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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3227 条记录，以下是41-50 订阅

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Tenet: A Flexible Framework for machine-learning-based Vulnerability Detection 2

Tenet: A Flexible Framework for Machine-Learning-based Vulne...

引用

IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Pinconschi, Eduard Reis, Sofia Zhang, Chi Abreu, Rui Erdogmus, Hakan Pasareanu, Corina S. Jia, Limin Univ Porto FEUP Porto Portugal Univ Lisbon IST Lisbon Portugal Univ Lisbon INESC ID Lisbon Portugal Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9798350301137

software vulnerability detection (SVD) aims to identify potential security weaknesses in software. SVD systems have been rapidly evolving from those being based on testing, static analysis, and dynamic analysis to those based on machine learning (ML). Many ML-based approaches have been proposed, but challenges remain: training and testing datasets contain duplicates, and building customized end-to-end pipelines for SVD is time-consuming. We present Tenet, a modular framework for building end-to-end, customizable, reusable, and automated pipelines through a plugin-based architecture that supports SVD for several deep learning (DL) and basic ML models. We demonstrate the applicability of Tenet by building practical pipelines performing SVD on real-world vulnerabilities.

关键词： framework machine learning software vulnerability detection

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Data Quality Matters: A Case Study of Obsolete Comment Detection 23

Data Quality Matters: A Case Study of Obsolete Comment Detec...

引用

45th IEEE/ACM international conference on software engineering (ICSE)

作者： Xu, Shengbin Yao, Yuan Xu, Feng Gu, Tianxiao Xu, Jingwei Ma, Xiaoxing Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Tiktok Culver City CA USA

ISBN: (纸本)9781665457019

machine learning methods have achieved great success in many software engineering tasks. However, as a data-driven paradigm, how would the data quality impact the effectiveness of these methods remains largely unexplored. In this paper, we explore this problem under the context of just-in-time obsolete comment detection. Specifically, we first conduct data cleaning on the existing benchmark dataset, and empirically observe that with only 0.22% label corrections and even 15.0% fewer data, the existing obsolete comment detection approaches can achieve up to 10.7% relative accuracy improvement. To further mitigate the data quality issues, we propose an adversarial learning framework to simultaneously estimate the data quality and make the final predictions. Experimental evaluations show that this adversarial learning framework can further improve the relative accuracy by up to 18.1% compared to the state-of-the-art method. Although our current results are from the obsolete comment detection problem, we believe that the proposed two-phase solution, which handles the data quality issues through both the data aspect and the algorithm aspect, is also generalizable and applicable to other machine learning based software engineering tasks.

关键词： Obsolete comment detection machine learning for software engineering data quality

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Teaching MLOps in Higher Education through Project-Based learning 23

Teaching MLOps in Higher Education through Project-Based Lea...

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IEEE/ACM 45th international conference on software engineering - software engineering Education and Training (ICSE-SEET)

作者： Lanubile, Filippo Martinez-Fernandez, Silverio Quaranta, Luigi Univ Bari Bari Italy Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9798350322590

Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab. In this paper, we present a project-based learning approach to teaching MLOps, focused on the demonstration and experience with emerging practices and tools to automatize the construction of ML-enabled components. We examine the design of a course based on this approach, including laboratory sessions that cover the end-to-end ML component life cycle, from model building to production deployment. Moreover, we report on preliminary results from the first edition of the course. During the present year, an updated version of the same course is being delivered in two independent universities;the related learning outcomes will be evaluated to analyze the effectiveness of project-based learning for this specific subject.

关键词： machine learning data science software engineering for AI model deployment reproducibility

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Towards Realistic SATD Identification through machine learning Models: Ongoing Research and Preliminary Results 32

Towards Realistic SATD Identification through Machine Learni...

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32nd ACM international conference on the Foundations of software engineering (FSE)

作者： Gama, Eliakim Paixao, Matheus Cortes, Mariela I. Monteiro, Lucas Univ State Ceara Fortaleza Ceara Brazil

ISBN: (纸本)9798400706585

Automated identification of self-admitted technical debt (SATD) has been crucial for advancements in managing such debt. However, state-of-the-arts studies often overlook chronological factors, leading to experiments that do not faithfully replicate the conditions developers face in their daily routines. This study initiates a chronological analysis of SATD identification through machine learning models, emphasizing the significance of temporal factors in automated SATD detection. The research is in its preliminary phase, divided into two stages: evaluating model performance trained on historical data and tested in prospective contexts, and examining model generalization across various projects. Preliminary results reveal that the chronological factor can positively or negatively influence model performance and that some models are not sufficiently general when trained and tested on different projects.

关键词： Self-admitted Technical debt Chronological Analysis machine learning

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Research on signal processing of upper limbs of three gestures

Research on signal processing of upper limbs of three gestur...

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2023 international conference on software engineering and machine learning, conf-seml 2023

作者： Yang, Rongkai Jiang, Jiajun Jiang, Zhaoling Zhu, Jiaming SWJTU-Leeds Joint School Southwest Jiaotong University Chengdu611756 China Faculty of Engineering The University of Sheffield SheffieldS10 2TN18 United Kingdom College of Engineering University of Massachusetts Amherst Amherst01003 United States Ulink College Guangzhou511466 China

There are a large number of disabled people in the world whose lives are seriously affected by the lack of upper limbs. Research on related prostheses is crucial to making these individuals’ lives as convenient as those of regular people. The association between the acts that took place and the relevant data waveforms are determined in this investigation. To lessen the impact of fluctuations in the data itself, EMG signals are obtained using the five channels' worth of data. By examining the related EMG data, preprocessing, feature extraction, and the construction of two CNN models are used to categories three gestures (scissors, rock, and cloth). Finally, the classification accuracy and loss indicate that the 2-D CNN model is better by comparison with the 1-D CNN model in 3 gesture classification. These correlation results show that the control of the prosthetic limb can realize the completion of various actions through the analysis of the corresponding data and transmission to the entity. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Gesture recognition

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Study on Automatic software Test Case Generation 5th

Study on Automatic Software Test Case Generation

引用

5th international conference on Data Science, machine learning and Applications

作者： Mulla, Nilofar Jayakumar, Naveenkumar Joshi, Shashank Godse, Deepali Bharati Vidyapeeth Deemed Be Univ Dept Comp Engn Coll Engn Pune 411043 Maharashtra India Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Bharati Vidyapeeths Coll Engn Women Pune Maharashtra India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The software development life cycle (SDLC) is incomplete without the software testing phase. It's the act of checking that a piece of software really does what it's supposed to. Test case creation is one of the testing tasks that have a major impact on the quality and speed with which the process is completed. Research into the automated production of test cases has been extensive because of the time and energy it can save over the human method of creating test cases. While most of the recommended methods are based on UML models, other publications have given a specifications-based method of creating test cases. This literature analysis focuses on automated test case generation strategies based on use case specifications and the techniques used to verify them. The analysis also highlights the ways in which the methods diverge when used to solving certain pressing problems in software testing.

关键词： software testing Automatic test case generation

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A Comparative Analysis of Techniques Used for software Fault Identification: A Survey 5th

A Comparative Analysis of Techniques Used for Software Fault...

引用

5th international conference on Data Science, machine learning and Applications, ICDSMLA 2023

作者： Panda, Radhe Shyam Sharma, Manisha Tiwari, Rajesh Bhilai Institute of Technology Durg India CMR Engineering College Hyderabad India

ISBN: (纸本)9789819780426

In the domain of the software fault prediction numerous methods have been introduced and implemented using data mining techniques and machine learning models. Nevertheless, initial and early fault prediction is big challenging to be overcome and improvised with higher classification rate of fault prediction. In order to fix this issue several software solutions and approaches are suggested by many researchers. In this paper a comparative study is done amongst all these approaches. Some software solutions for defect prediction are based on the software metrics evaluation but they are time consuming. To resolve this considerable time complexity many researchers suggested the software defect prediction model incorporating the machine learning model and got the better result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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software Project Estimation Using machine learning 2

Software Project Estimation Using Machine Learning

引用

2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Nalluri, Sowmya Sri Kumar, G A E Satish Arumalla, Dileep Kumar Auti, Vinod Kumar Vardhaman College of Engineering Electronics and Communication Hyderabad India

ISBN: (纸本)9798350308846

The major purpose of the project is to investigate a range of machine learning techniques in order to improve the accuracy of projecting costs and efforts for software development projects. Numerous strategies in software effort estimation have been investigated in order to develop models with the best accuracy. Our primary goal is to determine the important variables and factors that influence the costs and efforts necessary for software development. To solve the accuracy issues, we're building an Extreme learning machine (ELM) model that uses the Analogous Estimation technique. The performance of the model will then be compared to that of existing models such as the Constructive Cost Model (COCOMO), K-Nearest Neighbor (KNN), and Support Vector machine (SVM). This method enables us to successfully combine machine learning with the Analogous Estimation methodology, thereby boosting the precision of software project estimates for more dependable outcomes. © 2023 IEEE.

关键词： Analogous Estimation COCOMO ELM KNN machine learning SVM

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Model-Driven Quantum Federated learning (QFL) 23

Model-Driven Quantum Federated Learning (QFL)

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7th international conference on the Art, Science, and engineering of Programming (Programming)

作者： Moin, Armin Badii, Atta Challenger, Moharram Tech Univ Munich Munich Germany Univ Antwerp Antwerp Belgium Univ Reading Reading Berks England Flanders Make Lommel Belgium

ISBN: (纸本)9798400707551

Recently, several studies have proposed frameworks for Quantum Federated learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL. However, developers, in the main, are not as yet familiar with Quantum Computing (QC) libraries and frameworks. A Domain-Specific Modeling Language (DSML) that provides an abstraction layer over the underlying QC and Federated learning (FL) libraries would be beneficial. This could enable practitioners to carry out software development and data science tasks efficiently while deploying the state of the art in Quantum machine learning (QML). In this position paper, we propose extending existing domain-specific Model-Driven engineering (MDE) tools for machine learning (ML) enabled systems, such as MontiAnna, ML-Quadrat, and GreyCat, to support QFL.

关键词： model-driven engineering quantum computing federated machine learning

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Detection of Phishing in Mobile Instant Messaging using Natural Language Processing and machine learning 11

Detection of Phishing in Mobile Instant Messaging using Natu...

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11th international conference in software engineering Research and Innovation (CONISOFT)

作者： Verma, Suman Ayala-Rivera, Vanessa Portillo-Dominguez, A. Omar Natl Coll Ireland Sch Comp Dublin Ireland Technol Univ Dublin Sch Business Technol Retail & Supply Chain Dublin Ireland

ISBN: (纸本)9798350328837;9798350328844

Advancements in mobile technology makes it easier to communicate in real time, but at the cost of having a wider potential attack area for phishing. While there has been research in the field related to Email and SMS, Instant Messages lags behind. The widespread usage of instant messengers by individuals of all ages further motivates the addition of software security features in this context. This research aims to detect phishing in mobile instant messages by analysing the language of the message with the help of Natural Language Processing to detect keywords pointing towards phishing. We built the machine learning models using 3 different methods for feature extraction and 3 classification algorithms. Our tests showed that balancing the data with random oversampling increased the classifiers' performance, which were able to achieve an accuracy up to 99.2%.

关键词： Instant Messaging Social engineering Phishing Natural Language Processing Secure software engineering

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