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GlueTest: Testing Code Translation via Language Interoperability 40

GlueTest: Testing Code Translation via Language Interoperabi...

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40th international conference on software Maintenance and Evolution

作者： Abid, Muhammad Salman Pawagi, Mrigank Adhikari, Sugam Cheng, Xuyan Badr, Ryed Wahiduzzaman, Md Rathi, Vedant Qi, Ronghui Li, Choiyin Liu, Lu Naidu, Rohit Sai Lin, Licheng Liu, Que Palak, Asif Zubayer Haque, Mehzabin Chen, Xinyu Marinov, Darko Dutta, Saikat Cornell Univ Ithaca NY 14850 USA Indian Inst Sci Bangalore Karnataka India Islington Coll Kathmandu Nepal Dickinson Coll Carlisle PA 17013 USA Univ Illinois Champaign IL 61820 USA BRAC Univ Dhaka Bangladesh Adlai E Stevenson High Sch Lincolnshire IL USA Wuhan Univ Wuhan Hubei Peoples R China Po Leung Kuk Ngan Po Ling Coll Hong Kong Peoples R China Univ Washington Seattle WA 98195 USA Univ Calif Berkeley Berkeley CA 94720 USA Zhejiang Univ Hangzhou Zhejiang Peoples R China Univ Shanghai Sci & Technol Shanghai Peoples R China Univ Dhaka Dhaka Bangladesh

ISBN: (纸本)9798350395693;9798350395686

Code translation from one programming language to another has been a topic of interest for academia and industry for a long time, and has recently re-emerged with the advent of Large Language models (LLMs). While progress has been made in translating small code snippets, tackling larger projects with intricate dependencies remains a challenging task. A significant challenge in automating such translations is validating the resulting code. Translating existing tests to the target language can introduce errors, yielding potentially misleading quality assurance even when all the translated tests pass. We propose the idea of testing the translated code using the existing, untranslated tests written in the original programming language. the key to our idea is to leverage language interoperability to run code written in two different languages together. this partial translation approach offers two main benefits: (1) the ability to leverage original tests for validating translated code, not only from the project being translated but also from the clients using this project, and (2) the continuous maintainability and testability of the project during translation. We evaluate our approach by translating from Java to Python two popular Java libraries, Apache Commons CLI and Apache Commons CSV, with 1209 lines of code (in 22 Java files) and 860 lines of code (in 10 Java files), respectively. Our implementation uses Oracle's GraalVM framework for language interoperability. We successfully validate the translation using the original Java tests, not just from the CLI and CSV libraries themselves but also from client projects of these libraries (30 for CLI and 6 for CSV). Our approach is the first to systematically and semiautomatically validate translations for such non-trivial libraries.

关键词： Code Translation software Testing GlueTest

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Design of Brush Plating Repair Device for Big Hole of Connecting Rod of Automobile 6

Design of Brush Plating Repair Device for Big Hole of Connec...

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2022 6th international conference on Electrical, Mechanical and Computer engineering, ICEMCE 2022

作者： Qiu, Peng Li, Wei Department of Mechanical Engineering Xijing University Shaanxi province Xi'an710123 China

Brush plating technology has been applied to the surface repair and strengthening of parts and achieved remarkable benefits, so it can be used as a part remanufacturing method. this graduation project aims to design a brush plating repair device for the big end hole of an automobile connecting rod in the field of automobile technology application, including brush plating power supply, electroplating pen, plating solution, and brush plating auxiliary equipment, so as to realize the clamping and rotation of the workpiece, the clamping of plating pen and the axial and radial movement of plating pen relative to the workpiece. the three-dimensional models of parts and parts are established by modeling software and installed. the equipment can greatly improve the maintenance efficiency and product quality of vehicle connecting rods, obtain a safer production process, and is suitable for mass reproduction of vehicle connecting rods. © Published under licence by IOP Publishing Ltd.

关键词： Auxiliary equipment

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TauLiM: Test Data Augmentation of LiDAR Point Cloud by Metamorphic Relation 22

TauLiM: Test Data Augmentation of LiDAR Point Cloud by Metam...

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44th ACM/IEEE international conference on software engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Lin, Ju Liu, Jiawei Zhang, Quanjun Zhang, Xufan Fang, Chunrong Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781665495981

With the rapid development of object detection in deep learning (DL), applications on LiDAR point clouds have received much attention, such as autonomous driving. To verify the robustness of object detection models by testing, large amounts of diversified annotated LiDAR point clouds are required to be used as test data. However, considering the sparseness of objects, the diversity of the existing point cloud dataset is limited by the number and types of objects. therefore, it is important to generate diversified point clouds by test data augmentation. In this paper, we propose a tool for LiDAR point cloud via test data augmentation, named TauLiM. A well-designed metamorphic relation (MR) [1] is proposed to augment point clouds while maintaining their physical characteristic of LiDAR. TauLiM is composed of three modules, namely point cloud configuration, coordinate filtering, and object insertion. To evaluate our tool, we employ experiments to compare the ability of testing between the existing dataset and the augmented one. the result shows that TauLiM can effectively augment diversified test data and test the object detection model. the video of TauLiM is available at https://***/watch?v=9S6xpRbbhtQ and TauLiM can be used at http://1.13.193.98:2601/.

关键词： Data Augmentation software Testing LiDAR Point Cloud Test Data Augmentation

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Enhancing Statistical Analysis with Markov Chain models Using a Shiny R Interface 6th

Enhancing Statistical Analysis with Markov Chain Models Usin...

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6th international conference on communication and computational technologies, ICCCT 2024

作者： Torres-Cruz, Fred Coaquira-Flores, Evelyn Eliana Canqui-Flores, Bernabé Ibañez-Quispe, Vladimiro Coyla-Idme, Leonel Postgraduate Unit of Statistics and Computer Engineering Faculty of Statistics and Computer Engineering Universidad Nacional del Altiplano de Puno P.O. Box 291 Puno Peru

ISBN: (纸本)9789819774227

this study demonstrates the expansive utility of Markov chains in statistical modeling, emphasizing their role in simulating complex systems within diverse fields, including engineering, economics, biology, and computer science. We present an innovative integration of Markov chain theory to predict the future states of dynamic systems and introduce ModerCMarkov, a novel web application designed in R Studio using Shiny packages. this application leverages Markov chains for forecasting outcomes from varied database typologies. Our comprehensive evaluation of ModerCMarkov assesses its processing speed and predictive accuracy across multiple databases, varying in scope and complexity. the results highlight the application's robustness, evidenced by its rapid processing capabilities and precise predictions. Furthermore, our research utilizes the Markov chain approach to identify critical nodes within key variables, enhancing our understanding of these systems. ModerCMarkov emerges as a powerful tool for intricate analysis and modeling of complex variable databases, offering significant contributions to multidisciplinary research endeavors. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Markov chains

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Revolutionizing Alzheimer's Disease Prediction Using EfficientNetB6 10

Revolutionizing Alzheimer's Disease Prediction Using Efficie...

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10th international conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Praneeth, V Sai Gowtham, Naraganti Ramachandran, Siva Jansi, R. Srm Institute of Science and Technology College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350350951

Alzheimer's disease (AD) presents a pressing challenge globally, demanding the development of accurate predictive models for early diagnosis and intervention. In this research, we propose a novel prediction framework harnessing deep learning methodologies, specifically employing the advanced EfficientNetB6 algorithm, on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset comprising approximately 20,000 MRI images. Our aim is to differentiate between MRI images indicating normal cognitive function and those signaling AD progression, with a target of achieving 97.78% accuracy and a 98.21% F1 score. Moreover, our model categorizes images into various stages, including mild cognitive impairment (MCI), facilitating early identification. the proposed model's high accuracy and precision hold the potential to transform early detection efforts, allowing for timely interventions and thereby improving patients' quality of life. In addition to leveraging the EfficientNetB6 model for image feature extraction and LSTM networks for sequential data analysis, the proposed hybrid architecture incorporates dense layers to facilitate feature fusion and abstraction. the dense layers enable the model to combine the extracted image and sequential features, capturing complex relationships between different modalities and enhancing the model's discriminative power. By integrating dense layers, the model gains the flexibility to learn higher-level representations, further refining the extracted features for improved classification performance. this holistic approach, combining convolutional, recurrent, and dense layers, offers a comprehensive solution for multi- modal data analysis, showcasing the potential of deep learning techniques in addressing complex real-world problems. © 2024 IEEE.

关键词： Neuroimaging

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Deep Learning based Abstractive text Summarization: A Survey 6

Deep Learning based Abstractive text Summarization: A Survey

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Kiran, G. Uday Gandi, Ramakrishna Lavanya, M. Desale, Girish Bhagwant Rao, Ch. Upendar Reddy, B. Veerasekhar B V Raju Institute of Technology Narsapur India Department of Computer Science & Engineering Madanapalle Institute of Technology & Science India Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science IT JET'S Z. B. Patil College M.S. Dhule India Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798350369748

the explosion in the amount of textual data in the last several years has created an abundant source of resources for data extraction and analysis. It is necessary to summarize this data in order to access relevant information in a fair amount of time. Here we take a look at some of the more current methods for summarizing abstractive texts with deep learning models. A hotspot for research in the field of Natural Language Processing (NLP) is summarization, the process of condensing a document without losing any of its significance. You can classify summarization methods as either "extractive" or "abstractive" depending on whether they use natural language processing to construct new phrases or keep the exact ones from the source text. Extensive research has been conducted on extractive summarization, and the issue has now attained maturity. At this point, the focus of the study is on providing an abstract summary. Abstractive Text Summarization (ATS) is a tough and complicated operation because of the inherent complexity of the natural language content. Researchers can get a thorough overview of Deep Learning based ATS in this work. this paper provides a comprehensive survey of deep learning models employed for abstractive summarization, delving into their architectures and methodologies. Furthermore, it scrutinizes prevalent datasets utilized for training and evaluation purposes, offering insights into their characteristics and suitability for benchmarking. the study evaluates the performance of various abstractive summarization methods on these datasets, shedding light on their effectiveness and limitations. Lastly, emerging themes in the field are discussed, along with unresolved challenges, aiming to foster deeper understanding and advancement in DL-based abstractive text summarization among researchers. © 2024 IEEE.

关键词： Contrastive Learning

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Cotton leaf disease prediction using VGG16 and RESNET50 6

Cotton leaf disease prediction using VGG16 and RESNET50

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Srivastava, Animesh Rawat, Bhupender Singh Kumar, Gautam Bhatnagar, Vivek Garg, Navin Department of Computer Science & Engineering Graphic Era Hill University Dehradun India College of Smart Computing COER University Roorkee India School of Computing Graphic Era Hill University Haldwani Campus Uttarakhand Nainital India Mullana Ambala India Uttarakhand Dehradun India

ISBN: (纸本)9798350369748

the farming business is a vital component of every economy, and its success is directly proportional to the yields of individual farmers. the micro-level illnesses that manifest themselves throughout the development of a plant often have an effect on the production of the plant under consideration. the presence of a cotton plant disease is a significant cause for worry because of the detrimental impact it has on crop output. Diagnostic procedures for these disorders need to be carried out on a consistent and accurate basis. Without this, there is a possibility that its output may suffer a major decline, which will eventually have an impact on the farmer economy. Diagnoses made manually take a significant amount of time. Consequently, this study is to make use of the advantages of deep learning to automate the diagnosis of disease in cotton plants. this study investigates the impact of deep learning on a selection of pre-trained models, including VGG-16 and ResNet50, and also proposes a hybrid method to predict cotton leaf disease. According to the examination of the results, the proposed model scored the greatest accuracy of 99.25% when compared to other CNN architectures that had been trained. Several criteria, including Precision, Recall, and F1 score, are used to measure the performance of the model. © 2024 IEEE.

关键词： Plant diseases

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Two-stage thai Misspelling Correction based on Pre-trained Language models 20

Two-stage Thai Misspelling Correction based on Pre-trained L...

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

作者： Pankam, Idhibhat Limkonchotiwat, Peerat Chuangsuwanich, Ekapol Chulalongkorn University Computer Engineering Bangkok Thailand Vidyasirimedhi Institute of Science and Technology Information Science and Technology Rayong Thailand

ISBN: (纸本)9798350300505

thai spelling detection and correction is a task to detect and correct mistakes in natural language. the implementation of an edit distance and dictionary mapping to identify and correct misspelled words is a widely used method for addressing this challenge in the thai language. However, this technique excludes contextualization from the input and can yield incorrect answers. In this paper, we propose a two-stage framework that leverages pre-trained language models, such as WangchanBERTa, for correcting thai misspellings. Our proposed method consists of a model for misspelling detection based on the token prediction task and a correction model based on masked prediction. Additionally, character edit distance (CED) can be used as a post-processing step to help improve the correction results. Our experiments were conducted on two standard datasets for thai misspellings, namely UGWC and VISTEC-TPth-2021. On the UGWC data, our model can help correct 3,867 out of the 6,399 misspelled words (60.43%), which is higher than the baseline's rate of 41.75%.11https://***/bookpanda/Two-stage-thai-Misspelling-Correction-Based-on-Pre-trained-Language-models © 2023 IEEE.

关键词： Computational linguistics

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Gradient Boosting-Based predictive Click Fraud Detection Using Manifold Criterion Variable Elimination 6th

Gradient Boosting-Based Predictive Click Fraud Detection Usi...

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proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2023

作者： Singh, Lokesh Sisodia, Deepti Taranath, N.L. Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India

ISBN: (纸本)9783031382956

Online advertising models are vulnerable to click fraud, which occurs when an individual or a group repeatedly clicks on an online advertisement with the intent to generate illegitimate clicks and make money from the advertiser. In machine learning-based approaches for detecting click fraud, the performance of the models can be affected by the presence of collinear, redundant, and least significant features in the dataset. these types of features can lead to overfitting, where the model becomes too complex and fails to generalize well to new data. therefore, a Manifold Criterion Variable Elimination method is proposed in this work to select significant features utilizing the potential of six filter-based feature selection techniques for the discrimination of fraud and genuine publishers. Experimentations are conducted on the online advertisement user click dataset in two modes, first considering all extracted features and second considering only selected features. An extraction of 103 statistical features from the user-click dataset is performed for each class instance labelled with OK, Fraud and Observation. the Manifold Criterion Variable Elimination method selects the top 15 most relevant features. Individual and ensemble learning models are trained with selected feature-set and tuned parameter values. the performances of learners are evaluated using standard evaluation measures. the results demonstrated that, in general, the performance of all learners improved with the selected feature set. Particularly, the Gradient Tree Boosting (GTB) ensemble model performed superiorly by improving the weak learners by minimizing the model's loss via merging the weak learners into a strong one iteratively. © 2023, IFIP international Federation for Information Processing.

关键词： Feature Selection

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Pioneering Minimalist Speech Analysis through Optimized Spectral Features Machine Learning models 6

Pioneering Minimalist Speech Analysis Through Optimized Spec...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Harsha Vardhini, P.A. Shiva Prasad, S. Vishnu Sai, M.H.S. Santoshi, Ch Konduru, Dhanush Vignan Institute of Technology and Science Department of ECE Telangana Deshmukhi India Telangana India Matrusri Engineering College Department of CSE Telangana India Integrated Software Engineering VIT Department of CSE Tamilnadu Vellore India

ISBN: (纸本)9798350306613

In the contemporary landscape, speech processing stands as a cornerstone across diverse applications, with gender identification emerging as a pivotal task in numerous domains, including law enforcement for swift resolution of criminal cases. the discernment of gender from speech recordings, especially in languages such as Telugu, holds paramount importance given its widespread usage and cultural significance in South India. this paper addresses the imperative need for robust gender identification methodologies tailored specifically for Telugu speech. Leveraging the rich heritage of Telugu as one of the most historically significant and widely spoken Dravidian languages, our study delves into the application of sophisticated statistical models, namely Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM), to unravel the gender nuances embedded within Telugu speech patterns. To facilitate this identification task, we meticulously curated a bespoke dataset, drawing from diverse sources to ensure representativeness and reliability. Our methodology encompasses an array of advanced feature extraction techniques, including Mel Frequency Cepstral Coefficient (MFCC), augmented with first and second-order differentials, and Zero Crossing Rate (ZCR). Moreover, to streamline the analysis and optimize accuracy, we employ Principal Component Analysis (PCA) for feature reduction, thus enhancing computational efficiency without compromising on discernment accuracy. the efficacy of our approach is substantiated through rigorous evaluation and comparison analyses, wherein we meticulously assess the performance of both original and reduced feature sets. Our findings underscore the effectiveness of our methodology in achieving notable accuracy levels, thereby underscoring its potential for real-world applications in gender identification from Telugu speech data. © 2024 IEEE.

关键词： Machine Learning models MFCC PCA speech Analysis ZCR

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