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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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Explaining software Bugs Leveraging Code Structures in Neural machine Translation 23

Explaining Software Bugs Leveraging Code Structures in Neura...

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

作者： Mahbub, Parvez Shuvo, Ohiduzzaman Rahman, Mohammad Masudur Dalhousie Univ Dept Comp Sci Halifax NS Canada

ISBN: (纸本)9781665457019

software bugs claim approximate to 50% of development time and cost the global economy billions of dollars. Once a bug is reported, the assigned developer attempts to identify and understand the source code responsible for the bug and then corrects the code. Over the last five decades, there has been significant research on automatically finding or correcting software bugs. However, there has been little research on automatically explaining the bugs to the developers, which is essential but a highly challenging task. In this paper, we propose Bugsplainer, a transformer-based generative model, that generates natural language explanations for software bugs by learning from a large corpus of bug-fix commits. Bugsplainer can leverage structural information and buggy patterns from the source code to generate an explanation for a bug. Our evaluation using three performance metrics shows that Bugsplainer can generate understandable and good explanations according to Google's standard, and can outperform multiple baselines from the literature. We also conduct a developer study involving 20 participants where the explanations from Bugsplainer were found to be more accurate, more precise, more concise and more useful than the baselines.

关键词： software bug bug explanation software engineering software maintenance natural language processing deep learning transformers

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Text Labels Classification Model based on BERT Algorithm 4

Text Labels Classification Model based on BERT Algorithm

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4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Wu, Sai Huang, Zhiyin Feng, Hao Guangdong University of Technology School of Management Guangzhou China Guangdong University of Technology School of Electro-Mechanical Engineering Guangzhou China

ISBN: (纸本)9798350329490

Text labels are extracted from the content of texts and is a issue of natural language process, which contains multiple labels. The text labels classification aims to divide the multiple labels into only one correct category. Traditionally, researchers utilize machine learning algorithms to train an one classifier to solve the multiple labels, which may cause wrong classifier due to the machine learning can not process the relationship between texts. In this paper, we utilize pre-training of deep bidirectional transformers (BERT) to dispose text understanding issues. Initially, the pre-train process is established to capture the text representations and the initial classification is executed. Subsequently, the fine-tunning function is to optimize the parameters of BERT model including learning rate, training epochs and batch sizes. From our extensive experimental results, we can significantly conclude that our proposed method can achieve the text labels classification with reasonable computation costs and training process. © 2023 IEEE.

关键词： learning algorithms

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software engineering for Data Intensive Scalable Computing and Heterogeneous Computing

Software Engineering for Data Intensive Scalable Computing a...

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IEEE/ACM international conference on software engineering - Future of software engineering (ICSE-FoSE)

作者： Kim, Miryung UCLA Los Angeles CA 90095 USA

ISBN: (纸本)9798350324969

With the development of big data, machine learning, and AI, existing software engineering techniques must be re-imagined to provide the productivity gains that developers desire. Furthermore, specialized hardware accelerators like GPUs or FPGAs have become a prominent part of the current computing landscape. However, developing heterogeneous applications is limited to a small subset of programmers with specialized hardware knowledge. To improve productivity and performance for data-intensive and compute-intensive development, now is the time that the software engineering community should design new waves of refactoring, testing, and debugging tools for big data analytics and heterogeneous application development. In this paper, we overview software development challenges in this new data-intensive scalable computing and heterogeneous computing domain. We describe examples of automated software engineering (debugging, testing, and refactoring) techniques that target this data and compute intensive domain and share lessons learned from building these techniques.

关键词： data-intensive scalable computing heterogeneous computing big data analytics debugging testing refactoring software development tools

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Study on software Defects Prediction Model based on machine learning

Study on Software Defects Prediction Model based on Machine ...

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2022 international conference on Mathematics, Modeling, and Computer Science, MMCS 2022

作者： Ren, Wenqing Chengdu Neusoft University Chengdu China

ISBN: (纸本)9781510664593

In the rapid development of network science and technology, the software, as the basic part of the network system operation, the practical application quality directly determines the realization of the function, so the users put forward higher requirements for the software quality. According to the application situation of network system software in recent years, software defects are the main factor affecting the application quality, and the relevant detection technology is the only way before the formal promotion of software. Therefore, researchers have put forward a defect prediction scheme based on the software code, which can not only reduce the cost, but also improve the practical efficiency. This paper focuses on the understanding of the machine learning algorithm and constructing automatic and comprehensive learning models according to the software defect prediction technology, thus discovering the defects in the software. The final experimental results prove that different algorithms have different advantages in different evaluation indicators. By using these advantages and the stacking integrated learning methods in machine learning, building a prediction model with combined machine learning algorithms as the core can find defects more accurately and perfectly. © 2023 SPIE.

关键词： Defects

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An Empirical Study on Data Balancing in machine learning Based software Traceability Methods

An Empirical Study on Data Balancing in Machine Learning Bas...

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international Joint conference on Neural Networks (IJCNN)

作者： Wang, Bangchao Wang, Zihan Wan, Hongyan Li, Xingfu Deng, Yang Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Wuhan Text Univ Engn Res Ctr Hubei Prov Clothing Informat Wuhan Peoples R China Wuhan Text Univ Sch Math & Phys Sci Wuhan Peoples R China

ISBN: (纸本)9781665488679

machine learning (ML) has been widely used in trace link recovery (TLR) to reduce the manual maintenance cost of trace links by developers. However, the imbalanced distribution of valid links and invalid links seriously affects the performance of classifiers. Although a few studies have applied data balancing techniques (DBT) to ML-based TLR, none of them has systematically analyzed more effective combinations of them. Therefore, we perform an empirical study on three groups of control experiments to explore the impact of the combination of different ML methods with and without DBT on TLR efficiency. We compare the performance of supervised ML-based TLR and unsupervised ML-based TLR with and without DBT respectively. Then, we analyze the performance of the ensemble learning model (EM) with DBT on TLR. The experimental results on the 7 imbalance datasets of CoEST indicate that DBT has a positive effect on ML-based TLR. Specifically, the recall of the LR model increased by 0.5517 after combining with most DBTs on EasyClinic(ID-TC), while Tomek-link significantly improves the precision of K-Nearest Neighbor (KNN), Decision Tree (DT), LR, Support Vector machine (SVM). The precision of LR increased from 0.5036 to 1.0. BalanceRF is best at increasing recall, reaching 1.0 on 4 datasets. Moreover,the improvement degree of ML-based TLR with DBT shows differences in terms of the size of datasets and the proportion of valid links.

关键词： machine learning Data balancing software traceability software engineering

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Customer Churn Prediction Using Weight Average Ensemble machine learning Model 20

Customer Churn Prediction Using Weight Average Ensemble Mach...

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

作者： Nyoman Mahayasa Adiputra, I.N. Wanchai, Paweena College of Computing Khon Kaen University Khon Kaen Thailand

ISBN: (纸本)9798350300505

Obtaining a new customer is more expensive than predicting the churn probability of an existing customer. A high-performance model in churn prediction can help a company to reduce the cost of obtaining a new customer. Ensemble machine learning is one of the machine learning techniques that can be used in prediction problems. Many studies have shown ensemble machine learning achieves superior results. The main purpose of this study is to build a framework with several combinations of preprocessing techniques and an ensemble of two machine learning models, XGBoost and random forest. The dataset for this study is from a public dataset platform;the experiment uses two different sectors: telecom and insurance. This study achieved 0.850 F1-score in the telecom sector dataset and the insurance sector achieved 0.947 F1-score and 28 seconds in processing time. Compared with the latest work in the same dataset, our model achieved a greater effectiveness in F1-score performance and efficiency performance in dataset 1, but slower algorithm time in dataset 2. © 2023 IEEE.

关键词： machine learning

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machine learning-based Analysis and Prediction of Telecoms Customer Churn 5

Machine Learning-based Analysis and Prediction of Telecoms C...

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5th international conference on Applied machine learning, ICAML 2023

作者： Chen, Liwei Nanjing University of Posts and Telecommunications Department of Software Engineering Nanjing China

ISBN: (纸本)9798350341416

Today, in the crowded and competitive telecom market, countless telecom companies suffer from customer churn. To help telecom companies predict the potential churn rate of their customers, this paper proposes to apply machine learning algorithms to construct evaluation models. Specifically, the experiment consists of five stages: data preprocessing, feature engineering, data segmentation, model building and evaluation. Missing values are dealt with in the data preprocessing and feature engineering phases. Transformation of variables is also performed. A 70% training set and a 30% testing set are then created. The unbalance in the training set is subsequently addressed using the Synthetic Minority Oversampling Technique (SMOTE). Support vector machine (SVM) and random forest (RF) models are then constructed to predict customer churn. Finally, the prediction results of the testing set are evaluated by comparing the confusion matrix and Receiver Operating Characteristic (ROC) curves. Eventually, with accuracy, F1 score, accuracy, and Area Under Curve (AUC) values of 0.80, 0.76, 0.87, and 0.93, individually, the RF method outperforms the SVM. The experimental findings demonstrate that the RF method is capable of efficiently analyzing customer turnover data and making accurate predictions. © 2023 IEEE.

关键词： Support vector machines

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Mobile App for Kidney Patients to Provide a Comprehensive Solution to Manage Disease 4

Mobile App for Kidney Patients to Provide a Comprehensive So...

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4th international Informatics and software engineering conference, IISEC 2023

作者： Madubashana, C.P. Rupasinghe, M.R.S.N. De Silva, W.P.S. Lokesha, Weerasinghe Thamaraka, G.I. Thamali, Kelegama Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350318036

Chronic Kidney Disease (CKD) is a common and critical health problem that calls for effective disease treatment and careful kidney donor selection. This research introduces a mobile application made to help medical professionals manage CKD patients successfully and effectively. The app consists of four main parts: A module based on medical report scanning using OCR and NLP for patient summary generation, patient classification based on their health state using machine learning algorithms, a personalized chatbot specific for CKD patients to provide health references and meal plans and a donor selection module based on a tailored point schema. Python is used to create the backend of the software, Android is used for the front end, and MongoDB is used for the database. machine learning models improve several of the app's features. Results show that the components are functional, and noted shortcomings are being fixed in anticipation of future improvements. The software has the potential to revolutionize the management of CKD and the choice of kidney donors, improving patient outcomes and treatment effectiveness. © 2023 IEEE.

关键词： machine learning

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Proceedings of 2023 IEEE international conference on Data and software engineering, ICoDSE 2023

Proceedings of 2023 IEEE International Conference on Data an...

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2023 IEEE international conference on Data and software engineering, ICoDSE 2023

ISBN: (纸本)9798350381382

The proceedings contain 44 papers. The topics discussed include: analysis and the preliminary design for backend technology of disaster management information system;development of simulator for learning scrum;improving cultural objects portal application usability using user usability evaluation;a static IDE plugin to detect security hotspot for Laravel framework based web application;achieving high-level software component summarization via hierarchical chain-of-thought prompting and static code analysis;aspect-based sentiment analysis model with local sentiment aggregation for online travel reviews;automated chest x-ray report generator using multi-model deep learning approach;comparative analysis of big data utilization at vocational and productivity training centers (BBPVP) and the domestic job market;and comparison machine learning technique for grade classification of exported Cavendish banana.

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software Risk Prediction at Requirement and Design Phase : An Ensemble machine learning Approach 5

Software Risk Prediction at Requirement and Design Phase : A...

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

作者： Assefa, Yibeltal Alemneh, Esubalew Nibret, Shegaw Worku, Abebaw Kombolcha Institute of Technology Wollo University Software Engineering Ethiopia Bahir Dar Institute of Technology Bahir Dar University ICT4D Research Center Ethiopia College Natural and Computation Science Mekdela University Software Engineering Ethiopia

ISBN: (纸本)9798350328486

software development is a highly structured process that involves the creation and maintenance of a particular system, ranging from simple applications to complex enterprise software. Despite following a well-defined process, unforeseen events can occur at any stage of the SDLC that may impact the software development process, leading to losses or failures in software development. software projects inherently involve risks, and no software development project is immune to these risks. Identifying and predicting such risks accurately is a challenge in software project development. To address this challenge, this study aims to develop a software risk prediction model using homogenous ensemble machine learning algorithms. These algorithms were selected due to their proven effectiveness in handling complex datasets and their ability to achieve high prediction accuracy. We have used an experimental research methodology to develop a software risk prediction model. The methodology involved collecting datasets related to requirements and design from publicly available websites such as zenodo and Harvard education dataset. These datasets were then used to train and validate the performance of the machine learning algorithms. Our study has achieved impressive prediction scores of 98.67%, 97.3%, 96.0%, and 96.0% for the algorithms Gradient Boost, Random Forest, AdaBoost, and bagging algorithms with their homogenous decision tree respectively. Using the four different homogeneous ensemble machine learning algorithms we develop software risk predictive models. Ultimately, Gradient Boost was selected as the algorithm to construct our risk predictive model due to its superior performance and ability to handle complex data. By employing this model, software development organizations can improve their ability to identify and mitigate risks, thereby improving the quality and reliability of their software products. © 2023 IEEE.

关键词： Decision trees

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