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HOBAT: Batch Verification for Homogeneous Structural Neural Networks 38

HOBAT: Batch Verification for Homogeneous Structural Neural ...

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

作者： Li, Jingyang Li, Guoqiang Shanghai Jiao Tong Univ Sch Software Shanghai 200240 Peoples R China

ISBN: (纸本)9798350329964

The rapid development of deep learning has significantly transformed the ecology of the software engineering field. As new data continues to grow and evolve at an explosive rate, the challenge of iteratively updating software built on neural networks has become a critical issue. While the continuous learning paradigm enables networks to incorporate new data and update accordingly without losing previous memories, resulting in a batch of new networks as candidates for software updating, these approaches merely select from these networks by empirically testing their accuracy;they lack formal guarantees for such a batch of networks, especially in the presence of adversarial samples. Existing verification techniques, based on constraint solving, interval propagation, and linear approximation, provide formal guarantees but are designed to verify the properties of individual networks rather than a batch of networks. To address this issue, we analyze the batch verification problem corresponding to several non-traditional machine learning paradigms and further propose a framework named HOBAT (BATch verification for HOmogeneous structural neural networks) to enhance batch verification under reasonable assumptions about the representation of homogeneous structure neural networks, increasing scalability in practical applications. Our method involves abstracting the neurons at the same position in a batch of networks into a single neuron, followed by an iterative refinement process on the abstracted neuron to restore the precision until the desired properties for verification are met. Our method is orthogonal to boundary propagation verification on a single neural network. To assess our methodology, we integrate it with boundary propagation verification and observe significant improvements compared to the vanilla approach. Our experiments demonstrate the enormous potential for verifying large batches of networks in the era of big data.

关键词： Batch verification Neural networks Homogeneous structure Abstraction

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Towards Autonomous Testing Agents via Conversational Large Language Models 38

Towards Autonomous Testing Agents via Conversational Large L...

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

作者： Feldt, Robert Kang, Sungmin Yoon, Juyeon Yoo, Shin Chalmers Univ Technol Gothenburg Sweden Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350329964

software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized "hallucination" of LLMs can be beneficial for testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss potential limitations.

关键词： software testing machine learning large language model artificial intelligence test automation

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Singleton and Factory Design Patterns Detection Based on Features and machine learning 1

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Nacef, Abir Sehimi, Sabeur Bahroun, Sahbi Ben Ahmed, Samir Fac Math Phys & Nat Sci Tunis FST Tunis Tunisia Inst Super Etud Technol Kelibia Kelibia Tunisia Higher Inst Comp Sci ISI Ariana Tunisia

ISBN: (数字)9783031641824

ISBN: (纸本)9783031641817;9783031641824

Design patterns are essential for developing flexible, extensible, and easily maintainable applications that provide effective, scalable, reliable, and manageable software systems. The advantage of using patterns is to take advantage of best practices and experience in solving challenging tasks. Patterns have been extensively tested across different applications, and represent a necessity to guarantee the creation of high-quality software. In this paper, we extend our previous work [17] on Singleton pattern detection to propose a new method for recovering both Singleton and Factory pattern. The detection approach will begin through the use of features and supervised machine learning. In this work, we will identify a set of Factory non-standard variants, analyze them, select their relevant information in the form of features, analyze the Java program by the LSTM to extract feature values, and create a machine learning classifier to detect both Singleton and Factory patterns. To extract values of Factory features, we create structured data (containing snippets of code) to train the LSTM. On the other hand, to train the pattern classifier, we create another dataset (containing a feature combination values). We use different machine learning algorithms for creating the detector to compare their performance. The empirical results demonstrate that our detection method, can identify any Singleton or Factory implementation and distinct between them with highly precision, and recall rates. We have compared the proposed approach to similar studies, namely DPDf and GEML. The results show that the detector outperforms the state-of-the-art approaches by more than 21% on evaluated data constructed from different repositories;PMART, DPB and DPDf corpus in terms of standard mesures.

关键词： Singleton design pattern Factory pattern machine learning LSTM

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Replication and Extension of Schnappinger's Study on Human-level Ordinal Maintainability Prediction Based on Static Code Metrics 23

Replication and Extension of Schnappinger's Study on Human-l...

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27th international conference on Evaluation and Assessment in software engineering (EASE)

作者： Bertrand, Sebastien Ciappelloni, Silvia Favier, Pierre-Alexandre Andre, Jean-Marc Univ Bordeaux IMS Lab Onepoint Sud Ouest Bordeaux France Sud Ouest Onepoint Bordeaux France Univ Bordeaux IMS Lab ENSC Bordeaux INP Bordeaux France

ISBN: (纸本)9798400700446

As a part of a research project concerning software maintainability assessment in collaboration with the development team, we wanted to explore dissensions between developers and the confounding effect of size. To this end, this study replicated and extended a recent study from Schnappinger et al. with the public part of its dataset and the metrics extracted from the graph-based tool Javanalyser. The entire processing pipeline was automated, from metrics extraction to the training of machine learning models. The study was extended by predicting the continuous maintainability to take account of dissensions. Then, all experimental shots were duplicated to evaluate the overall influence of the class size. In the end, the original study was successfully replicated. Moreover, good performance was achieved on the continuous maintainability prediction. Finally, the class size was not sufficient for fine-grained maintainability prediction. This study shows the necessity to explore the nature of what is measured by code metrics, and is also the first step in the construction of a maintainability model.

关键词： software Maintainability Maintainability Prediction Ordinal Classification Expert Judgment machine learning

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Multi-source machine Reading Comprehension with Meta-learning and Adaptive Adversarial Training 35

Multi-source Machine Reading Comprehension with Meta-Learnin...

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35th international conference on software engineering and Knowledge engineering, SEKE 2023

作者： Wen, Zhe Qi, Yatao Liu, Huan Lin, Zheng Wang, Weiping Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

machine reading comprehension (MRC) methods have shown great success in many datasets, but existing methods fail to achieve satisfactory results in low-resource scenarios. In addition, existing MRC models suffer from a notable decrease in performance when confronted with scenes different from the training data. Thus, it is hard to transfer knowledge between domains. In this paper, we propose an adaptive meta-learning framework to learn and transfer the shared knowledge. The framework is based on model-agnostic meta-learning algorithm, aiming to aggregate meta-knowledge among multi-source datasets. Furthermore, for better adaptation to different target domain, we investigate an adaptive adversarial training strategy to obtain domain-specific meta-knowledge. We empirically adopt three large-size datasets as source domains and five small-size datasets as target domains, and extensive experiments show the effectiveness of our framework. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： learning algorithms

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Predicting GameFi's Daily Market Direction Using Support Vector machine 20

Predicting GameFi's Daily Market Direction Using Support Vec...

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20th international Joint conference on Computer Science and software engineering, JCSSE 2023

作者： Phumphuang, Prathan Jareevongpiboon, Wirat Thammasat University Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9798350300505

This research uses support vector machines to forecast the direction of the GameFi market on a daily basis. GameFi is a blockchain-based product that combines gaming and trading. The customer can profit from any in-game activity. machine learning is a computational method that is able to forecast the future using historical data or attribute information such as open, high, low, close, volume, and market capitalization. Like many researchers who are working to develop accurate machine learning models that can forecast the market value of stocks, commodities like gold and gas, and cryptocurrencies, the main goal of this study is to use SVM to predict the daily direction of GameFi's price. The experimental result shows that SVM's prediction performance is best at 57.6%. © 2023 IEEE.

关键词： Forecasting

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Fatigue driving detection method based on deep learning

Fatigue driving detection method based on deep learning

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2023 international conference on Optics and machine Vision, ICOMV 2023

作者： Lu, Peilong Xue, Run Li, Dingkai Ma, Zhengyi School of Cyberspace Security Zhengzhou University Henan Province China School of Software Henan University of Engineering Henan Province China

ISBN: (纸本)9781510664777

In order to avoid fatigue driving, the driver fatigue detection technology is studied by extracting facial fatigue feature parameters. Use the optimized SSD to extract facial features, use PFLD to detect key points of the face, and detect the key points and spatial attitude angles of the eyes, mouth, and head of the face;calculate the face fatigue feature parameters based on time series The matrix is input to GRU for fatigue driving detection. Compared with other 8 methods in the case of low computing power, it has a high accuracy rate and detection speed, which meets the needs of the fatigue driving detection system. © 2023 SPIE. All rights reserved.

关键词： Deep learning

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Do Code Quality and Style Issues Differ Across (Non-)machine learning Notebooks? Yes! 23

Do Code Quality and Style Issues Differ Across (Non-)Machine...

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23rd IEEE international Working conference on Source Code Analysis and Manipulation, SCAM 2023

作者： Siddik, Md Saeed Bezemer, Cor-Paul University of Alberta Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350305067

The popularity of computational notebooks is rapidly increasing because of their interactive code-output visualization and on-demand non-sequential code block execution. These notebook features have made notebooks especially popular with machine learning developers and data scientists. However, as prior work shows, notebooks generally contain low quality code. In this paper, we investigate whether the low quality code is inherent to the programming style in notebooks, or whether it is correlated with the use of machine learning techniques. We present a large-scale empirical analysis of 246,599 opensource notebooks to explore how machine learning code quality in Jupyter Notebooks differs from non-machine learning code, thereby focusing on code style issues. We explored code style issues across the Error, Convention, Warning, and Refactoring categories. We found that machine learning notebooks are of lower quality regarding PEP-8 code standards than non-machine learning notebooks, and their code quality distributions significantly differ with a small effect size. We identified several code style issues with large differences in occurrences between machine learning and non-machine learning notebooks. For example, package and import-related issues are more prevalent in machine learning notebooks. Our study shows that code quality and code style issues differ significantly across machine learning and non-machine learning notebooks. © 2023 IEEE.

关键词： machine learning

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Vision: Requirements engineering for software Development in Aged Care 31

Vision: Requirements Engineering for Software Development in...

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31st IEEE international Requirements engineering conference (RE)

作者： Grundy, John Madugalla, Anuradha McIntosh, Jennifer Tran, Truyen Monash Univ Software Syst & Cybersecur Melbourne Vic Australia Monash Univ Melbourne Vic Australia Univ Melbourne Melbourne Vic Australia Deakin Univ A2I2 Geelong Vic Australia

ISBN: (纸本)9798350326918

Technology can play a key role in enabling the aged care system to provide better care for the older adults. In the past few years we have collaborated with several industry partners working in the field of aged care. Some of these operate and maintain aged care facilities, and some provide technological solutions to aged care providers. These collaborations involved extensive requirements elicitation to identify the needs of aged care residents, carers, clinical staff and facility administrators, on projects including mobile apps, data analysis, workforce training, and smart homes. Based on these, we have come up with a vision on a number of opportunities, and exposed many significant challenges, for requirements engineering in the aged care software domain that must he addressed. We summarise some of these requirements engineering challenges and a propose a vision with a framework to help address them in next generation aged care software projects.

关键词： software for aged care ageing users adaptive user interfaces machine learning mobile apps data analysis predictive analytics smart homes

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KGR-Rainfall: Temperature-Based Rainfall Prediction in Bangladesh with Novel KGR Stacking Ensemble

KGR-Rainfall: Temperature-Based Rainfall Prediction in Bangl...

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2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Bitto, Abu Kowshir Rubi, Maksuda Akter Bijoy, Md. Hasan Imam Shuvo, Subrata Das Das, Aka Chowdhury, Amit Daffodil International University Department of Software Engineering Dhaka Bangladesh Development Daffodil International University Department of General Educational Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chattogram Bangladesh Premier University Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9781665456272

Climate change factors such as wet or dry, cold or warm seasons have a significant impact on both the economy and culture. Extreme rainfall events have historically posed a major threat to many parts of the world. In Bangladesh, during monsoon seasons, wet southern airflows from the Bay of Bengal collide with dry mainland air, causing heavy rainfall that negatively affects various socio-economic sectors. These include agriculture, food production, urban planning, energy, water resource management, fisheries, forest management, healthcare, disaster management, transportation, tourism, sports, and leisure. To address this issue, the paper proposes a machine-learning approach to forecast rainfall in Bangladesh using multiple regression models and a novel Stacked Ensemble Model (KGR Stacking). The study also investigates the relationship between rainfall and temperature. The KGR Stacking model outperforms the other 12 regression models, achieving an accuracy of 86.43% and lower error. © 2023 IEEE.

关键词： machine learning

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