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

共 2518 条记录，以下是31-40 订阅

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Enhancing Power System Protection with Prediction of Inertia Constant and Frequency Deviations using Machine Learning 9

Enhancing Power System Protection with Prediction of Inertia...

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

作者： Sharma, Satyam Shenoy, Akash Manitha, P.V. Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350377972

this paper explores how machine learning can improve power system protection by predicting critical parameters such as inertia constants and frequency deviations. In the current era, various power systems are consistently integrating renewable energy sources, hence, maintaining the stability of the system has become complex. this study analyzes two configurations i.e. a conventional grid and a microgrid. Machine Learning models are applied to predict frequency deviations and inertia variations under different operating conditions. By forecasting system responses to disturbances, the proposed approach aims to provide timely insights into potential frequency shifts and stability needs. this predictive framework aims to equip power systems with greater resilience and adaptability, addressing the challenges posed by modern grid demands and the variable nature of renewable energy sources. © 2024 IEEE.

关键词： Conventional grid Frequency Deviation Inertia constant Machine Learning Microgrid Power System Protection

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Model-Driven engineering and software Development - 9th international conference, modelsWARD 2021, and 10th international conference, modelsWARD 2022, Revised Selected Papers

Model-Driven Engineering and Software Development - 9th Inte...

引用

Model-Driven engineering and software Development - 9th international conference, modelsWARD 2021, and 10th international conference, modelsWARD 2022, Revised Selected Papers

ISBN: (纸本)9783031388200

the proceedings contain 11 papers. the special focus in this conference is on Model-Driven engineering and software Development. the topics include: Decomposable and Executable models for Verification of Real-Time Systems;comparing Goal-Oriented Analysis Techniques: A Controlled Experiment;A Methodological Framework for SPL engineering from DSML;HERO vs Zombie: Destroying Zombie Guests in Virtual Machine Environments;Dependency Graphs to Boost the Verification of SysML models;acknowledging Implementation Trade-Offs When Developing with Units of Measurement;a Digital Twin Description Framework and Its Mapping to Asset Administration Shell;preface;Managing Schema Migration in NoSQL Databases: Advisor Heuristics vs. Self-adaptive Schema Migration Strategies.

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Improving Prediction Accuracy in Drift Detection Using Random Forest in Comparing with Modified Light Gradient Boost Model 9

Improving Prediction Accuracy in Drift Detection Using Rando...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Likitha, N. Raja Nagalakshmi, J.T. Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Dept of Ece Chennai India Saveetha Institute of Medical And Technical Sciences Department of Vlsi Micro Electronics Saveetha School of Engineering Chennai India

Improving the comparison of the Random Forest model9;s predictive power in drift detection with that of the modified light gradient boost model is the ultimate goal of this suggested study endeavor. Research Tools ... 详细信息

ISBN: (纸本)9798350365092

Improving the comparison of the Random Forest model's predictive power in drift detection with that of the modified light gradient boost model is the ultimate goal of this suggested study endeavor. Research Tools and Procedures: Based on the size of the data set, 40 samples were gathered using different testing and training methods. A combination of Random Forest and a modified light gradient boost model was used to assess the rising prediction accuracy in drift detection. the accuracy was tested using these models. there are a total of 40 samples, split evenly between two groups: Random Forest the Light Gradient Boost Model with Some Adjustments. It is assumed that the G power is 80 %. Findings: the simulation's mean Random Forest accuracy was 0.71 and the Modified Light Gradient Boost Model accuracy was 0.96708. A 2 tailed significance level of 0.0 was derived, which is lower than the threshold of p © 2024 IEEE.

关键词： Deep learning

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software Defect Prediction Using Ensemble Technique 9

Software Defect Prediction Using Ensemble Technique

引用

9th international conference on Information Management and Technology, ICIMTech 2024

作者： Wicaksono, Pandu Arisaputra, Panji Yunanda, Rezky Kristian, Willy Mujhid, Almuzhidul School of Computer Science Bina Nusantara University Software Engineering Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

software defect prediction is the methodical process of identifying code segments that are likely to have problems. this is done by analyzing software metrics and using categorization algorithms. this work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. the models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. the models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. the test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. this study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms. © 2024 IEEE.

关键词： Prediction models

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Supply Chain Visualization and Optimization using Machine Learning 9

Supply Chain Visualization and Optimization using Machine Le...

引用

9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Kirtane, Akash Tiwari, Archana Lalwani, Yashwant Sood, Jai Pasha, Ayaan Hisham, Mohammad Web Beta Pvt Ltd Nagpur India Shri Ramdeobaba College of Engineering And Management Department of Electronics Engineering Nagpur India

ISBN: (纸本)9798350394474

this research paper delves into the role of quantitative analysis in enhancing e-commerce supply chains. Leveraging machine learning techniques and a comprehensive e-commerce dataset from Kaggle, this study explores the nuanced art of demand forecasting. the application of the Prophet library in Python for predictive modeling serves as a cornerstone. Furthermore, this paper showcases the practical implications through a dynamic PowerBI dashboard, emphasizing the synthesis of predictive analytics and intuitive visualization. the findings underscore the accuracy of the predictive models and their impact on decision-making within e-commerce logistics. through this research, a robust framework is unveiled, advancing the understanding of quantitative analysis in supply chains and enhancing operational efficiency and strategic decision-making in the dynamic realm of e-commerce. © 2024 IEEE.

关键词： Supply chains

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Spotting Code Mutation for predictive Mutation Testing 24

Spotting Code Mutation for Predictive Mutation Testing

引用

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

作者： Zhao, Yifan Chen, Yizhou Sun, Zeyu Liang, Qingyuan Wang, Guoqing Hao, Dan Peking Univ Key Lab HCST PKU MOE Beijing Peoples R China Peking Univ SCS Beijing Peoples R China Chinese Acad Sci Inst Software Natl Key Lab Space Integrated Informat Syst Beijing Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Mutation testing is widely used to measure the test adequacy of a project. Despite its popularity, mutation testing is time-consuming and extremely expensive. To mitigate this problem, researchers propose predictive Mutation Testing (PMT). Existing PMT approaches build classification models based on statistical program features or source code of programs to predict mutation testing results. Previous statistical feature-based PMT models need expensive overhead to collect dynamic features and neglect the rich information inherent in code text. Previous text-based PMT models extract essential code elements as input and outperform the feature-based models. However, they encode code text in a plain way. therefore, they cannot sensitively capture subtle differences in mutants and they have difficulty in capturing the correlation between mutants and tests. To address these challenges, we propose a new model, SODA. SODA uses a new learning strategy, Mutational Semantic Learning, to make our model spot code mutation and its impact on test behavior. In particular, we employ a new sampling strategy to reinforce the corresponding relationship between mutants and tests by sampling same-mutant contrastive groups. then we employ contrastive learning to make our model capture subtle differences in mutants. We conduct experiments to investigate the performance of SODA. the results demonstrate that both in the cross-project and cross-version scenarios, SODA achieves state-of-the-art classification performance (improves upon baselines by 5.32%-114.92% in kill-F1 score, 0.04%-25.54% in survive-F1 score, 4.25%-60.43% in accuracy) and has the lowest mutation score error.

关键词： contrastive learning mutation testing

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OGCP: Experimental Evaluation of Epilepsy Disease Prediction Using Optimized Gradient Classification Principle 9

OGCP: Experimental Evaluation of Epilepsy Disease Prediction...

引用

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

作者： Prasad, Himayavardhini Jagath Ramkumar, G. Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Epilepsy is a neurological disorder characterized by recurrent seizures, and accurate, real-time prediction of seizures is critical for timely interventions. this study presents an Optimized Gradient Classification Principle (OGCP) with a Hybrid Deep Learning (DL) Model for predicting epilepsy from EEG signals. the proposed model was evaluated using the Epilepsy Seizure dataset from Kaggle and compared against nine well-established models, including CNN, LSTM, GRU, ResNet50, XGBoost, and SVM. the performance of the models was evaluated based on key metrics such as accuracy, precision, F1-score, recall, inference time, and model size. the proposed model outperformed all other models, achieving an impressive accuracy of 96.89%, precision of 94.56%, and F1-score of 95.78%. the model's inference time was 43.4 ms, making it suitable for real-time applications. Additionally, the model's relatively moderate size of 57.9 MB ensures efficient deployment in memory-constrained devices. these results demonstrate the superiority of the OGCP-based Hybrid DL model for epilepsy prediction, making it a promising approach for medical applications where accuracy and real-time performance are crucial. Future work will focus on improving inference time and incorporating additional physiological signals to enhance predictive performance. © 2024 IEEE.

关键词： Prediction models

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Evaluating Generative Language models with Prompt engineering for Categorizing User Stories to its Sector Domains 9

Evaluating Generative Language Models with Prompt Engineerin...

引用

9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Alawaji, Batool Hakami, Mona Alshemaimri, Bader Qassim University Department of Computer Qassim Saudi Arabia King Saud University Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350394474

In Agile software development, user stories capture requirements through a concise, user-centric approach. Manual categorization of these stories is both labor-intensive and error-prone. this study addresses the gap in existing research by exploring the application of generative language models, specifically GPT-style models, with prompt engineering for user story *** research introduces experiments utilizing two generative language models, gpt-3.5-turbo and Llama-2-chat-hf, emphasizing innovative prompt engineering. the evaluation of gpt-3.5-turbo and Llama-2-chat-hf demonstrates commendable performance in zero-shot experiments. Notably, gpt-3.5-turbo outperforms in few-shot prompting with a higher F-score of 67.93%. the gpt-3.5-turbo showcased superior compliance with instructions compared to Llama-2-chat-hf. the study underscores the promising capability of generative language models, with prompt engineering, to automate user story categorization *** work contributes to the potential of automating user story categorization in low-resource settings, showcasing how leveraging LLMs can enhance accuracy and streamline the categorization process in software development, ultimately leading to more efficient outcomes. © 2024 IEEE.

关键词： Classification (of information)

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Estimating the Manufacturing Cost of a Metal Part from Textual and Geometric Features 9

Estimating the Manufacturing Cost of a Metal Part from Textu...

引用

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

作者： Abid, Talha Rehman Daloǧlu, Mert Yldiz, Cem Erten, Ali Erman Kaya, Kamer Dakik Software Technologies Ankara Turkey Ermetal Bursa Turkey Sabanci University Istanbul Turkey

ISBN: (纸本)9798350365887

this work presents a comprehensive approach to estimating labor hours for machining complex industrial metal parts, leveraging a rich dataset with diverse features like geometric dimensions, material properties, and operational parameters. Advanced ML models, including XGBoost, LightGBM, CNN, GBM, Random Forest, Linear Regression, Ridge, SVR, and MLP, are employed to predict labor hours. these predictions are a foundation for accurate cost estimation and are critical for efficient production planning and competitive pricing in the manufacturing sector. Our experimental study performed on a real-life metal-part dataset obtained from ERMETAL shows that the proposed LightGBM model estimates the manufacturing cost of a metal part with only 10.3% mean absolute percentage error. © 2024 IEEE.

关键词： Costs

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Smarter Project Selection for software engineering Research 20

Smarter Project Selection for Software Engineering Research

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

作者： Dey, Tapajit Loungani, Jonathan Ivers, James Carnegie Mellon Univ Software Engn Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9798400706752

Open Source software (OSS) hosting platforms like Github also contain many non-software projects that should be excluded from the dataset for most software engineering research studies. However, due to the lack of obvious indicators, researchers have to spend considerable manual effort to find suitable projects or rely on convenience sampling or heuristics for selecting projects for their research. Moreover, the diverse nature of OSS projects often poses further challenges in selecting projects aligned with study objectives, especially when the study intends to identify projects based on semantic information like intended use, which is not easy to discern solely based on the project characteristics that are available through the search APIs like Github's. Our goals are to establish a robust method of identifying software projects from the population of repositories hosted in social coding platforms and to categorize the software projects based on who the target users are and how those projects are meant to be used. Using data from 35,621 projects in the World of Code dataset, we employed a combination of machine learning techniques, including Doc2Vec and Random Forest, to identify the software projects and to categorize them as standalone applications, libraries, or plug-ins. Furthermore, our findings highlight the risks of selecting projects solely based on filtering by commonly used project criteria like the number of contributors, commits, or stars as even after using similar filtering, 16.6% of projects were found to be non-software projects. Our research should aid software engineering researchers in project selection, benefiting both industry and academia. We also envision our work inspiring further research in this domain.

关键词： Open Source Github OSS projects Application Library Plug-in categorization machine learning Doc2Vec random forest multiclass classification

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