Hasil untuk "Computer software"

Nadia Ghezaiel Hammouda, Zakarya Ahmed, Ihab Omar et al.

Abstract Stratum ventilation (SV) has emerged as a promising approach for simultaneously addressing indoor thermal comfort, airflow effectiveness, and energy efficiency. Yet, most prior research considers predictive modeling, optimization, and decision-support separately, which reduces their usefulness in practice. To overcome this gap, the present study develops an integrated hybrid framework that links machine learning models, metaheuristic optimization, and multi-criteria decision-making into a unified workflow for SV enhancement. The proposed methodology unfolds in four sequential phases: (1) data preparation and statistical assessment, (2) development of predictive models using artificial neural networks (ANN) optimized through genetic algorithm (GA) and leader Harris Hawks optimization (LHHO), (3) multi-objective optimization employing NSGA-III, and (4) ranking of Pareto-optimal solutions with the VIKOR method to accommodate different operational priorities. The findings indicate that GA-assisted ANN consistently achieved superior prediction accuracy (R > 0.995) compared to LHHO-ANN. Optimal thermal comfort was obtained with supply air velocities of 1.18–1.20 m/s, supply air temperatures around 22.0–22.2 °C, and clothing insulation levels near 1.0 clo. Ventilation performance benefited from small vane angles (≤ 5°) and cooler wall surface temperatures (≤ 12 °C), while stratification was mitigated under wider vane angles (> 10°) combined with moderately higher wall surface temperatures (13–14 °C). Heating efficiency proved robust across all candidate solutions, with a consistent utilization coefficient of approximately 1.58. The VIKOR-based ranking organized the Pareto front into ten representative design scenarios, each offering a balanced trade-off among comfort, air quality, and energy use under varying preference weights. By structuring prediction, optimization, and decision-making in a single framework, this study delivers actionable strategies for tailoring SV operation in diverse settings such as office buildings emphasizing comfort, healthcare spaces requiring ventilation effectiveness, and large halls where stratification control is critical.

Mauro Marcelino, Marcos Alves, Bianca Trinkenreich et al.

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

Shree Hari Bittugondanahalli Indra Kumar, Lilia Rodrigues Sampaio, André Martin et al.

Open-source libraries are widely used by software developers to speed up the development of products, however, they can introduce security vulnerabilities, leading to incidents like Log4Shell. With the expanding usage of open-source libraries, it becomes even more imperative to comprehend and address these dependency vulnerabilities. The use of Software Composition Analysis (SCA) tools does greatly help here as they provide a deep insight on what dependencies are used in a project, enhancing the security and integrity in the software supply chain. In order to learn how wide spread vulnerabilities are and how quickly they are being fixed, we conducted a study on over 1k open-source software projects with about 50k releases comprising several languages such as Java, Python, Rust, Go, Ruby, PHP, and JavaScript. Our objective is to investigate the severity, persistence, and distribution of these vulnerabilities, as well as their correlation with project metrics such as team and contributors size, activity and release cycles. In order to perform such analysis, we crawled over 1k projects from github including their version history ranging from 2013 to 2023 using VODA, our SCA tool. Using our approach, we can provide information such as library versions, dependency depth, and known vulnerabilities, and how they evolved over the software development cycle. Being larger and more diverse than datasets used in earlier works and studies, ours provides better insights and generalizability of the gained results. The data collected answers several research questions about the dependency depth and the average time a vulnerability persists. Among other findings, we observed that for most programming languages, vulnerable dependencies are transitive, and a critical vulnerability persists in average for over a year before being fixed.

Mir Mohammad Yousuf, Shabir Ahmad Sofi

Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for classifying issues in quantum software repositories by bug type, category, severity, and impacted quality attributes, with additional focus on quantum-specific bug types. The framework applies keyword and heuristic-based techniques tailored to quantum computing. To assess its reliability, we manually classified a stratified sample of 4,984 issues from a dataset of 12,910 issues across 36 Qiskit repositories. Automated classifications were compared with ground truth using accuracy, precision, recall, and F1-score. The framework achieved up to 85.21% accuracy, with F1-scores ranging from 0.7075 (severity) to 0.8393 (quality attribute). Statistical validation via paired t-tests and Cohen's Kappa showed substantial to almost perfect agreement for bug type (k = 0.696), category (k = 0.826), quality attribute (k = 0.818), and quantum-specific bug type (k = 0.712). Severity classification showed slight agreement (k = 0.162), suggesting room for improvement. Large-scale analysis revealed that classical bugs dominate (67.2%), with quantum-specific bugs at 27.3%. Frequent bug categories included compatibility, functional, and quantum-specific defects, while usability, maintainability, and interoperability were the most impacted quality attributes. Most issues (93.7%) were low severity; only 4.3% were critical. A detailed review of 1,550 quantum-specific bugs showed that over half involved quantum circuit-level problems, followed by gate errors and hardware-related issues.

Siyu Wang, Hong Yan, Jun Wen et al.

<h4>Objective</h4>To systematically evaluate the risk factors of conversion to thoracotomy in thoracoscopic surgery (VATS) for lung cancer, and to provide a theoretical basis for the development of personalized surgical plans.<h4>Methods</h4>CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, Web of Science, and Embase databases were searched by computer from the establishment of the database to March 2024. Relevant studies on the risk factors of conversion to thoracotomy in VATS for lung cancer were searched. Two reviewers independently performed literature screening, data extraction, and quality evaluation, and Stata16.0 software was used for data analysis.<h4>Results</h4>A total of 14 studies were included in this study, with a total sample size of 10605, and a total of 11 risk factors were obtained. Mate analysis showed that, Age ≥ 65 years old [OR(95%CI) = 2.61(1.67,4.09)], male [OR(95%CI) = 1.46(1.19,1.79)], BMI(Body Mass Index) ≥ 25 [OR(95%CI) = 1.79(1.17,2.74)], tuberculosis history [OR(95%CI) = 7.67(4.25,13.83)], enlarged mediastinal lymph nodes [OR(95%CI) = 2.33(1.50,3.06)], lung door swollen lymph nodes [OR(95%CI) = 6.33(2.07,19.32)], pleural adhesion [OR(95%CI) = 2.50(1.93,3.25)], tumor located in the lung Upper lobe [OR(95%CI) = 4.01(2.87,5.60)], sleeve lobectomy [OR(95%CI) = 3.40(1.43,8.08)], diameter of tumor ≥ 3.5cm [OR(95%CI) = 2.13(1.15,3.95)] associated with lung cancer VATS transit thoracotomy.<h4>Conclusions</h4>Age ≥ 65 years old, male, BMI ≥ 25, tuberculosis history, enlarged mediastinal lymph nodes, lung door swollen lymph nodes, pleural adhesion, tumor located in the lung Upper lobe, sleeve lobectomy, diameter of tumor ≥ 3.5cm are risk factors for conversion to thoracotomy during VATS for lung cancer. Clinicians should pay attention to the above factors before VATS to avoid forced conversion due to the above factors during VATS. Due to the number and limitations of the included studies, the above conclusions need to be validated by additional high-quality studies.<h4>Trail registration</h4>The protocol was registered into the PROSPERO database under the number CRD42023478648.

Minaoar Hossain Tanzil, Junaed Younus Khan, Gias Uddin

We conducted a survey of 135 software engineering (SE) practitioners to understand how they use Generative AI-based chatbots like ChatGPT for SE tasks. We find that they want to use ChatGPT for SE tasks like software library selection but often worry about the truthfulness of ChatGPT responses. We developed a suite of techniques and a tool called CID (ChatGPT Incorrectness Detector) to automatically test and detect the incorrectness in ChatGPT responses. CID is based on the iterative prompting to ChatGPT by asking it contextually similar but textually divergent questions (using an approach that utilizes metamorphic relationships in texts). The underlying principle in CID is that for a given question, a response that is different from other responses (across multiple incarnations of the question) is likely an incorrect response. In a benchmark study of library selection, we show that CID can detect incorrect responses from ChatGPT with an F1-score of 0.74 - 0.75.

Yuchen Guo, Martin Shepperd, Ning Li

Context: Software defect prediction utilizes historical data to direct software quality assurance resources to potentially problematic components. Effort-aware (EA) defect prediction prioritizes more bug-like components by taking cost-effectiveness into account. In other words, it is a ranking problem, however, existing ranking strategies based on classification, give limited consideration to ranking errors. Objective: Improve the performance of classifier-based EA ranking methods by focusing on ranking errors. Method: We propose a ranking score calculation strategy called EA-Z which sets a lower bound to avoid near-zero ranking errors. We investigate four primary EA ranking strategies with 16 classification learners, and conduct the experiments for EA-Z and the other four existing strategies. Results: Experimental results from 72 data sets show EA-Z is the best ranking score calculation strategy in terms of Recall@20% and Popt when considering all 16 learners. For particular learners, imbalanced ensemble learner UBag-svm and UBst-rf achieve top performance with EA-Z. Conclusion: Our study indicates the effectiveness of reducing ranking errors for classifier-based effort-aware defect prediction. We recommend using EA-Z with imbalanced ensemble learning.

Songhui Yue

While AI is extensively transforming Software Engineering (SE) fields, SE is still in need of a framework to overall consider all phases to facilitate Automated Software Evolution (ASEv), particularly for intelligent applications that are context-rich, instead of conquering each division independently. Its complexity comes from the intricacy of the intelligent applications, the heterogeneity of the data sources, and the constant changes in the context. This study proposes a conceptual framework for achieving automated software evolution, emphasizing the importance of multimodality learning. A Selective Sequential Scope Model (3S) model is developed based on the conceptual framework, and it can be used to categorize existing and future research when it covers different SE phases and multimodal learning tasks. This research is a preliminary step toward the blueprint of a higher-level ASEv. The proposed conceptual framework can act as a practical guideline for practitioners to prepare themselves for diving into this area. Although the study is about intelligent applications, the framework and analysis methods may be adapted for other types of software as AI brings more intelligence into their life cycles.

Salvatore Claudio Fanni, Alessandro Marcucci, Federica Volpi et al.

Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database “AI for radiology” was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings.

Diling Liao, Cuiping Shi, Liguo Wang

Abstract In the past, convolutional neural network (CNN) has become one of the most popular deep learning frameworks, and has been widely used in Hyperspectral image classification tasks. Convolution (Conv) in CNN uses filter weights to extract features in local receiving domain, and the weight parameters are shared globally, which more focus on the high‐frequency information of the image. Different from Conv, Transformer can obtain the long‐term dependence between long‐distance features through modelling, and adaptively focus on different regions. In addition, Transformer is considered as a low‐pass filter, which more focuses on the low‐frequency information of the image. Considering the complementary characteristics of Conv and Transformer, the two modes can be integrated for full feature extraction. In addition, the most important image features correspond to the discrimination region, while the secondary image features represent important but easily ignored regions, which are also conducive to the classification of HSIs. In this study, a complementary integrated Transformer network (CITNet) for hyperspectral image classification is proposed. Firstly, three‐dimensional convolution (Conv3D) and two‐dimensional convolution (Conv2D) are utilised to extract the shallow semantic information of the image. In order to enhance the secondary features, a channel Gaussian modulation attention module is proposed, which is embedded between Conv3D and Conv2D. This module can not only enhance secondary features, but suppress the most important and least important features. Then, considering the different and complementary characteristics of Conv and Transformer, a complementary integrated Transformer module is designed. Finally, through a large number of experiments, this study evaluates the classification performance of CITNet and several state‐of‐the‐art networks on five common datasets. The experimental results show that compared with these classification networks, CITNet can provide better classification performance.

Kevin Moran, Ali Yachnes, George Purnell et al.

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.

Mohamed Soliman, Kirsten Gericke, Paris Avgeriou

Software engineers share their architectural knowledge (AK) in different places on the Web. Recent studies show that architectural blogs contain the most relevant AK, which can help software engineers to make design steps. Nevertheless, we know little about blogs, and specifically architectural blogs, where software engineers share their AK. In this paper, we conduct an exploratory study on architectural blogs to explore their types, topics, and their AK. Moreover, we determine the relevance of architectural blogs to make design steps. Our results support researchers and practitioners to find and re-use AK from blogs.

Yiming Sun, Daniel M. German, Stefano Zacchiroli

We consider the problem of identifying the provenance of free/open source software (FOSS) and specifically the need of identifying where reused source code has been copied from. We propose a lightweight approach to solve the problem based on software identifiers-such as the names of variables, classes, and functions chosen by programmers. The proposed approach is able to efficiently narrow down to a small set of candidate origin products, to be further analyzed with more expensive techniques to make a final provenance determination.By analyzing the PyPI (Python Packaging Index) open source ecosystem we find that globally defined identifiers are very distinct. Across PyPI's 244 K packages we found 11.2 M different global identifiers (classes and method/function names-with only 0.6% of identifiers shared among the two types of entities); 76% of identifiers were used only in one package, and 93% in at most 3. Randomly selecting 3 non-frequent global identifiers from an input product is enough to narrow down its origins to a maximum of 3 products within 89% of the cases.We validate the proposed approach by mapping Debian source packages implemented in Python to the corresponding PyPI packages; this approach uses at most five trials, where each trial uses three randomly chosen global identifiers from a randomly chosen python file of the subject software package, then ranks results using a popularity index and requires to inspect only the top result. In our experiments, this method is effective at finding the true origin of a project with a recall of 0.9 and precision of 0.77.

Fazal Raheman

The first week of August 2022 saw the world’s cryptographers grapple with the second shocker of the year. Another one of the four post-quantum cryptography (PQC) algorithms selected by the NIST (National Institute of Standards and Technology) in a rigorous 5-year process was cracked by a team from Belgium. They took just 62 min and a standard laptop to break the PQC algorithm to win a USD 50,000 bounty from Microsoft. The first shocker came 6 months earlier, when another of the NIST finalists (Rainbow) was taken down. Unfortunately, both failed PQC algorithms are commercially available to consumers. With 80 of the 82 PQC candidates failing the NIST standardization process, the future of the remaining two PQC algorithms is, at best, questionable, placing the rigorous 5-year NIST exercise to build a quantum-safe encryption standard in jeopardy. Meanwhile, there is no respite from the quantum threat that looms large. It is time we take a step back and review the etiology of the problem de novo. Although state-of-the-art computer security heavily relies on cryptography, it can indeed transcend beyond encryption. This paper analyzes an encryption-agnostic approach that can potentially render computers quantum-resistant. Zero-vulnerability computing (ZVC) secures computers by banning all third-party permissions, a root cause of most vulnerabilities. ZVC eliminates the complexities of the multi-layered architecture of legacy computers and builds a minimalist, compact solid-state software on a chip (3SoC) that is robust, energy-efficient, and potentially resistant to malware as well as quantum threats.

Lamia Fatma Houbaba Chaouche Ramdane, Habib Mahi, Mostafa El Habib Daho et al.

Abstract The Multiple Classifier System (or classifier ensemble) is the consensus of different clustering algorithms that can provide high accuracy for the best partition and thus overcome the constraints of conventional approaches based on single classifiers. The MCS is divided into two stages: Partition creation and partition combining. The potential benefits of this methodology in unsupervised land cover categorization utilizing synthetic, composite, and remotely sensed data are investigated in this paper. Four clustering algorithms are used for the MCS's first step, and according to the WB index, the best‐unsupervised classification is obtained. In the second stage, relabeling and, voting approaches are then applied. The MCS's experimental results outperform the individual clustering outcomes in terms of accuracy.

Vasu Kalariya, Pushpendra Parmar, Patel Jay et al.

Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.

Nicolas Tancogne-Dejean, Florian G. Eich, Angel Rubio

Abstract In this work, we performed extensive first-principles simulations of high-harmonic generation in the topological Diract semimetal Na3Bi using a first-principles time-dependent density functional theory framework, focusing on the effect of spin-orbit coupling (SOC) on the harmonic response. We also derived an analytical model describing the microscopic mechanism of strong-field dynamics in presence of spin-orbit coupling, starting from a locally U(1) × S U(2) gauge-invariant Hamiltonian. Our results reveal that SOC: (i) affects the strong-field excitation of carriers to the conduction bands by modifying the bandstructure of Na3Bi, (ii) makes each spin channel reacts differently to the driven laser by modifying the electron velocity (iii) changes the emission timing of the emitted harmonics. Moreover, we show that the SOC affects the harmonic emission by directly coupling the charge current to the spin currents, paving the way to the high-harmonic spectroscopy of spin currents in solids.

Lejun Zhang, Weijie Chen, Weizheng Wang et al.

In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.

Marcel F. Langer, Alex Goeßmann, Matthias Rupp

Abstract Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We comprehensively review and discuss current representations and relations between them. For selected state-of-the-art representations, we compare energy predictions for organic molecules, binary alloys, and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution, regression method, and hyper-parameter optimization.

J. Dongarra, Lain S. Duff, D. Sorensen et al.