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

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Using Deep learning and Object-Oriented Metrics to Identify Critical Components in Object-Oriented Systems 23

Using Deep Learning and Object-Oriented Metrics to Identify ...

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5th World Symposium on software engineering, WSSE 2023

作者： Tete, Akpedje Touré, Fadel Badri, Mourad Software Engineering Research laboratory Department of Mathematics and Computer Science University of Quebec Trois-RivièresQC Canada

ISBN: (纸本)9798400708053

This paper aims at studying the ability of deep machine learning to predict software faults based on object-oriented metrics. This research investigated software faults from the perspective of fault-proneness, faults number and faults frequency, and used data collected from several versions of a Java open-source software system. This study relied on Chidamber and Kemerer suite of metrics as proxy to capture various software characteristics. In this study, the deep learning regression and classification results were compared to linear and logistic regressions. Auto-Encoders (AE) and Principal Components Analysis (PCA) have been used to reduce redundant information from the dataset. To evaluate the prediction ability of the models, this research used the inter-version validation strategy. The results showed that the models can achieve a significant average performance up to 89%. © 2023 Copyright held by the owner/author(s).

关键词： Principal component analysis

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Design of machine learning-Based Malware Detection Methodologies in the Internet of Things Environment 2

Design of Machine Learning-Based Malware Detection Methodolo...

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2nd international conference for Advancement in Technology, ICONAT 2023

作者： Baviskar, Pallavi V. Singh, Guddi Patil, Vijay Narendranath Kalinga University Raipur Research Department Raipur India Ph.D. Guide Kalinga University Raipur Research Department Raipur India Principal HSBPVT'S GOI Parikrama College of Engineering Kashti Shrigonda Ahmednagar Ahmednagar India

ISBN: (纸本)9781665475174

As the Internet of Things (IoT) devices become more common, so do ways to protect against cyber-attacks. Malware prevention is hard to do because there are so many ways to get infected and IoT devices don't have a lot of processing power for security software. To stop the spread of malware, it is important to be able to find it on devices. Because there are more and more devices and types of malicious software, machine learning needed to find Internet of Things devices that have been hacked. IoT gateway devices should use machine learning to analyze the behavior of IoT devices to make up for the fact that they do not have as much processing power. The goal of this research is to come up with an architecture for finding malware traffic that uses summarized statistical packet data instead of whole packet information. This idea just uses the traffic and destination addresses for each IoT device. This makes it easier to analyze a large number of devices by reducing the amount of data storage space and processing power needed. We used Isolation Forestand K-means clustering, which are both types of machine learning, to find malware traffic on the given architecture. When all of the statistical information was add up, the results were correct. After collecting statistical data from 26 different Internet of Things devices (divided into 9 categories), we found that the amount of data needed for analysis could be cut by more than 90% while still being correct. © 2023 IEEE.

关键词： Internet of things

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Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We? 38

Towards Automatically Addressing Self-Admitted Technical Deb...

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38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Mastropaolo, Antonio Di Penta, Massimiliano Bavota, Gabriele Univ Svizzera italiana USI SEART Software Inst Lugano Switzerland Univ Sannio Dept Engn Benevento Italy

ISBN: (纸本)9798350329964

Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i.e., the fact that software is released in a shape not as good as it should be, e.g., in terms of functionality, reliability, or maintainability. This paper empirically investigates the extent to which technical debt can be automatically paid back by neural-based generative models, and in particular models exploiting different strategies for pre-training and fine-tuning. We start by extracting a dateset of 5,039 Self-Admitted Technical Debt (SATD) removals from 595 open-source projects. SATD refers to technical debt instances documented (e.g., via code comments) by developers. We use this dataset to experiment with seven different generative deep learning (DL) model configurations. Specifically, we compare transformers pre-trained and fine-tuned with different combinations of training objectives, including the fixing of generic code changes, SATD removals, and SATDcomment prompt tuning. Also, we investigate the applicability in this context of a recently-available Large Language Model (LLM)-based chat bot. Results of our study indicate that the automated repayment of SATD is a challenging task, with the best model we experimented with able to automatically fix similar to 2% to 8% of test instances, depending on the number of attempts it is allowed to make. Given the limited size of the fine-tuning dataset (similar to 5k instances), the model's pre-training plays a fundamental role in boosting performance. Also, the ability to remove SATD steadily drops if the comment documenting the SATD is not provided as input to the model. Finally, we found general-purpose LLMs to not be a competitive approach for addressing SATD.

关键词： Self-Admitted Technical Debt Pre-trained models and machine learning for Code

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Investigating a NASA Cyclomatic Complexity Policy on Maintenance Risk of a Critical System 23

Investigating a NASA Cyclomatic Complexity Policy on Mainten...

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IEEE/ACM 45th international conference on software engineering - software engineering in Practice (ICSE-SEIP)

作者： Port, Dan Taber, Bill Huang, LiGuo Univ Hawaii Manoa Informat Technol Management Honolulu HI 96822 USA CALTECH Jet Prop Lab Pasadena CA 91109 USA Southern Methodist Univ Dept Comp Sci Dallas TX USA

ISBN: (纸本)9798350300376

Monte is a mission critical system used by NASA for navigation and design of deep space missions has been used in over 40 missions over the last 18 years. Its continuous, reliable operation is considered critical to the operation for over 18 ongoing missions. Recently Monte has been escalated to safety- critical software and subject to NASA software Assurance and software Safety Standard requirements. One of these requirements mandates a policy that the cyclomatic complexity (CC) for safety-critical components be under 16 or given a technically detailed explanation as to why it cannot or should not be lower. conformance to this requirement would be costly and we had doubts about its benefit and efficacy in managing our maintenance risk (defect proneness and defect repair effort). The requirement was not substantiated either empirically or in principle, and guidance in the literature for use of CC as in indicator of maintenance risk is limited and often speculative or have contradictory empirical or non-definitive results. This led us to rigorously investigate the impact the CC policy would have on the practical management of maintenance risk for Monte. The effect of CC on maintenance risk for Monte is explored using a variety of statistical methods and machine learning with aim of provide objective empirical evidence to support our decision as to what extent we will conform to the NASA CC policy of maintaining a CC under 16. This paper presents the conclusions and insights gained from this investigation. Practical questions related to the policy are addressed such as: Does higher CC associate with higher defect proneness? More effort to repair defects? If so, is there a CC after which action should be taken to reduce defect proneness and repair effort? We conclude that the impacts of CC on maintenance risk are, with high confidence, consistent with the risk management expectations of the NASA policy. We can quantify the benefit and weight it against the cost to info

关键词： cyclomatic complexity software maintenance defect proneness defect repair effort

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Fault Detection Model Using Measurement Data in Fiber Optic Internet Lines 4

Fault Detection Model Using Measurement Data in Fiber Optic ...

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4th international Informatics and software engineering conference, IISEC 2023

作者： Battal, Eray Ozkan, Gozde Savas, Kerem Cakar, Tuna COSKUNOZ Die Machine and Trade Inc. Lean and Digital Transformation Responsible Bursa Turkey Beyes Inc. Ankara Turkey MEF University Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350318036

In this study, a model has been developed to predict potential faults in advance based on performance metrics of various fiber-optic internet lines, as well as alarm (fault data) and performance measurement values from the 5 hours prior to the occurrence of the alarm. Performance metrics that vary over time have been analyzed in a time-series format based on alarm numbers, and anomaly detection methods have been used to label the data for any potential patterns that may occur in the performance metrics specific to the alarm. The labeled data was then fed into a classification model to create a model that enables to detect possible patterns in the relevant performance values for the specific fault type. The best performing model was Random Forest Classifier with accuracy and F1 scores of 0.89 and 0.84 respectively. © 2023 IEEE.

关键词： machine learning

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FORECASTING THE RANGE OF POSSIBLE HUMAN HAND MOVEMENT IN CONSUMER ELECTRONICS DISASSEMBLY USING machine learning 18

FORECASTING THE RANGE OF POSSIBLE HUMAN HAND MOVEMENT IN CON...

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ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Liao, Hao-yu Chen, Yuhao Hu, Boyi Liang, Xiao Behdad, Sara Univ Florida Environm Engn Sci Gainesville FL 32611 USA Univ Florida Ind & Syst Engn Gainesville FL 32611 USA SUNY Buffalo Civil Struct & Environm Engn Buffalo NY 14260 USA

ISBN: (纸本)9780791887233

Robotic technology can benefit disassembly operations by reducing human operators' workload and assisting them with handling hazardous materials. Safety consideration and predicting human movement is a priority in human-robot close collaboration. The point-by-point forecasting of human hand motion which forecasts one point at each time does not provide enough information on human movement due to errors between the actual movement and predicted value. This study provides a range of possible hand movements to enhance safety. It applies three machine learning techniques including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bayesian Neural Network (BNN) combined with Bagging and Monte Carlo Dropout (MCD), namely LSTM-Bagging, GRU-Bagging, and BNN-MCD to predict the possible movement range. The study uses an Inertial Measurement Units (IMU) dataset collected from the disassembly of desktop computers to show the application of the proposed method. The findings reveal that BNN-MCD outperforms other models in forecasting the range of possible hand movement.

关键词： Human Motion Prediction Gated Recurrent Unit Bayesian Neural Network Long Short-Term Memory Human-Robot Collaboration Disassembly Remanufacturing

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An Implementation of Feature Selection for Detecting LOIC-based DDoS Attack

An Implementation of Feature Selection for Detecting LOIC-ba...

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2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chen, Shih-Chieh Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

machine learning method is efficient and effective in detecting DDoS attacks, but it all begins from identifying and selecting their important features. This paper presents an implementation of feature selection for DDoS detection based on Random Forest method. In our implementation, we use a LOIC software flood DDoS requests to a target computer, then control the target to extract the features from the captured IP packets, and finally calculate their Gini feature importance and ranking for subsequent feature selection. © 2023 IEEE.

关键词： Feature Selection

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Optimizing Issue Tracking Systems using Deep learning-based Issue Classification 2

Optimizing Issue Tracking Systems using Deep Learning-based ...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and Signal Processing, PCEMS 2023

作者： Samant, Nimish Limaye, Heramba Bapat, Anurag Shinde, Shraddha Nerurkar, Amit Vidyalankar Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350310719

This research paper aims to investigate the use of text classification for automatic issue tagging in issue-tracking systems. The study focuses on the current state of issue-tracking systems and their limitations in terms of issue tagging, specifically the manual effort required to tag and categorize issues. The research describes the implementation of a text classification model for automatic issue tagging and evaluates its performance in terms of accuracy and loss. The results of this study show that the use of text classification can significantly improve the efficiency and accuracy of issue tagging in issue-tracking systems, while also providing a more efficient and user-friendly experience. The results also provide valuable insights into the design and implementation of issue-tracking systems and demonstrates the potential of deep learning to enhance the accuracy and efficiency of issue-tracking. This research also provides insights for software development teams and managers on how to use text classification techniques to improve the efficiency and effectiveness of their issue-tracking systems. © 2023 IEEE.

关键词： Efficiency

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A Review of Multi-omics and machine learning: Advancements and Applications in Cancer Research 3

A Review of Multi-omics and Machine Learning: Advancements a...

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Ayman, Hazem Bassem, Madonna Madkour, Malak Fawzi, Mariam Amr, Nada Yousry, Youmna Elgayar, Ziad Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem. © 2023 IEEE.

关键词： Gaussian distribution

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LLM Meets Bounded Model Checking: Neuro-symbolic Loop Invariant Inference 24

LLM Meets Bounded Model Checking: Neuro-symbolic Loop Invari...

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

作者： Wu, Guangyuan Cao, Weining Yao, Yuan Wei, Hengfeng Chen, Taolue Ma, Xiaoxing Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Birkbeck Univ London Sch Comp & Math Sci London England

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Loop invariant inference, a key component in program verification, is a challenging task due to the inherent undecidability and complex loop behaviors in practice. Recently, machine learning based techniques have demonstrated impressive performance in generating loop invariants automatically. However, these methods highly rely on the labeled training data, and are intrinsically random and uncertain, leading to unstable performance. In this paper, we investigate a synergy of large language models (LLMs) and bounded model checking (BMC) to address these issues. The key observation is that, although LLMs may not be able to return the correct loop invariant in one response, they usually can provide all individual predicates of the correct loop invariant in multiple responses. To this end, we propose a "query-filter-reassemble" strategy, namely, we first leverage the language generation power of LLMs to produce a set of candidate invariants, where training data is not needed. Then, we employ BMC to identify valid predicates from these candidate invariants, which are assembled to produce new candidate invariants and checked by off-the-shelf SMT solvers. The feedback is incorporated into the prompt for the next round of LLM querying. We expand the existing benchmark of 133 programs to 316 programs, providing a more comprehensive testing ground. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art techniques, successfully generating 309 loop invariants out of 316 cases, whereas the existing baseline methods are only able to tackle 219 programs at best. The code is publicly available at https://***/SoftWiser-group/***.

关键词： loop invariant program verification large language model

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