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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

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Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction 20

Graph Neural Network vs. Large Language Model: A Comparative...

引用

20th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Acharya, Jagrit Ginde, Gouri Univ Calgary Calgary AB Canada

ISBN: (纸本)9798400706752

A vast number of incoming bug reports demand effective methods to identify priority and severity for bug triaging. With increased technological advancement, machine learning and deep learning have been extensively examined to address this problem. Although Large Language models (LLMs) such as Fine-tuned BERT (early generation LLM) have proven to capture context in the underlying textual data, severity and priority prediction demand additional features for understanding the relationships with other bug reports. this work utilizes the graph-based approach to model the bug reports and their other attributes, such as component, product and bug type information. It utilizes the relational intelligence of Graph Neural Network (GNN) to address the prioritization and severity assessment of bug reports in the Bugzilla bug tracking system. Initial tests on the Mozilla project dataset indicate that a project-wise predictive approach using GNNs yields higher accuracy in determining the priority and severity of bug reports compared to LLMs across multiple Mozilla projects, contributing to a notable advancement in the automation of bug severity and priority prediction tasks. Specifically, GNNs demonstrated a remarkable improvement over LLMs, increasing the priority prediction accuracy by 37% & 30% and severity prediction accuracy by 43% & 30% for Core and Firefox projects, respectively. Overall, GNN outperformed the Fine-tuned BERT (LLM) in predicting priority and severity for all the Mozilla projects.

关键词： Requirement engineering Large Language Model BERT Graph Neural Networks Natural Language Processing

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Document Classification and Key Information Extraction Using Multimodal Transformers 9

Document Classification and Key Information Extraction Using...

引用

9th international conference on Computer Science and engineering, UBMK 2024

作者： Baysan, Mehmet Selman Kizilay, Furkan Özmen, Ayşe Irem Ince, Gökhan Mdp Group Turkey Istanbul Technical University Computer Engineering Department Turkey

ISBN: (纸本)9798350365887

Companies manage and track their expenses either physically or through software applications. However, manual expense entry steps are prone to errors. Manual expense entry errors losses in terms of money, time and productivity. therefore, this study presents a novel system on the automation of document information entry with a special focus on financial documents through machine I earning techniques. the methodology involves training LayoutLM models for sequence and token classification to categorize and extract detailed information from various financial documents such a s receipts and invoices. the proposed system integrates state-of-the-art models such as LayoutLMv2, LayoutLMv3, and fastText to achieve accurate document classification a nd information extraction. the designed system was implemented and tested on various types of receipts and invoices containing financial values, using evaluation metrics such as accuracy, precision, recall, and F1-score. the capability of the proposed system to achieve high accuracy, precision and F1 scores above 90 % across various document types and in automated document processing tasks reaffirms its suitability for document processing applications. © 2024 IEEE.

关键词： Financial data processing

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Sociotechnical Dynamics in Open Source Smart Contract Repositories: An Exploratory Data Analysis of Curated High Market Value Projects 20

Sociotechnical Dynamics in Open Source Smart Contract Reposi...

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20th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Costa, Saori Paixao, Matheus Steinmacher, Igor Soares, Pamella Araujo, Allysson Allex Souza, Jerffeson Univ Estadual Ceara Fortaleza Ceara Brazil No Arizona Univ Flagstaff AZ 86011 USA Fed Univ Cariri Juazeiro Do Norte Ceara Brazil

ISBN: (纸本)9798400706752

Blockchain and Smart Contracts (SCs) have emerged as a promising avenue for organizations looking to innovate. Similar to other fields of software engineering, collaborative platforms, such as Github, are gaining attention in SCs development. Moreover, public blockchain platforms, such as Ethereum, commonly serve as a medium to deploy SCs. this ecosystem serves as the basis on which the sociotechnical phenomenon of SC development emerges. Despite the growth of research regarding SCs, there is a gap in understanding the sociotechnical factors involved in their development, specially the ones with high market value. To address this issue, we leveraged Sociotechnical theory and Data Analysis to investigate the sociotechnical dynamics in open source repositories of SCs deployed on Ethereum. To ensure suitability for our analysis, we curated a list of 16 high market value SCs deployed on Ethereum. Our research yielded four primary analyses. First, we unveiled how collaboration aspects are impacted by the deployment of SCs. Second, we explored the characteristics of contributors participating in these projects. third, we looked into commit messages to categorize commonly performed software changes. Fourth, we investigated the relationship between market metrics and SC evolution. these analyses help to deepen the understanding of sociotechnical dynamics within SC repositories, assisting organizations in designing better strategies to support their development efforts.

关键词： Smart Contracts Sociotechnical theory software Evolution Exploratory Data Analysis

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On Effectiveness of Further Pre-training on BERT models for Story Point Estimation 19

On Effectiveness of Further Pre-training on BERT Models for ...

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19th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Amasaki, Sousuke Okayama Prefectural Univ Dept Syst Engn Soja Okayama Japan

ISBN: (纸本)9798400703751

CONTEXT: Recent studies on story point estimation used deep learning-based language models. these language models were pre-trained on general corpora. However, using language models further pre-trained with specific corpora might be effective. OBJECTIVE: To examine the effectiveness of further pre-trained language models for the predictive performance of story point estimation. MEthOD: Two types of further pre-trained language models, namely, domain-specific and repository-specific models, were compared with off-the-shelf models and Deep-SE. the estimation performance was evaluated with 16 project data. RESULTS: the effectiveness of domain-specific and repository-specific models were limited though they were better than the base model they further pre-trained. CONCLUSION: the effect of further pre-training was small. Large off-the-shelf models might be better to be chosen.

关键词： effort estimation BERT further pre-training story points

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Evaluating the Performance of Turkish Automatic Speech Recognition Using the Generative AI-Based Whisper Model 9

Evaluating the Performance of Turkish Automatic Speech Recog...

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9th international conference on Computer Science and engineering, UBMK 2024

作者： Gokcimen, Tunahan Das, Bihter Das, Resul Firat University Technology Faculty Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798350365887

Automatic speech recognition (ASR) for the Turkish language faces significant challenges due to its agglutinative structure and diverse phonetic variations. In this study, we evaluate the performance of OpenAI's Whisper models of varying sizes-tiny, base, small, and medium-on Turkish ASR tasks. the experiments were designed to measure each model's training loss, validation loss, word error rate (WER), and training time. Our findings indicate that larger models, specifically the whisper-medium, achieve superior performance with the lowest validation loss and WER, albeit at the cost of longer training times. the results demonstrate a substantial improvement in Turkish ASR capabilities compared to existing models, filling a significant gap in the literature. this study not only advances the state of ASR for the Turkish language but also provides valuable insights into the trade-offs between model complexity and performance, guiding future research and applications in the field. © 2024 IEEE.

关键词： Speech recognition

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AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Severity Prediction, and Post Effects 9

AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Sev...

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9th international conference on Information Technology Research

作者： Nivasheni, B. Fernando, V. G. S. O. Srihari, M. Sarah, M. I. L. Jayasinghearachchi, Vishan Rathnayake, Samadhi Chathuranga Sri Lanka Inst Informat Technol Dept Comp Sci Malabe Sri Lanka Sri Lanka Inst Informat Technol Dept Software Engn Malabe Sri Lanka

ISBN: (纸本)9798331511425;9798331511432

this paper aims to examine the applicability of machine learning models for aiding in the diagnosis and management of arboviral illnesses, especially Dengue. the study focuses on three key objectives: Diagnosis of Dengue, prediction of prognosis of severe disease, and the identification of the post dengue effects. Multiple strategies were used to address these challenges. they are feature engineering based multiclass classification of arboviral diseases, enhancement of standard conventional machine learning algorithms for binary classification of severe Dengue and finally MLSOL enhancing ensemble model for drawing future effects of Dengue infections. the comparison of traditional models included both Random Forest, XGBoost, and also ensemble method while applying the feature selection methods of SFA and RFE. this study presents the comparison of traditional individual models and optimal ensemble method for the multiclass classification in arboviral diseases. In the case of binomial classification, the best accuracy level was 0.93 achieved by the Random Forest with RFE feature selection compared to Logistic Regression as well as the Gradient Boosting. Furthermore, the study proposed an MLSOL augmented ensemble approach to handle label imbalance problem in the dataset which in turn substantially enhanced the prediction accuracy. this approach decreased Hamming Loss to 0. 12 and increased the F-measure to 0.82, this has the capability of handling the imbalanced dataset and improving the predictive accuracy and performance. this machine learning framework offers great potential for clinical use in such aspects as early intervention and in effects following Dengue fever. It is meant to help healthcare practitioners and policy makers on how to better prevent and combat Dengue in Sri Lanka.

关键词： Dengue Fever DENV Post Dengue effects Disease epidemiology Severe Dengue Arboviral Disease

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AACEGEN: Attention Guided Adversarial Code Example Generation for Deep Code models 24

AACEGEN: Attention Guided Adversarial Code Example Generatio...

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39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Li, Zhong Zhang, Chong Pan, Minxue Zhang, Tian Li, Xuandong Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Adversarial code examples are important to investigate the robustness of deep code models. Existing work on adversarial code example generation has shown promising results yet still falls short in practical applications due to either the high number of model invocations or the limited naturalness of generated examples. In this paper, we propose AACEGEN, an attention-guided adversarial code example generation method for deep code models. the key idea of AACEGEN is to utilize the attention distributions behind deep code models to guide the generation of adversarial code examples. As such, the code elements critical for model predictions could be prioritized for exploration, enhancing the effectiveness and efficiency of adversarial code example generation. In addition, AACEGEN implements a code transformation library providing diverse semantic-preserving code transformations for various code elements, and further conducts a search under the constraint of a maximum number of allowable code transformations to generate adversarial code examples with subtlety and stealth. Our extensive experiments on 9 diverse subjects, taking into account different software engineering tasks and varied deep code models, demonstrate that AACEGEN outperforms 3 baseline approaches under comprehensive evaluation.

关键词： Adversarial Example Deep Code Model Code Transformation Search-based Testing

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Research on adaptive control strategy for distributed new energy 9

Research on adaptive control strategy for distributed new en...

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9th international conference on Energy Materials and Electrical engineering, ICEMEE 2023

作者： Zhi, Qingyu Xia, Lei Wang, Xianjun Zhang, Shilin Zheng, Ke State Grid Henan Marketing Service Center Zhengzhou China

ISBN: (纸本)9781510672765

In this paper, adaptive control strategies for distributed new energy systems are discussed in detail. the capacity of new energy sources is often affected by climate and weather conditions, so traditional control methods may not be effective in dealing with these uncertainties. this study introduces adaptive control strategies, which can be adjusted in real time according to the performance or behavior 'ic theory and method of adaptive control. Finally, an adaptive control strategy based on Model predictive Control (MPC) is proposed, and its effect is verified by simulation experiments. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Solar energy Wind energy Control systems Data modeling Adaptive control Solar radiation models Distributed computing Energy efficiency Instrument modeling Solar cells

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On Calibration of Pretrained Code models 24

On Calibration of Pretrained Code Models

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44th ACM/IEEE international conference on software engineering, ICSE 2024

作者： Zhou, Zhenhao Sha, Chaofeng Peng, Xin School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Pretrained code models have achieved notable success in the field of software engineering (SE). However, existing studies have pre-dominantly focused on improving model performance, with limited attention given to other critical aspects such as model calibration. Model calibration, which refers to the accurate estimation of predictive uncertainty, is a vital consideration in practical applications. therefore, in order to advance the understanding of model cali-bration in SE, we conduct a comprehensive investigation into the calibration of pretrained code models in this paper. Our inves-tigation focuses on five pretrained code models and four code understanding tasks, including analyses of calibration in both in-distribution and out-of-distribution settings. Several key insights are uncovered: (1) pretrained code models may suffer from the issue of over-confidence;(2) temperature scaling and label smoothing are effective in calibrating code models in in-distribution data;(3) the issue of over-confidence in pretrained code models worsens in different out-of-distribution settings, and the effectiveness of temperature scaling and label smoothing diminishes. All materi-als used in our experiments are available at https://***/queserasera22/Calibration-of-Pretrained-Code-models. © 2024 ACM.

关键词： software engineering

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Integrating Stacking Ensembles with SVM Kernels for High-Fidelity Malware Classification 9

Integrating Stacking Ensembles with SVM Kernels for High-Fid...

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9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Singh, Preet Hasija, Taniya Ramkumar, K.R. Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350377972

Malicious software, or malware, is programming code that infiltrates computer systems through security holes. this research paper presents an all-inclusive study on improving the malware detection process using ensemble-based and kernel-specific machine-learning classification techniques. Advanced cyber threats require the most advanced solutions that can effectively balance accuracy, precision, recall, and F1 score for robust malware identification. the proposed methodology combines the diversity of models, including SVM using its linear, polynomial, and radial basis function (RBF) kernels, and a stacking classifier that aggregates these base models to take advantage of the strengths of the individual components. the analysis concludes with stronger proof that the stacking classifier performs better, gaining the highest accuracy at 98.57% and retaining good precision and recall for all the metrics involved in the evaluation. this paper identifies a major need for hybrid and ensemble methods in cybersecurity and suggests how they can further increase detection rates while reducing false classifications. Future work can further integrate the adaptation towards incorporating more advanced ensemble strategies along with deep learning models and explainable AI (XAI) techniques aimed to optimize model transparency and performance into adaptability to provide effectiveness in sophisticated and evolving malware threats. © 2024 IEEE.

关键词： Android malware

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