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What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes

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arXiv 2021年

作者： Trautsch, Alexander Erbel, Johannes Herbold, Steffen Grabowski, Jens Institute of Software and Systems Engineering TU Clausthal Germany Institute of Computer Science University of Goettingen Germany

Many software metrics are designed to measure aspects that are believed to be related to software quality. Static software metrics, e.g., size, complexity and coupling are used in defect prediction research as well as software quality models to evaluate software quality. Static analysis tools also include boundary values for complexity and size that generate warnings for developers. While this indicates a relationship between quality and software metrics, the extent of it is not well understood. Moreover, recent studies found that complexity metrics may be unreliable indicators for understandability of the source code. To explore this relationship, we leverage the intent of developers about what constitutes a quality improvement in their own code base. We manually classify a randomized sample of 2,533 commits from 54 Java open source projects as quality improving depending on the intent of the developer by inspecting the commit message. We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for the fine-tuning of a state-of-the art deep learning model for natural language processing. The benchmark we provide with our ground truth indicates that the deep learning model can be confidently used for commit intent classification. We use the model to increase our data set to 125,482 commits. Based on the resulting data set, we investigate the differences in size and 14 static source code metrics between changes that increase quality, as indicated by the developer, and other changes. In addition, we investigate which files are targets of quality improvements. We find that quality improving commits are smaller than other commits. Perfective changes have a positive impact on static source code metrics while corrective changes do tend to add complexity. Furthermore, we find that files which are the target of perfective maintenance already have a lower median complexity than other files. Our study results provid

关键词： Static analysis

Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants

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Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

Frontiers of Information Technology (FIT)

作者： Hassan Ashfaq Usama Arshad Raja Hashim Ali Zain ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model’s performance. To address this, we propose using regularization and data augmentation techniques to enhance the model’s generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture.

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QuIP: A P4 Quantum Internet Protocol Prototyping Framework

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arXiv 2024年

作者： Kozlowski, Wojciech Kuipers, Fernando A. Smets, Rob Turkovic, Belma QuTech Delft University of Technology Delft2628 CD Netherlands The Networked Systems Group Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands SURF Utrecht3511 EP Netherlands TNO The Hague2595 DA Netherlands

Quantum entanglement is so fundamentally different from a network packet that several quantum network stacks have been proposed;one of which has even been experimentally demonstrated. Several simulators have also been developed to make up for limited hardware availability, and which facilitate the design and evaluation of quantum network protocols. However, the lack of shared tooling and community-agreed node architectures has resulted in protocol implementations that are tightly coupled to their simulators. Besides limiting their reusability between different simulators, it also makes building upon prior results and simulations difficult. To address this problem, we have developed QuIP: a P4-based Quantum Internet Protocol prototyping framework for quantum network protocol design. QuIP is a framework for designing and implementing quantum network protocols in a platform-agnostic fashion. It achieves this by providing the means to flexibly, but rigorously, define device architectures against which quantum network protocols can be implemented in the network programming language P416. QuIP also comes with the necessary tooling to enable their execution in existing quantum network simulators. We demonstrate its use by showcasing V1Quantum, a completely new device architecture, implementing a link- and network-layer protocol, and simulating it in the existing simulator NetSquid. © 2024, CC BY.

关键词： Network architecture

Exploring the relationship between performance metrics and cost saving potential of defect prediction models

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arXiv 2021年

作者： Tunkel, Steffen Herbold, Steffen Institute of Computer Science University of Goettingen Germany Institute of Software and Systems Engineering TU Clausthal Germany

Context: Performance metrics are a core component of the evaluation of any machine learning model and used to compare models and estimate their usefulness. Recent work started to question the validity of many performance metrics for this purpose in the context of software defect prediction. Objective: Within this study, we explore the relationship between performance metrics and the cost saving potential of defect prediction models. We study whether performance metrics are suitable proxies to evaluate the cost saving capabilities and derive a theory for the relationship between performance metrics and cost saving potential. Methods: We measure performance metrics and cost saving potential in defect prediction experiments. We use a multinomial logit model, decision, and random forest to model the relationship between the metrics and the cost savings. Results: We could not find a stable relationship between cost savings and performance metrics. We attribute the lack of the relationship to the inability of performance metrics to account for the property that a small proportion of very large software artifacts are the main driver of the costs. fact that performance metrics are incapable of accurately considering the costs associate with individual artifacts, which is required due to the exponential distribution of artifact sizes. Conclusion: Any defect prediction study interested in finding the best prediction model, must consider cost savings directly, because no reasonable claims regarding the economic benefits of defect prediction can be made otherwise. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision trees

A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications

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IEEE Transactions on Network and Service Management 2024年

作者： Tang, Jun Guo, Bing Shen, Yan Garg, Sahil Kaddoum, Georges Hossain, M. Shamim Sichuan University College of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China École de Technologie Supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Lebanese American University Artificial Intelligence and Cyber Systems Research Center Beirut03797751 Lebanon King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy. © 2024 IEEE.

关键词： Data assimilation

xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart

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arXiv 2024年

作者： Chen, Tianrun Ding, Chaotao Zhu, Lanyun Xu, Tao Ji, Deyi Wang, Yan Zang, Ying Li, Zejian College of Computer Science and Technology Zhejiang University China Tech. Co. LTD China School of Information Engineering Huzhou University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore School of Software Technology Zhejiang University China School of Information Science and Technology University of Science and Technology of China China School of Instrumentation and Optoelectronic Engineering State Key Laboratory of Software Development Environment Beihang University China

Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) have been pivotal in biomedical image segmentation. Yet, their ability to manage long-range dependencies remains constrained by inherent locality and computational overhead. To overcome these challenges, in this technical report, we first propose xLSTM-UNet, a UNet structured deep learning neural network that leverages Vision-LSTM (xLSTM) as its backbone for medical image segmentation. xLSTM has recently been proposed as the successor of Long Short-Term Memory (LSTM) networks and has demonstrated superior performance compared to Transformers and State Space Models (SSMs) like Mamba in Neural Language Processing (NLP) and image classification (as demonstrated in Vision-LSTM, or ViL implementation). Here, xLSTM-UNet we designed to extend the success in the biomedical image segmentation domain. By integrating the local feature extraction strengths of convolutional layers with the long-range dependency-capturing abilities of xLSTM, xLSTM-UNet offers a robust solution for comprehensive image analysis. We validate the efficacy of xLSTM-UNet through experiments. Our findings demonstrate that xLSTM-UNet consistently surpasses the performance of leading CNN-based, Transformer-based, and Mamba-based segmentation networks in multiple datasets in biomedical segmentation including organs in abdomen MRI, instruments in endoscopic images, and cells in microscopic images. With comprehensive experiments performed, this technical report highlights the potential of xLSTM-based architectures in advancing biomedical image analysis in both 2D and 3D. The code, models, and datasets are publicly available at http://***/xLSTM-Unet/. © 2024, CC BY-NC-ND.

关键词： Long short-term memory

Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

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Tsinghua Science and Technology 2020年第1期25卷 1-11页

作者： Jiachen Yang Chaofan Ma Jiabao Man Huifang Xu Gan Zheng Houbing Song School of Electrical and Information Engineering Tianjin UniversityTianjin 300000China Wolfson School of Mechanical Electrical and Manufacturing EngineeringLoughborough UniversityLoughboroughLE11 3TUUK Department of Electrical ComputerSoftwareand Systems EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL 32114USA.

The proliferation of mobile devices that support the acceleration of data services(especially smartphones)has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary ***, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching.

关键词： cooperative cognitive radio network content caching power allocation

Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

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Performance Evaluation of Popular Deep Neural Networks for N...

Frontiers of Information Technology (FIT)

作者： Muhammad Naeem Abu Bakar Siddique Raja Hashim Ali Usama Arshad Zain ul Abideen Talha Ali Khan Muhammad Huzaifa Shah Ali Zeeshan Ijaz Nisar Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels.

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Research and development of two-factor authentication methods using neural networks

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Research and development of two-factor authentication method...

Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Temirkhan Darbek Assel Mukasheva Alibek Bissembayev Sergiy Gnatyuk Dinargul Mukammejanova Department of Information Systems and Cybersecurity Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital

关键词： Codes Face recognition Heuristic algorithms Data security Neural networks Authentication Chatbots