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共 3227 条记录，以下是121-130 订阅

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Enhancing Collaboration and Agility in Data-Centric AI Projects 1

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Stieler, Fabian Baul, Bernhard Univ Augsburg Software Methodol Distributed Syst Augsburg Germany

ISBN: (数字)9783031641824

ISBN: (纸本)9783031641817;9783031641824

Usually, mature Artificial Intelligence (AI) projects are developed by a team of various members, such as data engineers, data scientists, software engineers and machine learning (ML) engineers. They often pursue highly heterogeneous approaches, leading to new challenges in collaboration, particularly regarding software quality, data versioning and the traceability of model metrics and other resulting artifacts. These challenges are further intensified when AI projects rely on dynamic datasets, introducing an entirely new dimension that teams must deal with. Adopting principles from the machine learning operations (MLOps) paradigm becomes essential in this context. To go beyond existing process models and develop actionable guidelines, our work introduces a Git workflow for AI projects. We present basic instructions for data and code while outlining a minimal infrastructure setup. Building upon abstract concepts, we delve into concrete, actionable steps by examining the proposed branching workflow. Through a case study, we apply the development methodology to two use cases and demonstrate that the principles and approaches positively impact project outcomes.

关键词： software engineering for machine learning Agile development MLOps AI development Data-centric AI

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Balancing Effectiveness and Flakiness of Non-Deterministic machine learning Tests 23

Balancing Effectiveness and Flakiness of Non-Deterministic M...

引用

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

作者： Xia, Chunqiu Steven Dutta, Saikat Misailovic, Sasa Marinov, Darko Zhang, Lingming Univ Illinois Champaign IL USA

ISBN: (纸本)9781665457019

Testing machine learning (ML) projects is challenging due to inherent non-determinism of various ML algorithms and the lack of reliable ways to compute reference results. Developers typically rely on their intuition when writing tests to check whether ML algorithms produce accurate results. However, this approach leads to conservative choices in selecting assertion bounds for comparing actual and expected results in test assertions. Because developers want to avoid false positive failures in tests, they often set the bounds to be too loose, potentially leading to missing critical bugs. We present FASER - the first systematic approach for balancing the trade-off between the fault-detection effectiveness and flakiness of non-deterministic tests by computing optimal assertion bounds. FASER frames this trade-off as an optimization problem between these competing objectives by varying the assertion bound. FASER leverages 1) statistical methods to estimate the flakiness rate, and 2) mutation testing to estimate the fault-detection effectiveness. We evaluate FASER on 87 non-deterministic tests collected from 22 popular ML projects. FASER finds that 23 out of 87 studied tests have conservative bounds and proposes tighter assertion bounds that maximizes the fault-detection effectiveness of the tests while limiting flakiness. We have sent 19 pull requests to developers, each fixing one test, out of which 14 pull requests have already been accepted.

关键词： machine learning

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Commit Message Matters: Investigating Impact and Evolution of Commit Message Quality 23

Commit Message Matters: Investigating Impact and Evolution o...

引用

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

作者： Li, Jiawei Ahmed, Iftekhar Univ Calif Irvine Irvine CA 92617 USA

ISBN: (纸本)9781665457019

Commit messages play an important role in communication among developers. To measure the quality of commit messages, researchers have defined what semantically constitutes a Good commit message: it should have both the summary of the code change (What) and the motivation/reason behind it (Why). The presence of the issue report/pull request links referenced in a commit message has been treated as a way of providing Why information. In this study, we found several quality issues that could hamper the links' ability to provide Why information. Based on this observation, we developed a machine learning classifier for automatically identifying whether a commit message has What and Why information by considering both the commit messages and the link contents. This classifier outperforms state-of-the-art machine learning classifiers by 12 percentage points improvement in the F1 score. With the improved classifier, we conducted a mixed method empirical analysis and found that: (1) Commit message quality has an impact on software defect proneness, and (2) the overall quality of the commit messages decreases over time, while developers believe they are writing better commit messages. All the research artifacts (i.e., tools, scripts, and data) of this study are available on the accompanying website [2].

关键词： Commit message quality software defect proneness empirical analysis

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Challenges of Accurate and Efficient AutoML 38

Challenges of Accurate and Efficient AutoML

引用

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

作者： Dey, Swarnava Ghose, Avik Das, Soumik TCS Res Embedded Devices & Intelligent Syst Kolkata India TCS Res Digital Hlth & Wellness Kolkata India Tata Consultancy Serv Ltd TCS Pace Port Toronto ON Canada

ISBN: (纸本)9798350329964

Embedded Artificial Intelligence (AI) is becoming increasingly important in the field of healthcare where such AI enabled devices are utilized to assist physicians, clinicians, and surgeons in their diagnosis, rehabilitation and therapy planning. However, it is still a challenging task to come up with an accurate and efficient machine learning model for resource-limited devices that work 24 x 7. It requires both intuition and experience. This dependence on human expertise and reliance on trial-and-error-based design methods create impediments to the standard processes of effort estimation, design phase planning, and generating service-level agreements for projects that involve AI-enabled MedTech devices. In this paper, we present AutoML search from an algorithmic perspective, instead of a more prevalent optimization or black-box tool view. We briefly present and point to case studies that demonstrate the efficacy of the automation approach in terms of productivity improvements. We believe that our proposed method can make AutoML more amenable to the applications of software engineering principles and also accelerate biomedical device engineering, where there is a high dependence on skilled human resources.

关键词： automation software engineering IoT Deep learning AutoML

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Research on Monitoring Data Optimization Method Based on machine learning 4

Research on Monitoring Data Optimization Method Based on Mac...

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

作者： Wu, Shuang Li, Songhui Liu, Annan Wang, Xianzuo Shandong University of Science and Technology College of Civil Engineering and Architecture Qingdao China

ISBN: (纸本)9798350329490

In order to analyze the variation characteristics of the measured data of the monitoring system and determine the main factors affecting the accuracy of the measured data, four models in machine learning methods were optimized for parameters and compared for prediction effects. The optimal model was selected for analysis and prediction. The results showed that the support vector machine model was the optimal choice. Through the quantitative analysis of temperature strain linear regression results, it is found that the temperature change of the bridge is correlated with the zero point change of the strain reference. Embedding the predictive model algorithm into the system to remove data changes caused by temperature effects, the optimization results show that this method can effectively eliminate the impact of temperature effects on monitoring data such as strain. © 2023 IEEE.

关键词： Support vector machines

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machine learning-based software Bug Classification through Mining Open Source Repositories 21

Machine Learning-based Software Bug Classification through M...

引用

21st OITS international conference on Information Technology, OCIT 2023

作者： Meher, Jyoti Prakash Mall, Rajib Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798350358230

A taxonomy of expected bugs in a typical program can be useful while performing several software engineering tasks such as test case design, mutation testing, and fault localization. In this context, we have analyzed the bug characteristics available in two open source projects: Eclipse and NetBeans. On these bug repositories, we have used a discriminative classical algorithm and three machine learning techniques to classify 126536 bugs into 9 different categories. Consequently, we could classify several hidden patterns over time in different classes. Our experimental results show an 29.73% increase in classifier performance in terms of AUC-ROC as compared to state-of-the-art results. © 2023 IEEE.

关键词： software testing

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software Defect Prediction Using machine learning - A Systematic Literature Review 20

Software Defect Prediction Using Machine Learning - A System...

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20th international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chengxiao, Yan Owais, Jaweria Ahmed, Awais Khan, Muhammad Owais Xiaoyang, Zeng Tunio, Muhammad Hanif School of Computer Science and Engineering University of Electronic Science and Technology of China China Muhammad Ali Jinnah University Department of Computer Science Sindh Karachi Pakistan

ISBN: (纸本)9798350318982

The most expressive attribute for defining successful software is its quality, which can only be attained when the chances of a defect occurrence are doubtful. software defect Prediction is a cycle to develop a model that software practitioners can use to detect faulty areas before the testing phase. Prediction of such classes before its testing phase will enable the development team to allocate resources more efficiently. It reduces the testing effort and can yield higher quality software with reduced cost. Prediction of such defects can be further elevated when machine learning techniques are introduced to such a software defect Prediction (SDP) model. In this paper, we conducted a systematic literature review of the studies that are available on such practices of predicting defects in software before the actual testing is started with a blend of machine learning techniques that provide an edge to software defect prediction model is carried out with the latest Research present in this area providing the most upgrade statistics on software defect prediction using machine learning. © 2023 IEEE.

关键词： Defects

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Comparing the Effectiveness of machine learning and Deep learning Techniques for Feature Envy Detection in software Systems 1

Comparing the Effectiveness of Machine Learning and Deep Lea...

引用

1st international conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Menshawy, Rana S. Yousef, Ahmed H. Salem, Ashraf Faculty of Engineering Computer and System Department Ain Shams University Cairo Egypt Egypt University of Informatics Cairo Egypt

ISBN: (纸本)9798350335569

Code smells are common in poorly designed software that can hinder code maintainability. Automatic detection of design flaws assists developers in identifying code smells in theirsoftware programs to avoid low-quality delivery. However, the interpretation of code smells is subjective, and existing machine learning-based detection tools are limited in their ability to capture complex semantic relationships in textual code. To address this limitation, the paper proposes a detection system that exploits machine learning and deep learning techniques and compares their effectiveness in identifying the feature envy code smell. The study applies six deep learning techniques based on code semantics features extracted from abstract syntax trees, and eleven machine learning techniques based on code structural features. The results show that both approaches are effective in detecting code smells, with Random Forest achieving exceptional performance among the machine learning classifiers. © 2023 IEEE.

关键词： Deep learning

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Unveiling Research Trends in Stack Overflow: A Comprehensive Analysis of General Discussion Theme

Unveiling Research Trends in Stack Overflow: A Comprehensive...

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2024 international conference on Smart Computing, IoT and machine learning, SIML 2024

作者： Giwangkoro, Geri Gebyar Nugroho, Yusuf Sulistyo Universitas Muhammadiyah Surakarta Department of Informatics Surakarta Indonesia

ISBN: (纸本)9798350364101

Stack Overflow (SO), as technical Question & Answer (Q&A) platform, plays a pivotal role in facilitating knowledge exchange between software developers. Since the release of SO, numerous studies have been conducted to gain insights into the discussions of its users. Despite its significance, a comprehensive review in terms of the evolving trends, impact, sources, and themes within this domain is lacking. This paper presents a systematic literature review of SO-related studies from 2019 to 2023, exploring the evolution, impact assessment, relevant sources, and thematic explorations within this domain. Our study analyzes 804 SO-related journal and conference articles, employing Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) technique to collect data from Scopus. The findings show that the evolution of SO-related studies reveals a sustained academic interest, with 172 publications in 2020 and a continued engagement with 153 publications in 2023. The declining trend in citations over recent years suggests the need for further exploration into factors influencing the impact of SO studies on the broader field of software development. The identification of relevant sources highlights different contributions from journals and conferences, such as Empirical software engineering and the ACM international conference Proceeding Series. Thematic exploration reveals dynamic changes, with a dominant focus on Codes (Symbols) in 2019 and subsequent diversification into themes like Deep learning and software Design. This literature review provides valuable insights for academics, practitioners, and developers, emphasizing the importance of SO as a platform for collaborative knowledge sharing within the software development community. © 2024 IEEE.

关键词： software design

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Research on simulation of ejection seat cockpit layout based on human factors engineering 4

Research on simulation of ejection seat cockpit layout based...

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Shi, Jinshou Xiao, Ying Liu, Zhijun Li, Linjie Beijing Polytechnic Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Based on the size data of the human body of Chinese male pilots, this article uses the standard model library of Poser software to adjust and modify the dimensions of various parts, and establishes a three-dimensional human model that conforms to the size of Chinese male pilots. Then, based on the basic geometric dimensions of the aircraft cockpit and the ejection seat model, three-dimensional software is used to establish the spatial layout of the ejection seat and cockpit control mechanism, and a static comfort simulation platform for the ejection seat is obtained. Based on the principles of human factors engineering, the layout simulation analysis of the control mechanism in the simulation platform was conducted to obtain the ejection seat control layout that meets the comfort criteria for human riding, improving the static comfort of the ejection seat. This method can be used for rapid design and optimization of cockpit layout, and has important engineering practical significance. © 2024 SPIE.

关键词： Ejection seats

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