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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3288 条记录，以下是31-40 订阅

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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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Comparative Analysis of Four Metaheuristic Algorithms for Estimating Parameters in Solar Photovoltaic models 6

Comparative Analysis of Four Metaheuristic Algorithms for Es...

引用

6th international conference on Advanced Aspects of software engineering, ICAASE 2024

作者： Lakbichi, Rihab Zitouni, Farouq Limane, Abdelhadi Ferhat, Aridj Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

In the last decade, Metaheuristic Algorithms (MAs) have been widely employed to estimate parameters in photovoltaic (PV) models to maximize solar energy efficiency. this paper presents a comparative study of the performance of four novel and efficient MAs applied to solve the parameter extraction problem in three ubiquitous PV models: the Single-Diode Model (SDM), the Double-Diode Model (DDM), and the Photovoltaic Module Model (PMM). the primary objective of this study is to evaluate and compare the efficiency of these MAs and identify the most effective one based on the Root Mean Square Error (RMSE) between the simulated and the actual data. this work compares the performance of the Red-Tailed Hawk Optimizer (Rth), the One-To-One Based Optimizer (OOBO), the Sand Cat Swarm Optimization (SCSO), and the Archerfish Hunting Optimizer (AHO). the numerical results demonstrate that Rth significantly outperforms its counterparts in estimating parameters for the examined models. this exceptional performance underscores Rth's efficiency and effectiveness as a powerful tool for parameter estimation in PV models. Given these promising results, Rth stands out as a preferred method and holds the potential for further refinement and enhancement in the future. © 2024 IEEE.

关键词： Mean square error

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Predicting Momentum: A Model Based on Wavelet Neural Network and PSO Algorithm 6

Predicting Momentum: A Model Based on Wavelet Neural Network...

引用

6th international conference on Electronics and Communication, Network and Computer Technology, ECNCT 2024

作者： Li, Xinxuan Xinxuan Li School of Engineering Shantou University Guangdong Shantou515063 China

ISBN: (纸本)9798350366143

this paper presents a predictive model based on Wavelet Neural Networks (WNN) and Particle Swarm Optimization (PSO) to forecast momentum in competitive environments. Initially, data related to player performance is analyzed using various machine learning algorithms, including Random Forest, XGBoost, SVC, MLP, and Logistic Regression. these models are validated using a 5-fold cross-validation method to determine their efficacy. Following this, a WNN model is constructed to map one-dimensional input to output, utilizing 'momentum' as the key predictive feature. the model's accuracy is enhanced through the integration of PSO, which optimizes the combination of neural network models. Experimental results demonstrate that the WNN combined with PSO significantly improves prediction accuracy, making it a robust tool for forecasting in dynamic and complex systems. © 2024 IEEE.

关键词： Neural network models

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Research on Long and Short Term Heat Load Prediction of Central Heating 6

Research on Long and Short Term Heat Load Prediction of Cent...

引用

6th international conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Mu, Dongdong Sang, Zhendong Fan, Yunsheng Wang, Zheng Han, Yilin Liu, Weida Zhang, Yuxin Zhao, Xi College of Marine Electrical Engineering Dalian Maritime University Dalian China Heating Company Dalian Clean Energy Group Dalian China

ISBN: (纸本)9798350371253

In response to the heat load prediction problems of centralized heating, Various prediction models for short-term and long-term heat load prediction are summarized and verified for their applicability. Firstly, the historical data is preprocessed by removing abnormal data and normalizing the sample dataset. Secondly, models including the autoregressive integrated moving average model (ARIMA) and multivariate grey prediction model (GM(1,n)) are selected and built for predictive analysis. then, the mean square error (MSE) criterion is used to evaluate the models by calculating the corresponding MSE values for the predictive results of different models. Finally, historical data from a heating company in Dalian is used as the research object to verify the applicability of different prediction models by using actual historical production data. © 2023 IEEE.

关键词： Forecasting

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Analysis of Excitation Current in Synchronous Motors Based on Multiple Machine Learning models 6

Analysis of Excitation Current in Synchronous Motors Based o...

引用

6th international conference on Artificial Intelligence and Computer Applications, ICAICA 2024

作者： Su, Pincheng Sun, Wei Yao, Xuan School of Mechanical Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350377255

this study focuses on accurately predicting the excitation current in synchronous motors using a hybrid machine learning algorithm. Given the critical role of excitation current in maintaining motor stability and performance, reliable predictive models are essential for optimal motor control and energy efficiency. this paper compares the predictive capabilities of Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), MLP-RF and MLP-SVM. Experimental results demonstrate that the combined models outperform individual algorithms in both accuracy and robustness. the MLP-RF model, in particular, achieves the highest prediction accuracy on the validation and test sets, benefiting from MLP's adaptability and RF's ensemble averaging. Further analysis of the models' R^2 values across multiple trials reveals that the hybrid models offer more consistent predictions, making them less sensitive to variations in the data. © 2024 IEEE.

关键词： Random forests

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Smarter Project Selection for software engineering Research 20

Smarter Project Selection for Software Engineering Research

引用

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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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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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...

引用

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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Stress Prediction of Working Employees in Different Environments using Machine Learning Techniques 6

Stress Prediction of Working Employees in Different Environm...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Shanmugapriya, P. Balasubramanie, P. Dhivyaa, C.R. Senthil Raja, D. Dhivya, P. Department of CSE Kongu Engineering College Tamilnadu Erode India School of Computer Science & Engineering VIT Tamilnadu Vellore India Dept of Maths K S Rangasamy College of Technology Tamilnadu Tiruchengode India Department of CT-PG Kongu Engineering College Tamilnadu Erode India

ISBN: (纸本)9798331522667

Mental stress is a growing concern, especially among employees across sectors, affecting both well-being and job performance. Stressors like excessive workload, tight deadlines, and high employer expectations often lead to health issues such as anxiety, depression, cognitive decline, cardiovascular diseases, and suicidal thoughts. this study aims to predict employee stress using physiological and behavioral indicators. Real-time data was collected from employees in medical, teaching, and IT sectors through an online stress survey. three machine learning models, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were utilized for prediction. Among the models, the Random Forest (RF) algorithm showed the best performance with a 92% accuracy rate. the study’s findings highlight the effectiveness of real-time data and machine learning in stress prediction. A web-based application was also developed to help organizations monitor and manage employee well-being. this research demonstrates the potential of predictive analytics and machine learning to support stress management, encouraging proactive measures for a healthier work environment. © 2025 IEEE.

关键词： Adversarial machine learning

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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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