Hasil untuk "Computer software"

D. Knuth

Stefano Lambiase, Manuel De Stefano, Fabio Palomba et al.

Quantum computing has gained significant attention due to its potential to solve computational problems beyond the capabilities of classical computers. With major corporations and academic institutions investing in quantum hardware and software, there has been a rise in the development of quantum-enabled systems, particularly within open-source communities. However, despite the promising nature of quantum technologies, these communities face critical socio-technical challenges, including the emergence of socio-technical anti-patterns known as community smells. These anti-patterns, prevalent in open-source environments, have the potential to negatively impact both product quality and community health by introducing technical debt and amplifying architectural and code smells. Despite the importance of these socio-technical factors, there remains a scarcity of research investigating their influence within quantum open-source communities. This work aims to address this gap by providing a first step in analyzing the socio-technical well-being of quantum communities through a cross-sectional study. By understanding the socio-technical dynamics at play, it is expected that foundational knowledge can be established to mitigate the risks associated with community smells and ensure the long-term sustainability of open-source quantum initiatives.

Yiqiong Liang, Mingwan Luo

Abstract The Internet of Things (IoT) has been proposed to pose a greater risk of cyberattacks due to the large amounts of data traffic and the diverse range of devices. The main limitations of traditional centralized intrusion detection systems (IDSs) are attributed to privacy risks, high communication costs, and poor scalability. The research presents a distributed, privacy-preserving framework for intrusion detection, which combines Federated Learning (FL) with a new Deep Learning model that performs and optimizes network intrusions to collect and analyze aspects of “federated” augmentation, then improve security in Web usage. The particular method includes Recursive Feature Elimination (RFE) for the reduction in characteristics, the Federated Kalman Filter (FKF) to reduce noise, and an Adaptive Artificial Fish Swarm optimized Long Short-Term Memory (AdapAFS-LSTM) model for accurate detection of multi-type network intrusions. The model parameters are distributed based on IoT model nodes and do not share raw data. Model parameters learn from IoT nodes, which are combined based on the Federated Proximal (FedProx) algorithm and can be applied toward the development of a robust global IDS. Experimental evaluation of the distributed and privacy-preserving intrusion detection framework on the Multi-Type Network Attack Detection (M-TNAD) dataset demonstrated superior performance in achieving 99.79% accuracy, F1-score, precision, and recall, showing low resource consumption in the final execution time and performance metrics. This work demonstrates the potential of implementing a federated, optimization-driven deep learning method to effectively develop an IDS solution against IoT networks through optimization methodology and machine learning.

Lakshit Arora, Sanjay Surendranath Girija, Shashank Kapoor et al.

Artificial Intelligence (AI) is rapidly expanding and integrating more into daily life to automate tasks, guide decision making, and enhance efficiency. However, complex AI models, which make decisions without providing clear explanations (known as the "black-box problem"), currently restrict trust and widespread adoption of AI. Explainable Artificial Intelligence (XAI) has emerged to address the black-box problem of making AI systems more interpretable and transparent so stakeholders can trust, verify, and act upon AI-based outcomes. Researchers have developed various techniques to foster XAI in the Software Development Lifecycle. However, there are gaps in applying XAI techniques in the Software Engineering phases. Literature review shows that 68% of XAI in Software Engineering research is focused on maintenance as opposed to 8% on software management and requirements. In this paper, we present a comprehensive survey of the applications of XAI methods such as concept-based explanations, Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), rule extraction, attention mechanisms, counterfactual explanations, and example-based explanations to the different phases of the Software Development Life Cycle (SDLC), including requirements elicitation, design and development, testing and deployment, and evolution. To the best of our knowledge, this paper presents the first comprehensive survey of XAI techniques for every phase of the Software Development Life Cycle (SDLC). This survey aims to promote explainable AI in Software Engineering and facilitate the practical application of complex AI models in AI-driven software development.

Luz-Viviana Cobaleda, Julián Carvajal, Paola Vallejo et al.

Modern software systems increasingly integrate machine learning (ML) due to its advancements and ability to enhance data-driven decision-making. However, this integration introduces significant challenges for software engineering, especially in software product lines (SPLs), where managing variability and reuse becomes more complex with the inclusion of ML components. Although existing approaches have addressed variability management in SPLs and the integration of ML components in isolated systems, few have explored the intersection of both domains. Specifically, there is limited support for modeling and managing variability in SPLs that incorporate ML components. To bridge this gap, this article proposes a structured framework designed to extend Software Product Line engineering, facilitating the integration of ML components. It facilitates the design of SPLs with ML capabilities by enabling systematic modeling of variability and reuse. The proposal has been partially implemented with the VariaMos tool.

MAO Xinjun, LU Yao

Current education and teaching are mostly closed in physical boundaries,highly depending on teachers and textbooks,and deficient in the reuse of learning knowledges.For these limitations,we propose the concept and method of crowdsourced learning(CL) by drawing on the ideas of crowd intelligence and open source software.The core idea of CL is to break the boundaries of traditional classes,grades,and schools,and allow learners to engage in autonomous and collaborative social learning around specific themes in the form of open communities,such as problem discussions,experience sharing,and resource co-production.Such methods can produce a large number of high-quality and personalized learning resources,help learners effectively conduct course learning and practice,while also help teachers improve the efficiency and quality of course teaching.This paper also introduces the platform called LearnerHub that supports the CL,analyzes the application patterns of CL based on the software engineering course practices.We also evaluate the effectiveness and influence of CL in term of data analyses and investigation survey.The results show that students highly recognize the important role of CL method in course studies and practices,and there is a positive relationship between students' comprehensive practice performance of CL and their course grades.

Faquan Chen, Miaolei Deng, Hui Gao et al.

Abstract Surface defect detection is an essential task for ensuring the quality of products. Many excellent object detectors have been employed to detect surface defects in resent years, which has achieved outstanding success. To further improve the detection performance, a defect detector based on state‐of‐the‐art YOLOv8, named improved YOLOv8 by neck, head and data (NHD‐YOLO), is proposed. Specifically, YOLOv8 from three crucial aspects including neck, head and data is improved. First, a shortcut feature pyramid network is designed to effectively fuse features from backbone by improving the information transmission. Then, an adaptive decoupled head is proposed to alleviate the feature spatial misalignment between the classification and regression tasks. Finally, to enhance the training on small objects, a data augmentation method named selective small object copy and paste is proposed. Extensive experiments are conducted on three real‐world datasets: detection dataset from Northeastern University (NEU‐DET), printed circuit boards from Peking University (PKU‐Market‐PCB) and common objects in context (COCO). According to the results, NHD‐YOLO achieves the highest detection accuracy and exhibits outstanding inference speed and generalisation performance.

Omar Abou El Kheir, Luigi Bonati, Michele Parrinello et al.

Abstract The phase change compound Ge2Sb2Te5 (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these devices. We report here on the development of a machine-learned interatomic potential for GST225 that allowed us to perform large scale molecular dynamics simulations (over 10,000 atoms for over 100 ns) to uncover the details of the crystallization kinetics in a wide range of temperatures of interest for the programming of the devices. The potential is obtained by fitting with a deep neural network (NN) scheme a large quantum-mechanical database generated within density functional theory. The availability of a highly efficient and yet highly accurate NN potential opens the possibility to simulate phase change materials at the length and time scales of the real devices.

YAN Yintong, YU Lu, WANG Taiyan, LI Yuwei, PAN Zulie

Binary code similarity detection technology plays an important role in different security fields.Aiming at the problems of the existing binary code similarity detection methods,such as high computational cost and low accuracy,incomplete semantic information recognition of binary function and single evaluation data set,a binary code similarity detection technique based on Jump-SBERT is proposed.Jump-SBERT has two main innovations.One is to use twin networks to build SBERT network structure,which can reduce the calculation cost of the model while keeping the calculation accuracy unchanged.The other is to introduce jump recognition mechanism,which enables Jump-SBERT to learn the graph structure information of binary functions.Thus,the semantic information of binary function can be captured more comprehensively.Experimental results show that the re-cognition accuracy of Jump-SBERT can reach 96.3% in the small function pool(32 functions) and 85.1% in the large function pool(10 000 functions),which is 36.13% higher than state-of-the-art(SOTA) methods.Jump-SBERT is more stable in large-scale binary code similarity detection.Ablation experiments show that both of the two main innovation points have positive effects on Jump-SBERT,and the contribution of jump recognition mechanism is up to 9.11%.

Akmalbek Abdusalomov, Sabina Umirzakova, Makhkamov Bakhtiyor Shukhratovich et al.

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire detection systems rely heavily on single-sensor approaches and are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), a novel deep learning-based model optimized for drone-based wildfire and smoke detection. AMSO-SFS combines optical, infrared, and Synthetic Aperture Radar (SAR) data to detect fire and smoke under varied visibility conditions. The model introduces a Space–Frequency Selective Convolution (SFS-Conv) module to enhance the discriminative capacity of features in both spatial and frequency domains. Furthermore, AMSO-SFS utilizes weakly supervised learning and adaptive scale and angle detection to identify fire and smoke regions with minimal labeled data. Extensive experiments show that the proposed model outperforms current state-of-the-art (SoTA) models, achieving robust detection performance while maintaining computational efficiency, making it suitable for real-time drone deployment.

Yuze Jiang, Beijun Shen, Xiaodong Gu

For predicting software defects at an early stage, researchers have proposed just-in-time defect prediction (JIT-DP) to identify potential defects in code commits. The prevailing approaches train models to represent code changes in history commits and utilize the learned representations to predict the presence of defects in the latest commit. However, existing models merely learn editions in source code, without considering the natural language intentions behind the changes. This limitation hinders their ability to capture deeper semantics. To address this, we introduce a novel bi-modal change pre-training model called BiCC-BERT. BiCC-BERT is pre-trained on a code change corpus to learn bi-modal semantic representations. To incorporate commit messages from the corpus, we design a novel pre-training objective called Replaced Message Identification (RMI), which learns the semantic association between commit messages and code changes. Subsequently, we integrate BiCC-BERT into JIT-DP and propose a new defect prediction approach -- JIT-BiCC. By leveraging the bi-modal representations from BiCC-BERT, JIT-BiCC captures more profound change semantics. We train JIT-BiCC using 27,391 code changes and compare its performance with 8 state-of-the-art JIT-DP approaches. The results demonstrate that JIT-BiCC outperforms all baselines, achieving a 10.8% improvement in F1-score. This highlights its effectiveness in learning the bi-modal semantics for JIT-DP.

Easter Joseph, Pradeep Menon Vijaya Kumar, Balbir Singh Mahinder Singh et al.

This research work aims to develop a fault detection and performance monitoring system for a photovoltaic (PV) system that can detect and communicate errors to the user. The proposed system uses real-time data from various sensors to identify performance problems and faults in the PV system, particularly for encapsulation failure and module corrosion. The system incorporates a user interface that operates on a micro-computer utilizing Python software to show the detected errors from the PV miniature scale system. Fault detection is achieved by comparing the One-diode model with a controlled state retrieved through field testing. A database is generated by the system based on acceptable training data and it serves as a reference point for detecting faults. The user is notified of any deviations based on the threshold value from the training data as an indication of an error by the system. The system offers real-time monitoring, easy-to-understand error messages, and remote access capability, making it an efficient and effective tool for both users and maintenance personnel to manage and maintain the PV system.

Paul Hilaire, Yaron Castor, Edwin Barnes et al.

Quantum threshold theorems impose hard limits on the hardware capabilities to process quantum information. We derive tight and fundamental upper bounds to loss-tolerance thresholds in different linear-optical quantum information processing settings through an adversarial framework, taking into account the intrinsically probabilistic nature of linear optical Bell measurements. For logical Bell state measurements—ubiquitous operations in photonic quantum information—we demonstrate analytically that linear optics can achieve the fundamental loss threshold imposed by the no-cloning theorem even though, following the work of Lee et al. [Phys. Rev. A 100, 052303 (2019)] the constraint was widely assumed to be stricter. We spotlight the assumptions of the latter publication and find their bound holds for a logical Bell measurement built from adaptive physical linear-optical Bell measurements. We also give an explicit even stricter bound for nonadaptive Bell measurements.

Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong et al.

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, specifically by 10% on the overall score. We release the data, analysis scripts, and detailed results at https://anonymous.4open.science/r/GAI4SQA-FD5C.

Islam T. Elgendy, Robert M. Hierons, Phil McMinn

There has been a significant amount of interest regarding the use of diversity-based testing techniques in software testing over the past two decades. Diversity-based testing (DBT) technique uses similarity metrics to leverage the dissimilarity between software artefacts - such as requirements, abstract models, program structures, or inputs - in order to address a software testing problem. DBT techniques have been used to assist in finding solutions to several different types of problems including generating test cases, prioritising them, and reducing very large test suites. This paper is a systematic survey of DBT techniques that summarises the key aspects and trends of 144 papers that report the use of 70 different similarity metrics with 24 different types of software artefacts, which have been used by researchers to tackle 11 different types of software testing problems. We further present an analysis of the recent trends in DBT techniques and review the different application domains to which the techniques have been applied, giving an overview of the tools developed by researchers to do so. Finally, the paper identifies some DBT challenges that are potential topics for future work.

Zengyang Li, Xiaoxiao Qi, Qinyi Yu et al.

Context: Modern software systems (e.g., Apache Spark) are usually written in multiple programming languages (PLs). There is little understanding on the phenomenon of multi-programming-language commits (MPLCs), which involve modified source files written in multiple PLs. Objective: This work aims to explore MPLCs and their impacts on development difficulty and software quality. Methods: We performed an empirical study on eighteen non-trivial Apache projects with 197,566 commits. Results: (1) the most commonly used PL combination consists of all the four PLs, i.e., C/C++, Java, JavaScript, and Python; (2) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 83% of the projects goes to a relatively stable level; (3) more than 90% of the MPLCs from all the projects involve source files in two PLs; (4) the change complexity of MPLCs is significantly higher than that of non-MPLCs; (5) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 89% of the projects; (6) MPLCs do not show significant effects on issue reopen; (7) source files undergoing MPLCs tend to be more bug-prone; and (8) MPLCs introduce more bugs than non-MPLCs. Conclusions: MPLCs are related to increased development difficulty and decreased software quality.

Marek Malik, A. Mercando, Marek Malik

Jinzhen Yao, Jianlin Zhang, Zhixing Wang et al.

Abstract Siamese network‐based object tracking learns features of a target object marked in the first frame and that of the object in subsequent frames simultaneously and then measures similarity between two features to recognize and locate the object. Owing to their efficiency and high accuracy, Siamese networks have attracted much attention recently. However, tracking accuracy decreases significantly when there are scale changes, occlusion, and pose variations due to the way that Siamese networks estimate feature similarity. To address this issue, the authors propose a tracking algorithm, named Semantic and context features integration for robust object tracking that integrates local and global features of the object. Local features provide context information for tracking parts of the object, while global features contain semantic information for tracking the object. The authors meticulously design local and global classification and regression heads and integrate them into one uniform framework to achieve integration tracking. This method effectively alleviates low accuracy in complex scenes such as scale changes, deformation, and occlusion. Numerous experiments demonstrate that this method achieves state‐of‐art (SOTA) performance with 45 FPS on a single RTX2060 Super GPU on public tracking datasets, including VOT2016, VOT2019, OTB100, GOT‐10k, and LaSOT, and its effectiveness and efficiency is confirmed.

Luca Borzacchiello, Emilio Coppa, Camil Demetrescu

Symbolic execution is a program analysis technique that aims to automatically identify interesting inputs for an application, using them to generate program executions covering different parts of the code. It is widely used in the context of vulnerability discovery and reverse engineering. In this paper we present SENinja, a symbolic execution plugin for the BinaryNinja disassembler. The tool allows the user to perform symbolic execution analyses directly within the user interface of the disassembler, and can be used to support a variety of reverse engineering tasks.

Viktor S. Gorbatov, Igor Y. Zhukov, Vladislav V. Kravchenko et al.

The purpose of this paper is an analytical pre-project study of possible technological aspects of countering external computer attacks on critical network infrastructure. This will make it possible to specify the tasks for further resolving this problem in the aspect of developing the necessary software and hardware. The practical implementation of such tasks is an urgent and rather unconventional problem due to various factors of change in the classical concept of the network perimeter as a physical boundary of the information infrastructure, which becomes virtual and, therefore, requires the use of new approaches to the development of technical solutions. Based on statistical data on the number and quality of computer incidents, the study provides a justification for the relevance of the above problem, and gives an overview of widely used technical means for protecting the classic network perimeter, such as firewalls and systems for detecting attacks and intrusions. A comparative analysis of modern technological trends in their development, referred to in publications as «Threat Detection and Response», «Extended Detection and Response», is carried out. However, despite the powerful software and hardware functionality of these solutions, their common drawback is indicated as the lack of adequate counteraction to computer attacks with a remote mode of the user work. In this regard, the latest concept of virtual network perimeter protection, referred to by the authors as «Cybersecurity Mesh» («cybersecurity network»), is detailed. It is this methodology that seems to be the most promising for the development of appropriate technological solutions to ensure the cybersecurity of the perimeter of the critical information infrastructure. The paper might be useful to specialists working on the security of critical information infrastructure facilities, as well as to employees of educational classes in the implementation of appropriate training, retraining and advanced training programs for such specialists.