fusion model's understanding and identification of targets in multi-modality images, leading to improved performance in downstream tasks. Furthermore, we employ bi-level asymptotic convergence optimization. This approach simplifies the intricate non-singleton non-convex bi-level problem into a series of convergent and differentiable single optimization problems that can be effectively resolved through gradient *** approach advances the state-of-the-art, delivering superior fusion quality and boosting the performance of related downstream tasks. Project page: https://***/hey-it-s-me/PromptFusion.

关键词： Bi-level optimization image fusion infrared and visible image prompt learning

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MSCM-Net:Rail Surface Defect Detection Based on a Multi-Scale Cross-Modal Network

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Computers, Materials & Continua 2025年第3期82卷 4371-4388页

作者： Xin Wen Xiao Zheng Yu He School of Software Engineering Shenyang University of TechnologyShenyang110870China

Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail ***,existing detection methods often struggle with challenges such as complex defect morphology,texture similarity,and fuzzy edges,leading to poor accuracy and missed *** order to resolve these problems,we propose MSCM-Net(Multi-Scale Cross-Modal Network),a multiscale cross-modal framework focused on detecting rail surface ***-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps,effectively capturing and enhancing features at different scales for each *** further enrich feature representation and improve edge detection in blurred areas,we propose a multi-scale void fusion module that integrates multi-scale feature *** improve cross-modal feature fusion,we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer *** also introduce a multimodal feature integration module,which merges modality-specific features from separate decoders into a shared decoder,enhancing detection by leveraging richer complementary ***,we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset,comparing it against 12 leading methods,and the results show that MSCM-Net achieves superior performance on all metrics.

关键词： Surface defect detection multiscale framework cross-modal fusion edge detection

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Adversarial Patterns: Building Robust Android Malware Classifiers

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ACM Computing Surveys 2025年第8期57卷 1-34页

作者： Bhusal, Dipkamal Rastogi, Nidhi Department of Software Engineering Rochester Institute of Technology RochesterNY United States Software Engineering Rochester Institute of Technology RochesterNY United States

Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, detect patterns, and make predictions or recommendations. In the field of cybersecurity, these models have made significant improvements in malware detection. However, despite their ability to understand complex patterns from unstructured data, these models are susceptible to adversarial attacks that perform slight modifications in malware samples, leading to misclassification from malignant to benign. Numerous defense approaches have been proposed to either detect such adversarial attacks or improve model robustness. These approaches have resulted in a multitude of attack and defense techniques and the emergence of a field known as 'adversarial machine learning.' In this survey paper, we provide a comprehensive review of adversarial machine learning in the context of Android malware classifiers. Android is the most widely used operating system globally and is an easy target for malicious agents. The paper first presents an extensive background on Android malware classifiers, followed by an examination of the latest advancements in adversarial attacks and defenses. Finally, the paper provides guidelines for designing robust malware classifiers and outlines research directions for the future. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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E-PRedictor: an approach for early prediction of pull request acceptance

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Science China(Information Sciences) 2025年第5期68卷 380-395页

作者： Kexing CHEN Lingfeng BAO Xing HU Xin XIA Xiaohu YANG State Key Laboratory of Blockchain and Data Security Zhejiang University Software Engineering Application Technology Lab

A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software(OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while(e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor(earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features(i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect475192 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models(e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1@Merge, F1@Reject, and AUC(area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1%, 60.5%, and 85.4%, respectively.

关键词： pull request prediction model GitHub

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Two stage-network: Automatic localization of Optic Disc (OD) and classification of glaucoma in fundus images using deep learning techniques

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Multimedia Tools and Applications 2025年第14期84卷 12949-12977页

作者： Sheraz, Huma Shehryar, Tehmina Khan, Zuhaib Ahmed Department of Software Engineering Mirpur University of Science & Technology Mirpur10240 Pakistan Department of Software Engineering Capital University of Science & Technology Islamabad Pakistan CareCloud Islamabad Pakistan

Glaucoma is an ophthalmic disorder which results in permanent vision loss because high intraocular pressure damages the optic nerve in the eye. This paper proposes a two-stage network for automated glaucoma identification utilizing fundus images. In the first stage, Yolo-v4 is used to locate and extract the optic disc from a retinal fundus image, and ResNet-101 is used in the second stage to determine whether the retrieved disc is glaucomatous or healthy. Unfortunately, none of the publicly accessible retinal fundus image datasets contain the necessary bounding box ground truth for disc localization. In this regard, a semi-automatic ground truth creation strategy has been proposed that gives the essential annotations enabling the training of the Yolo-v4 based model for autonomous disc localization. The proposed method is evaluated on ORIGA publicly available dataset. The proposed automated OD localization showed good results with 87.4% accuracy, 89.79% precision and 88.7% recall. Whereas the proposed glaucoma diagnosis module attained good results with 88.5% accuracy and AUC of.920. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computer aided diagnosis

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GPIO-Based Continuous Sliding Mode Control for Networked Control Systems Under Communication Delays With Experiments on Servo Motors

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 99-113页

作者： Kamal Rsetam Zhenwei Cao Zhihong Man Xian-Ming Zhang the School of Software and Electrical Engineering Swinburne University of Technology the Department of Automated Manufacturing Al Khwarizmi College of Engineering University of Baghdad IEEE

To handle input and output time delays that commonly exist in many networked control systems(NCSs), a new robust continuous sliding mode control(CSMC) scheme is proposed for the output tracking in uncertain single input-single-output(SISO) networked control systems. This scheme consists of three consecutive steps. First, although the network-induced delay in those systems can be effectively handled by using Pade approximation(PA), the unmatched disturbance cames out as another difficulty in the control design. Second, to actively estimate this unmatched disturbance, a generalized proportional integral observer(GPIO) technique is utilized based on only one measured state. Third, by constructing a new sliding manifold with the aid of the estimated unmatched disturbance and states, a GPIO-based CSMC is synthesized, which is employed to cope with not only matched and unmatched disturbances, but also networkinduced delays. The stability of the entire closed-loop system under the proposed GPIO-based CSMC is detailedly *** promising tracking efficiency and feasibility of the proposed control methodology are verified through simulations and experiments on Quanser's servo module for motion control under various test conditions.

关键词： Continuous sliding mode control (CSMC) generalized proportional integral observer (GPIO) networked control systems (NCSs) pade approximation (PA) time-delay unsmatched disturbances

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Events and findings of the Purdue University Calumet Computer Information technology & Graphics (CIT & G) department teaching partnership with the College of software engineering of Southeast University in Nanjing China 09

Events and findings of the Purdue University Calumet Compute...

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10th ACM Special Interest Group for Information technology Education, SIGITE 2009

作者： Winer, Charles Wu, Hanqian Department of Computer Information Technology and Graphics Purdue University Calumet Hammond IN United States College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781605587653

In this poster session we are reporting on the results of two, three-week summer graduate teaching experiences that took place in Nanjing, China over a two-year period. A faculty exchange program was entered into between Southeast University of Nanjing China and Purdue University Calumet of Hammond, Indiana, USA. One of the goals of the exchange program was to expose Chinese students to the instructional methods employed by United States Universities. By understanding the cultural differences and utilizing various teaching methodologies employed by American teachers, the faculty and students involved in these three-week classroom intensive training courses were able to adapt and successfully complete the graduate level material that was presented.

关键词： Teaching

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Multi-Scale Feature Optimization Point Cloud Completion Network Integrating SoftPool

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第1期52卷 233-243页

作者： Zhang, Wanpeng Zhou, Ziwei School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Point cloud completion is crucial in point cloud processing, as it can repair and refine incomplete 3D data, ensuring more accurate models. However, current point cloud completion methods commonly face a challenge: they fail to fully utilize multi-scale information from local features, leading to limitations in accuracy and detail preservation. To address this issue, this paper proposes a multi-scale feature optimization algorithm for point cloud completion that integrates SoftPool. Based on DGCNN, the method combines dilated convolution and bottleneck attention mechanisms to extract features at different scales, enhancing the ability to capture detailed information in point clouds. The bottleneck attention mechanism is used to optimize important detail features. The extracted local features are concatenated with their corresponding positional information to form point proxies, enhancing the effective extraction of local geometric features, resulting in more refined completed point cloud shapes. A Transformer architecture is employed to model these features. Finally, SoftPool is introduced for fine-grained feature downsampling, improving the network's ability to recover point cloud details. FoldingNet is used to reconstruct missing structures and output the completed point cloud. To validate the model's completion performance, training and testing are conducted on the PCN and ShapeNet55 datasets. Experimental results demonstrate that the model has better feature detail retention and more accurate completion results. On the PCN dataset, the average CD value is reduced by 6.5% compared to the best-performing model among the comparison methods. On the ShapeNet55 dataset, the average CD value across three difficulty levels is reduced by 6.9% compared to the best-performing model among the comparison methods. Additionally, the model also achieved a 2.1% improvement in F-score. © (2025), (International Association of Engineers). All rights reserved.

关键词： Ability testing

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