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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 3227 条记录，以下是31-40 订阅

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Toward a Method engineering Framework for Project Management and machine learning 47

Toward a Method Engineering Framework for Project Management...

引用

47th IEEE-Computer-Society Annual international conference on Computers, software, and Applications (COMPSAC)

作者： Uysal, Murat Pasa Baskent Univ Dept Management Informat Syst Ankara Turkiye

ISBN: (纸本)9798350326970

Adopting or tailoring a project management (PM) method for the domain-specific requirements of machine learning (ML) and software engineering (SE) has been a major challenge. However, the review of the literature cannot provide sufficient work, and thus engineering PM processes for ML remains neglected. Therefore, this paper presents the theoretical and methodological aspects of a study, which is also the third and last part of an integrated research project conducted for Baskent University hospital Ankara (BUHA). The outputs of the study are twofold: (a) a method engineering (ME) framework for ML PM;and (b) a new hybrid PM for ML projects. The research approach combined the guidelines and principles of Design Science Research and Action Research methods. It is thought that this study may be regarded as promising, and an attempt to improve SE and PM processes of ML in the healthcare domain.

关键词： machine learning method engineering project management agile healthcare

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A Standard Baseline for software Defect Prediction: Using machine learning and Explainable AI 47

A Standard Baseline for Software Defect Prediction: Using Ma...

引用

47th IEEE-Computer-Society Annual international conference on Computers, software, and Applications (COMPSAC)

作者： Bommi, Nitin Sai Negi, Atul Univ Hyderabad Sch Comp & Informat Sci Hyderabad India

ISBN: (纸本)9798350326970

The utility of ubiquitous computing systems drives large-scale software development with millions of lines of code (LOC). As there are vast code sets, it also increases the possibility of coding errors since it is difficult for even highly trained software engineers to write flawless code. Such flawed software can lead to severe issues once deployed. McCabe and Halstead proposed feature extractors for source code to define software quality. Based on the static features proposed by McCabe and Halstead, we run a series of feature engineering techniques and different machine learning models to detect code defects and use explainable algorithms to assess the prediction quality. We report different processing pipeline combinations to detect defects and compare the approaches. We conclude the paper with comments on the nature of the dataset and establish a baseline for further research.

关键词： machine learning Explainable AI software Defect Prediction software engineering Dataset Imbalance

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Optimization of software engineering Supervision Based on machine learning Algorithm

Optimization of Software Engineering Supervision Based on Ma...

引用

2023 international conference on Power, Electrical engineering, Electronics and Control, PEEEC 2023

作者： Tianchi, Kang Qingdao City University Shandong Qingdao266106 China

ISBN: (纸本)9798350329124

Sound supervision regulations and technical standards, forming a professional supervision team, and implementing the software engineering project supervision system are not only the common practice and successful experience in the world, but also the objective requirements of the software industry and the only way for the healthy development of the software industry. This paper establishes a comprehensive mathematical optimization model for the quality, cost, construction period and resources of software engineering supervision, and makes comprehensive optimization of multiple objectives. The improved machine learning algorithm is applied to the optimization process of the parts project supervision network plan, and the algorithm flow of the software engineering supervision network plan optimization is designed satisfactory solution. © 2023 IEEE.

关键词： Optimization

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Enabling machine learning in software Architecture Frameworks 2

Enabling Machine Learning in Software Architecture Framework...

引用

IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Moin, Armin Badii, Atta Guennemann, Stephan Challenger, Moharram Univ Antwerp Antwerp Belgium Tech Univ Munich TUM Munich Germany Univ Reading Reading England TUM Munich Germany MDSI Moormerland Germany Univ Antwerp Antwerp Belgium Flanders Make Lommel Belgium

ISBN: (纸本)9798350301137

Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They have identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the machine learning (ML) and data science-related concerns of data scientists and data engineers are yet to be included in existing architecture frameworks. We interviewed 65 experts from around 25 organizations in over ten countries to devise and validate the proposed framework that addresses the mentioned shortcoming.

关键词： architecture frameworks viewpoints views machine learning qualitative research

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Integrating machine learning into Supply Chain Management: Challenges and Opportunities 5

Integrating Machine Learning into Supply Chain Management: C...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Falkner, Dominik Boegl, Michael Gattinger, Anna Stainko, Roman Zenisek, Jan Affenzeller, Michael RISC Software GmbH Softwarepk 32A A-4232 Hagenberg Austria Johannes Kepler Univ Linz Inst Formal Models & Verificat Linz Austria Univ Appl Sci Upper Austria Softwarepk 11 A-4232 Hagenberg Austria

machine learning is a popular tool for solving problems, however, incorporating it into a use case with additional business logic poses many challenges. Training, managing and storing many different models is not an easy task, requiring the use of multiple frameworks and languages. To take full advantage of existing frameworks it is necessary to facilitate communication between different programming languages. This paper presents an approach to integrating machine learning in a real-world use case which involves predicting demand for a diverse set of products and combining it with business rules and other components to establish a system that improves and automates the ordering process. machine learning models are trained on real-world data from a retailer in Austria and the predictions are incorporated into a heuristic that controls and manages stock levels. This work focuses on the challenges that emerge from the integration of machine learning and presents a message bus based architecture to address them. (c) 2023 The Authors. Published by Elsevier B.V.

关键词： software engineering machine learning architecture message bus

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Analysis of software engineering Practices in General software and machine learning Startups 21

Analysis of Software Engineering Practices in General Softwa...

引用

21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Lakha, Bishal Bhetwal, Kalyan Eisty, Nasir U. Boise State University Computer Science Department BoiseID United States

ISBN: (纸本)9798350345889

Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products. © 2023 IEEE.

关键词： machine learning

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software Defect Prediction using machine learning 10

Software Defect Prediction using Machine Learning

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ali, Sayyed Ashar Roy, Nihar Ranjan Raj, Dharm School of Engineering and Technology Sharda University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9789380544472

software engineering phases and approaches have always targeted to deliver high-performance software designed to fulfill a certain task while optimizing criteria such as length and price. In this particular study, while focusing on ML (machine learning) methodologies, the examination on the SDP (software defect prediction) strategies is performed. The purpose of study is to assess the interoperability of machine learning methods in the prediction of defects. Many experimental data have been provided by academicians that promise improved accuracy in software fault prediction. In the recent past, researchers have adopted machine learning based techniques for software defect detection. We surveyed a list of notable research publications that have used machine learning approaches to anticipate software flaws. The most prevalent methods are NB (Nave Bayes), RF (Random Forest), and SVM (Support Vector machine). Out of these it has been found that Naive Bayes performs better followed by Random Forest. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Defects

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Bridging the Gap between Academia and Industry in machine learning software Defect Prediction: Thirteen Considerations 38

Bridging the Gap between Academia and Industry in Machine Le...

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

作者： Stradowski, Szymon Madeyski, Lech Nokia Mobile Networks Radio Frequency Wroclaw Poland Wroclaw Univ Sci & Technol Dept Appl Informat Wroclaw Poland

ISBN: (纸本)9798350329964

This experience paper describes thirteen considerations for implementing machine learning software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on the ground of the most important observations and lessons learned gathered during a large-scale research effort and introduction of ML SDP to the system-level testing quality assurance process of one of the leading telecommunication vendors in the world - Nokia. We adhere to a holistic and logical progression based on the principles of the business analysis body of knowledge: from identifying the need and setting requirements, through designing and implementing the solution, to profitability analysis, stakeholder management, and handover. Conversely, for many years, industry adoption has not kept up the pace of academic achievements in the field, despite promising potential to improve quality and decrease the cost of software products for many companies worldwide. Therefore, discussed considerations hopefully help researchers and practitioners bridge the gaps between academia and industry.

关键词： machine learning software defect prediction Nokia 5G industry introduction experience paper

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Explicit or Implicit? On Feature engineering for ML-based Variability-intensive Systems 23

Explicit or Implicit? On Feature Engineering for ML-based Va...

引用

17th international Working conference on Variability Modelling of software-Intensive Systems (VaMoS)

作者： Temple, Paul Perrouin, Gilles Univ Rennes CNRS Inria IRISA Rennes France Univ Namur PReCISE NaDI Namur Belgium

ISBN: (纸本)9798400700019

software variability engineering benefits from machine learning (ML) to learn e.g., variability-aware performance models, explore variants of interest and minimize their energy impact. As the number of applications of combining variability with ML grows, we would like to reflect on what is the core to the configuration process in software variability and inference in ML: feature engineering. These disciplines previously managed features explicitly, easing graceful combinations. Now, deep learning techniques derive automatically obscure but efficient features from data. Shall we give up explicit feature management in variability-intensive systems to embrace machine learning advances?

关键词： feature machine learning software variability

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Fair machine learning-An Analytical Study Based on CiteSpace 3

Fair Machine Learning-An Analytical Study Based on CiteSpace

引用

3rd international conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Luo, Xiangxi Cui, Jianfeng Ma, Shuai College of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350331578

With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular research topic in this field. At present, the research on fair machine learning is still in its initial stage. In this paper, we analyzed the research and articles related to fair machine learning (January 2011 to December 2022) using CiteSpace visualization software, explored research collaboration networks (authors, institutions, and countries), keyword co-occurrence and clustering networks, and literature co-citation and clustering networks, and analyzed and constructed knowledge graphs. To understand the research foundation, related research progress, the latest research directions, and the research methods receiving attention in the field of fair machine learning through the analysis of the knowledge graph. Relevant key articles are discussed, and future research directions are envisioned. © 2023 IEEE.

关键词： machine learning

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