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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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Efficiency Matters: Speeding Up Automated Testing with GUI Rendering Inference 23

Efficiency Matters: Speeding Up Automated Testing with GUI R...

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

作者： Feng, Sidong Xie, Mulong Chen, Chunyang Monash Univ Melbourne Australia Australian Natl Univ Canberra Australia

ISBN: (纸本)9781665457019

Due to the importance of Android app quality assurance, many automated GUI testing tools have been developed. Although the test algorithms have been improved, the impact of GUI rendering has been overlooked. On the one hand, setting a long waiting time to execute events on fully rendered GUIs slows down the testing process. On the other hand, setting a short waiting time will cause the events to execute on partially rendered GUIs, which negatively affects the testing effectiveness. An optimal waiting time should strike a balance between effectiveness and efficiency. We propose AdaT, a lightweight image-based approach to dynamically adjust the inter-event time based on GUI rendering state. Given the real-time streaming on the GUI, AdaT presents a deep learning model to infer the rendering state, and synchronizes with the testing tool to schedule the next event when the GUI is fully rendered. The evaluations demonstrate the accuracy, efficiency, and effectiveness of our approach. We also integrate our approach with the existing automated testing tool to demonstrate the usefulness of AdaT in covering more activities and executing more events on fully rendered GUIs.

关键词： Efficient android GUI testing GUI rendering machine learning

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Research on Malicious software Detection Based on CNN-LSTM Hybrid Model 5

Research on Malicious Software Detection Based on CNN-LSTM H...

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5th international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Qi, Xiaoxia Johar, Md Gapar Md Khatibi, Ali Tham, Jacquline Zhang, Rui Mao, Sijie Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China Management and Science University Software Engineering and Digital Innovation Centre Shah Alam Malaysia Management and Science University School of Graduate Studies Shah Alam Malaysia

ISBN: (纸本)9798350359930

The proliferation of malicious applications has increased rapidly, requiring a more effective approach than traditional methods for auditing and matching code. The behavior of malicious software provides valuable information, with the API call sequence, which represents the order in which operations are generated by both malicious and benign software in a sandbox environment, has become a crucial data source. In this paper, we propose a hybrid model that combines convolutional neural network (CNN) and long short-term memory network (LSTM) to detect and classify malware. We train and classify the model using a large dataset of API sequences called by both malicious and non-malicious software. Compared to traditional machine learning methods, our model eliminates the need for manual extraction of specific instruction calls from assembly code. Furthermore, compared to other deep learning models, hybrid model with LSTM shows significantly improved detection performance. Specifically, the CNN-LSTM model achieves a 24% and 22% increase in accuracy and at least a 16% and 14% higher F1 score compared to other models. These results demonstrate the effectiveness of our proposed model in enhancing malware detection capabilities. © 2023 IEEE.

关键词： Long short-term memory

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Two-stage image inpainting method based on transformer and Fast Fourier convolution 2

Two-stage image inpainting method based on transformer and F...

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2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Li, Changxing Chen, Zhuang Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China Chongqing University of Technology School of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798350331417

In view of the insufficient ability of the currently existing deep learning-based methods to repair image high-frequency information and the small sensory field of the traditional convolutional methods. A two-stage image repair model is proposed. First, Transformer is used to repair the image initially;second, Fourier convolution is used to construct a generative network to refine the high-frequency details of the image;finally, the repair results are input into the discriminator for training to obtain the final image repair model. The experimental results on street view datasets show that the model in the paper has better restoration results in high-frequency details as well as semantic information;compared with mainstream methods, It achieves better results in peak signal-to-noise ratio and structural similarity. © 2023 IEEE.

关键词： Generative adversarial network image inpainting multiple attention Receptive field

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JITBoost: Boosting Just-In-Time Defect Prediction using Boolean Combination of Classifiers 23

JITBoost: Boosting Just-In-Time Defect Prediction using Bool...

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23rd IEEE international conference on software Quality, Reliability, and Security, QRS 2023

作者： Shehab, Mohammed A. Hamou-Lhadj, Abdelwahab Gunda, Venkata Sai Concordia University MontrealQC Canada

ISBN: (纸本)9798350319583

Just-In-Time software Defects Prediction (JIT-SDP) plays a critical role in software engineering by enabling the early identification of potential defects before they impact system performance. This study investigates the effectiveness of Boolean Combination of Classifiers (BCC) in building effective JIT-SDP models. We propose the JITBoost framework, which leverages three BCC algorithms, namely Brute-force Boolean Combination (BBC), Iterative Boolean Combination (IBC), and Weighted Pruning Iterative Boolean Combination (WPIBC). JITBoost combines the decisions of six traditional machine learning algorithms and one deep learning algorithm. When applied to 259K commits of 34 projects, we show that JITBoost models perform better than traditional machine learning and deep learning algorithms when used individually. Specifically, JITBoost-BBC, JITBoost-IBC, and JITBoost-WPIBC achieve mean AUCs of 0.891, 0.879, and 0.886, respectively, with cross-validation. With a time-aware data-splitting approach, they achieve mean AUCs of 0.863, 0.854, and 0.857, respectively. Overall, the findings suggest that combining machine learning models within the JITBoost framework can lead to improved performance in JIT-SDP models. © 2023 IEEE.

关键词： software reliability

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14th international conference on Information, Intelligence, Systems, and Applications, IISA 2023

14th International Conference on Information, Intelligence, ...

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14th international conference on Information, Intelligence, Systems, and Applications, IISA 2023

ISBN: (纸本)9783031674259

The proceedings contain 17 papers. The special focus in this conference is on Information, Intelligence, Systems, and Applications. The topics include: An Expert System for Decision Making in the Face of Imprecise, Inconsistent and Paracomplete Data – Metaverse Security Application Using DLP;machine learning and Human Unlearning;Circumstance Evaluation Using Extreme Value Analysis on Charging Station Data: The Case of DEI Blue in Greece;explainability Spectrum Analysis of Weather Sequences in Short-Term Load Forecasting;machine learning and Thermal Imaging in Precision Agriculture;reinforcement learning Agents in Precision Agriculture;Inductive machine learning for Classifying Breast Lesions from Imbalanced MRI-B Data;Fuzzy Clustering SMOTE and Fuzzy Classifiers for Hidden Disease Predictions;digitization of Forensic engineering: Overview, Perspectives and New Challenges;supporting the Maintenance Process of Healthcare Pervasive Devices;Integrating OpenAI Chat-GPT in a University Department of Education: Main Types of Use and Preliminary Assessment Results;travelTheLake: Promoting Sustainable Lake Development Using Augmented Reality;few-Shot 1/a Anomalies Feedback: Damage Vision Mining Opportunity and Embedding Feature Imbalance;traffic Congestion Prediction: A machine learning Approach;software Defect Detection Using machine learning on Data from Open-Source Programs.

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Assessing the Explainability of Lgbm Model for Effort Estimation on ISBSG Dataset 4

Assessing the Explainability of Lgbm Model for Effort Estima...

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4th international Informatics and software engineering conference, IISEC 2023

作者： Demir, Merve Ozdes Gezici, Bahar Tarhan, Ayca Kolukisa Hacettepe University Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350318036

Recently, machine learning (ML) has become very successful, which has triggered significant developments in the field of artificial intelligence (AI) and significant improvements in the predictive performance of ML algorithms. As improved performance models have emerged, the complexity of these models has increased, and this has been at the expense of human explainability of these systems, often referred to as black boxes. As a result, a new and active research field called explainable artificial intelligence (xAI) has emerged with the aim of building models with methods that are explainable and understandable to users. In this study, we focus on the explainability of the LightGBM regressor used for effort estimation by considering size attributes. We apply the SHapley Additive exPlanations (SHAP) method, a post-hoc model agnostic method, on the ISBSG dataset to shed light on the local and global explainability of the LGBM regressor. According to the results, the outputs of the SHAP method guide the user in understanding and interpreting the LGBM model on effort estimation. We concluded that there is a consistency with the results of local and global explainability. © 2023 IEEE.

关键词： machine learning

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Research on Quality Estimation of Chinese-English Translation Based on machine learning 6

Research on Quality Estimation of Chinese-English Translatio...

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6th international conference on Computer Information Science and Application Technology, CISAT 2023

作者： Han, Yonggong Zhao, Chuangang Beijing Forestry University School of Information Science and Technology China

ISBN: (纸本)9781510668546

With the deepening of international communication, the demand for translation software is also increasing. Traditional intelligent translation equipment still suffers from poor translation quality and incoherent words. To solve relevant problems. this paper, by using machine learning method, adds contextual word vectors into the original translation process, extracts and analyzes contextual words in input speech, automatically correlates relevant words, and conducts quality estimation of translation. It is helpful to improve the automatic calibration of Chinese-English translation and improve the accuracy of intelligent speech translation. © 2023 SPIE.

关键词： machine learning

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Optical Character Recognition Technology using machine learning

Optical Character Recognition Technology using Machine Learn...

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2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Kumar, B Anand Tamilarasan, S. Kiran, Ajmeera Tejaswi, Bagepally Sree, Manikonda Geethika Dasarla, Mallikarjun MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India HKBK College of Engineering Department of Information Science and Engineering Nagawara Bengaluru India

ISBN: (纸本)9798350348217

The creation of software that is capable of performing OCR in multiple languages is the goal of this project. The primary objective of optical character recognition (OCR) software is to convert pre-existing paper documents or picture files into digital versions that can be edited. Character and word detection are the two stages that are involved in the operation of multilingual OCR software. In the case of a method that is more complex, an OCR will additionally operate on the detection of sentences in order to maintain the structure of a document. Our project's goal was to better manage some of the more complicated recognition issues, such as distinct language alphabets, by better understanding, utilising, and improving the open source Optical Character Recognizer (OCR) software. This was our project's primary objective. © 2023 IEEE.

关键词： Optical character recognition

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Anaphoric Term Classification and Resolution Model for Amharic Pronouns: Using Deep learning Technique 5

Anaphoric Term Classification and Resolution Model for Amhar...

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5th international conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Mekonnen, Siraye Kumlachew, Alemu Assefa, Yibeltal Worku, Abebaw Blue Nile College Information Technology Bahir Dar Ethiopia Bahir Dar University Bahir Dar Institute of Technology Ethiopia Institute of Technology Wollo University Software Engineering Kombolcha Ethiopia Collage of Natural Science Mekdela University Software Engineering Ethiopia

ISBN: (纸本)9798350328486

The existence of an anaphoric term in a text has an impact on different natural language processing studies like machine translation, semantics analysis, and sentiment analysis. Both written texts and verbal dialogues regularly use anaphora and confuse the readers. This study use deep learning and a machine learning approach to design an anaphoric term identification and resolution model for the Amharic language. A total amount of 4524 Amharic input sentences are collected for anaphoric term identification and resolution. The data sources for this study are Amharic textbooks, the Amharic holy bible, Amharic news, and fiction. The data is annotated by the expert annotators using annotation guidelines developed by the researcher. The data preprocessing tasks such as stop word and punctuation mark removal, normalization, POS tagging, and morphological analysis are applied. We experimented with NB and RF from machine learning algorithms and LSTM, and BiLSTM from deep learning algorithms. The accuracy of these models is 85%, 93%, 95% and 98% respectively. The outcome of the experiment demonstrates that the suggested BiLSTM model outperforms NB, RF, and LSTM. © 2023 IEEE.

关键词： Sentiment analysis

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Classification of NFR based Importance Level of User Story in Agile software Development 9

Classification of NFR based Importance Level of User Story i...

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9th international conference on Signal Processing and Communication, ICSC 2023

作者： Kumar, Bhawnesh Tiwari, Umesh K. Dobhal, Dinesh C. Department of Computer Science and Engineering Graphic Era Deemed To Be University Dehradun India

ISBN: (纸本)9798350383201

The fact that requirement engineering creates effective technology and prevents system failure is very important in the early phases of software development. The final implementation criteria and the selection of many alternative models both heavily depend on non-functional requirements. The term "non-functional requirements" (NFR) refers to a group of qualities that software must possess, such as security, dependability, performance, etc. Extracting and considering NFR from user stories in Agile software Development (ASD) can help developers deliver quality software that meets the expectations of users completely. This paper proposed the classification of NFR based importance level of user story in ASD using machine learning models. At the beginning of this work, requirement converted into user stories, then the weight of NFR type and importance level of each user story calculated and classified using machine learning models. The outcome of this work shows that random forest classifier and XGBoost are achieved the best results with an accuracy of 96.39% and decision tree classifier accuracy is 96.13%, with comparisons of K-nearest neighbor, linear discriminant analysis, support vector classifier, logistic regression, and naïve bayes. © 2023 IEEE.

关键词： Decision trees

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