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

共 3232 条记录，以下是201-210 订阅

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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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Development of a script to aggregate document inspection comments across different platforms to GitHub in a software development PBL 35

Development of a script to aggregate document inspection com...

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35th IEEE international conference on software engineering Education and Training, CSEE and T 2023

作者： Sato, Oh Hazeyama, Atsuo Tokyo Gakugei University Graduate School of Education Tokyo Japan Tokyo Gakugei University Department of Information Science Tokyo Japan

ISBN: (纸本)9798350322026

This study regards inspections conducted in project-based learning (PBL) for software development as learning feedback. To solve the problem that inspection comments are distributed separately across platforms, which is an obstacle to classification of comments, we develop a script, Comment Gatherer, which aggregates inspection comments between GitHub and Figma. Then, we describe the concept of a support application that enables the automatic classification of inspection comments by machine learning. © 2023 IEEE.

关键词： Inspection

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Some Investigations of machine learning Models for software Defects 23

Some Investigations of Machine Learning Models for Software ...

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

作者： Bhutamapuram, Umamaheswara Sharma Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal Andhra Pradesh India

ISBN: (纸本)9798350322637

software defect prediction (SDP) and software defect severity prediction (SDSP) models alleviate the burden on the testers by providing the automatic assessment of a newlydeveloped program in a short amount of time. The research on defect prediction or defect severity prediction is primarily focused on proposing classification frameworks or addressing challenges in developing prediction models;however, the primary yet significant gap in the literature is interpreting the predictions in terms of project objectives. Furthermore, the literature indicates that these models have poor predictive performance. In this thesis, we investigate the use of a diversity-based ensemble learning mechanism for the cross-project defect prediction (CPDP) task and self-training semi-supervised learning for the software defect severity prediction, respectively, for obtaining better prediction performances. We also propose a few project-specific performance measures to interpret the predictions in terms of project objectives (such as a reduction in expenditure, time, and failure chances). Through the empirical analysis, we observe that (1) the diversity-based ensemble learning mechanism improves the prediction performance in terms of both the traditional and proposed measures, and (2) the self-training semi-supervised learning model has a positive impact on predicting the severity of a defective module. Once a potential prediction model is developed, any software organisation may utilise its services. How can an organisation showcase their trust in the developed prediction model? To this end, we investigate the feasibility of SDP models in real-world testing environments by providing proofs using the probabilistic bounds. The proofs summarised show that even if the prediction model has a lower failure probability, the probability of obtaining fewer failures in SDP-tested software than in similar but manually tested software is still exponentially small. This result enables the researchers

关键词： software Defect Prediction Cross-Project Defect Prediction software Defect Severity Prediction software Reliability Performance Measures Feasibility Study

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WeatherPon: A Weather and machine learning-Based Coupon Recommendation Mechanism in Digital Marketing 3

WeatherPon: A Weather and Machine Learning-Based Coupon Reco...

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3rd IEEE international conference on software engineering and Artificial Intelligence, SEAI 2023

作者： Liu, Liyuan Han, Meng Saint Joseph's University PhiladelphiaPA19063 United States Zhejiang University Binjiang Institute of Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350337952

machine learning has become an innovative technology tool to improve coupon recommendation systems in digital marketing. Users' information, coupon content, and customers' shopping history are the main factors to predict revenues, resolve promotions, and adjust distribution in past coupon recommendation models. However, weather is a critical factor that can affect human activities in daily life but has barely been awarded in previous studies. Many studies observed that weather change could impact human mood, behavior, and economics. Therefore, we proposed a weather and machine learning-based coupon recommendation mechanism named "WeatherPon."We employ real-time weather data to optimize coupon recommendation systems. Considering the non-linear relationship and multiple uncertainties, we utilize the deep neural networks and use Shapley Value to interpret the black-box model. The WeatherPon is a coupon recommendation mechanism not only improves accuracy but also helps enterprises achieve automated marketing solutions in real-time. © 2023 IEEE.

关键词： Recommender systems

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Advances in Assuring Artificial Intelligence and machine learning Development Lifecycle and Their Applications in Aviation 5

Advances in Assuring Artificial Intelligence and Machine Lea...

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5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Shuaia, Huiying Wang, Jiwen Wang, Anqi Zhang, Ran Yang, Xin Aircraft Design and Research Institute of COMAC Shanghai China

ISBN: (纸本)9798350357738

The use of Artificial Intelligence (AI) and machine learning(ML) in aviation has attracted attention from regulators. In order to prepare a means of compliance for the certification of trustworthiness AI within safety critical systems, this article reviews recent developments of aeronautical standard assuring AI development lifecycle and highlights three building blocks comprising the certification framework applicable to complex systems embedding ML components. To understand the challenges emerging during the development of ML models from the software engineering perspective, this article then presents the research of applying ML workflow in the development lifecycle of an intelligent Visual Guidance System(VGS) which is intended to guide the approach and landing in an emergency operation, by focusing on understanding how aircraft system and software designers could benefit from adapting the traditional engineering process to the ML-based approach and the necessary accommodations to support AI-enabled product certification. Finally, the practices of applying ML based approach are summarized, and the next steps of the research are given. © 2023 IEEE.

关键词： machine learning

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Achieving Last-Mile Functional Coverage in Testing Chip Design software Implementations 23

Achieving Last-Mile Functional Coverage in Testing Chip Desi...

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

作者： Yan, Ming Chen, Junjie Mao, Hangyu Jiang, Jiajun Hao, Jianye Li, Xingjian Tian, Zhao Chen, Zhichao Li, Dong Xian, Zhangkong Guo, Yanwei Liu, Wulong Wang, Bin Sun, Yuefeng Cui, Yongshun Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China Huawei Hisilicon Beijing Peoples R China

ISBN: (纸本)9798350300376

Defective chips may cause huge losses (even disasters), and thus ensuring the reliability of chips is fundamentally important. To ensure the functional correctness of chips, adequate testing is essential on the chip design implementation (CDI), which is the software implementation of the chip under design in hardware description languages, before putting on fabrication. Over the years, while some techniques targeting CDI functional testing have been proposed, there are still a number of hard-to-cover functionality points due to huge input space and complex constraints among variables in a test input. We call the coverage of these points last-mile functional coverage. Here, we propose the first technique targeting the significant challenge of improving last-mile functional coverage in CDI functional testing, called LMT, which does not rely on domain knowledge and CDI internal information. LMT first identifies the relevant variables in test inputs to the coverage of last-mile functionality points inspired by the idea of feature selection in machine learning, so as to largely reduce the search space. It then incorporates Generative Adversarial Network (GAN) to learn to generate valid test inputs (that satisfy complex constraints among variables) with a larger possibility. We conducted a practical study on two industrial CDIs in Huawei to evaluate LMT. The results show that LMT achieves at least 49.27% and 75.09% higher last-mile functional coverage than the state-of-the-art CDI test input generation techniques under the same number of test inputs, and saves at least 94.24% and 84.45% testing time to achieve the same functional coverage.

关键词： Chip Design Testing Test Generation Functional Coverage machine learning

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HIGHLY PARSIMONIOUS MULTI-FIDELITY learning OF PROCESS PARAMETER-PERFORMANCE RELATIONSHIPS: A CASE STUDY WITH FUSED FILAMENT FABRICATION 18

HIGHLY PARSIMONIOUS MULTI-FIDELITY LEARNING OF PROCESS PARAM...

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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： Cleeman, Jeremy Malhotra, Rajiv Rutgers State Univ Mech & Aerosp Engn New Brunswick NJ 08901 USA

ISBN: (纸本)9780791887240

Modeling the effects of the process parameters on the process or product performance is critical for predictive process design and control. While physics-based modeling has long been the path to such predictions, machine learning models are of increasing interest recently. But the data-hungry nature of machine learning entails prohibitive experimental cost, is hindered by large oversimplifications of computationally efficiency analytical models, or is limited by the time and cost of creating and executing more accurate numerical models. The situation becomes worse for newer processes or material for which the underlying coupled physics is often not fully known, thus decelerating process or material deployment. Transfer learning has been proposed as a solution for this problem, but the above issues remain since a source model of sufficient fidelity is still needed. This paper takes a first stab at this issue. We propose a physics-informed transfer learning that combines a large amount of data from an extremely low-fidelity but equally computationally efficient process model based on first principles with a small experimental dataset that represents ground truth. We demonstrate this approach by creating a support vector regression model of printed line width in Fused Filament Fabrication. Orders of magnitude reduction in computational cost and 60% reduction in experimental data needed is achieved, while retaining similar or lower error as compared to naive learning on a much larger experimental dataset. We demonstrate the importance of the number and location of high-fidelity experimental data points and discuss future work in this context.

关键词： Multi-fidelity learning machine learning Transfer learning Fused Filament Fabrication

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Accuracy Improvement in Chondrosarcoma Detection using Decision Tree Algorithm (DT) and Comparing with Support Vector machine (SVM) 8

Accuracy Improvement in Chondrosarcoma Detection using Decis...

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8th IEEE international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Bhanu Veekshith, A. Nagalakshmi, T.J. Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350347791

This research is based on the improvement of accuracy in detection of chondrosarcoma the decision tree is taken and comparing the results with the support vector machine from deep learning algorithms. The total set of samples taken for these processes are 38 to analyze the two groups. Group 1 for the decision tree consisting of 19 samples and Group 2 for the support vector machine also containing 19 samples. The dataset has been imported and python code is implemented using Google Collab software. The sample has been calculated from the values obtained from the prior studies with help of some online statistical analysis tools with the pretest power of 80% and the alpha of 0.05 value. From simulation results of machine learning algorithms, the decision tree gives output accuracy of 95% which provides better output than the other used machine algorithm in this research support vector machine gives output of accuracy 81% (p © 2023 IEEE.

关键词： Decision trees

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Survey on Data Ingestion for AutoML 35

Survey on Data Ingestion for AutoML

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

作者： Mac’Hamilton Renaux Alves, Gabriel Maciel, Alexandre Magno Andrade Department of Computer Engineering University of Pernambuco Recife Brazil

Automated machine learning (AutoML) is an increasingly popular approach to building machine learning (ML) models without the need for extensive human intervention. One key component of AutoML is automated data ingestion, which involves automatically collecting, cleaning, and preparing data for use in ML models. This paper aims to analyze the literature in order to identify how automated data ingestion is being developed in the literature. To achieve this goal, a survey was conducted on the state-of-the-art of automated data ingestion using a method based on a systematic literature review, in order to identify the existing practices. A total of 12 articles were initially found, however, after applying filters, only six of them were ultimately utilized in the research, showing that visual data navigation and validation as well as metadata inference are important features for automated data ingestion focused in AutoML. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： machine learning

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Active Deduction Heuristic Algorithm of Standard Contradiction Separation Rule Based on Reinforcement learning 18

Active Deduction Heuristic Algorithm of Standard Contradicti...

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18th international conference on Intelligent Systems and Knowledge engineering, ISKE 2023

作者： Zhong, Jian Liu, Peiyao Chen, Shuwei School of Computer and Software Engineering National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Xihua University Chengdu China

ISBN: (纸本)9798350318401

machine learning has been a prominent research focus in recent years, and integrating machine learning methods into automated theorem provers has become a current trend in theorem proving research. Standard contradiction separation rule is a newly proposed multi-clause, dynamic, and synergized deductive inference rule. The key aspect of standard contradiction separation rule is how to plan deduction paths. In order to better plan the deduction path, this paper explores the dynamic learning within the process of contradiction separation deduction and introduces a novel active deductive heuristic algorithm based on reinforcement learning. Experimental results demonstrate that this new algorithm enhances the reasoning capability of the theorem prover Prover9. © 2023 IEEE.

关键词： Reinforcement learning

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