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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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A Comprehensive Analysis of machine learning Methods for Bug Prediction in software Development 5th

A Comprehensive Analysis of Machine Learning Methods for Bug...

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5th international conference on Data Science, machine learning and Applications

作者： Ravikumar, Ch Kumar, Kotha Harish Sathish, Nandigama Suhasini, S. Nimmala, Satyanarayana Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Data Sci Hyderabad Telangana India Priyadarshini Inst Sci & Technol Women Dept CSE Khammam Telangana India CVR Coll Engn Dept Informat Technol Hyderabad Telangana India CVR Coll Engn Dept Data Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In order to create software that is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cost-effective and successful advanced bug prediction model presents challenges. This research endeavor aims to achieve precise bug identification by exploring the utilization of various machine learning techniques on training and testing datasets. Multiple machine learning methods have been devised to identify and learn from software defects. This study employs machine learning techniques to conduct a comprehensive examination of software bug detection, offering valuable insights to the software industry. It synthesizes existing research on bug prediction, detailing different methods and highlighting their effectiveness, advantages, and limitations. This comprehensive analysis offers valuable guidance to researchers and software developers seeking to enhance bug detection methods for the creation of higher-quality software.

关键词： Bug prediction software development machine learning Accuracy Fault detection

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Few-shot sheep counting based on density map regression

Few-shot sheep counting based on density map regression

引用

2023 international conference on software engineering and machine learning, conf-seml 2023

作者： Liu, Qiaokun College of Computer and Information Science College of Software Southwest University Chongqing China

With the continuous expansion of breeding scale, the automatic counting of sheep has attracted a lot of research attention in recent years, which aims to count the number of sheep in real time to rationally use grassland resources and design appropriate agricultural production policies. The traditional method of counting sheep mainly relies on manual work, which is time-consuming and labor-intensive. Thanks to the rapid development of image acquisition equipment and pattern recognition technology, the recognition and counting of sheep based on deep learning has become the current mainstream counting framework. However, for dense flocks of sheep, such as occlusions between sheep, the accuracy of existing methods cannot meet the needs of practical applications. In this paper, we suggest FamNet, a few-shot sheep counting method based on UAV images and density map regression. Specifically, we first collected 190 sheep images with different resolutions, aspect ratios, backgrounds, and sheep numbers;secondly, by selecting count samples to extract objects in the images, and using density map regression to predict the number of sheep, this enables sheep counting in small numbers of samples. A large number of experimental results have verified the effectiveness of the method in this paper, which can achieve an average counting accuracy of 91.02%. In addition, we discuss the impact of sample selection, backbone selection, and feature scaling on model accuracy. All the results show that the FamNet flock counting method proposed in this paper can be applied to flock counting, has good adaptability to flock counting under different background conditions and can count the number of flocks in a timely and effective manner. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Aspect ratio

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An underground pipeline relocation decision support system based on BIM and GIS integration 2

An underground pipeline relocation decision support system b...

引用

2nd international conference on software engineering and machine learning, conf-seml 2024

作者： Ma, Fei Gao, Yang Zhang, Guien Huang, Can Chen, Wenkai CCCC Wuhan Zhixing International Engineering Consulting Co. Ltd. Hanyang District Wuhan430100 China

This study introduces a rapid decision-support system for underground pipeline relocation, leveraging the synergistic capabilities of Building Information Modeling (BIM) and Geographic Information Systems (GIS). The amalgamation of BIM and GIS technologies within the system facilitates comprehensive information management and analysis. To validate the system's practicality, dedicated software applications have been developed to streamline data collection, model generation, and data fusion. In the data collection phase, a mobile application has been designed to efficiently acquire standardized physical exploration data. In the design phase, we have implemented an automated pipeline modeling process, utilizing the physical exploration data through our in-house Revit plug-in. Concurrently, the plug-in enables clash detection and distance analysis. Subsequently, the model can be seamlessly transferred to our custom-built integration platform, which is founded on ArcGIS. Within this platform, the integration of the BIM model with GIS can be visually represented. Furthermore, it provides tools for analyzing project quantity and monitoring project progress. The integrating proposed system and software enhances the efficiency of underground pipeline relocation and augments decision-making capabilities. © Published under licence by IOP Publishing Ltd.

关键词： Data fusion

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SewerOD: A visual sewer disease detection dataset for machine learning

SewerOD: A visual sewer disease detection dataset for machin...

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2023 international conference on software engineering and machine learning, conf-seml 2023

作者： Wei, Wei Li, Ce Li, Shuo Chen, Zheng Yang, Feng China University of Mining & Technology School of Mechanical Electronic & Information Engineering Department of Computer Science and Technology Xueyuan Rd Haidian District Beijing100089 China Peking University School of Electronics Engineering and Computer Science Department of Computer Science and Technology Yiheyuan Rd Haidian District Beijing100871 China

The underground sewer pipeline is an essential urban infrastructure that undertakes the vital responsibility of discharging sewage. The location and type of diseases in the pipeline often need to be checked manually by inspectors, which cannot be completed efficiently due to labor costs and time requirements. With the development of computer vision, the use of detection technology to maintain sewer pipelines has extremely high research value. However, image data of pipelines are often regarded as commercial secrets, and these studies are greatly limited due to the scarcity of open-source pipeline disease datasets. To solve this problem, we present a public large-scale object detection dataset for sewer disease detection named SewerOD in this work. The dataset contains about 47K images, annotated by professional researchers, and includes two of the most widespread structural disease types: Corrosion and Crack. Our dataset is available at https://***/SewerOD. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： machine learning

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Evolve the Model Universe of a System Universe 38

Evolve the Model Universe of a System Universe

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

作者： Yue, Tao Ali, Shaukat Simula Res Lab Oslo Norway Oslo Metropolitan Univ Simula Res Lab Oslo Norway

ISBN: (纸本)9798350329964

Uncertain, unpredictable, real-time, and lifelong evolution causes operational failures in intelligent software systems, leading to significant damages, safety and security hazards, and tragedies. To fully unleash such systems' potential and facilitate their wider adoption, ensuring the trustworthiness of their decision-making under uncertainty is the prime challenge. To overcome this challenge, an intelligent software system and its operating environment should be continuously monitored, tested, and refined during its lifetime operation. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most up-to-date states. Such representations are often in the form of prior-knowledge-based and machine-learning models, together called 'model universe'. In this paper, we present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe evolution.

关键词： Model Universe System Universe Coevolution Epigenetics machine learning

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learning Program Models from Generated Inputs 23

Learning Program Models from Generated Inputs

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

作者： Mammadov, Tural Saarland Univ CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany

ISBN: (纸本)9798350322637

Recent advances in machine learning (ML) show that Neural machine Translation (NMT) models can mock the program behavior when trained on input-output pairs. Such models can mock the functionality of existing programs and serve as quick-to-deploy reverse engineering tools. Still, the problem of automatically learning such predictive and reversible models from programs needs to be solved. This work introduces a generic approach for automated and reversible program behavior modeling. It achieves 94% of overall accuracy in the conversion of Markdown-to-HTML and HTML-to-Markdown markups.

关键词： software testing security testing reverse engineering deep learning

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Towards machine learning guided by best practices 23

Towards machine learning guided by best practices

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

作者： Mojica-Hanke, Anamaria Univ Passau Passau Germany Univ Los Andes Bogota Colombia

ISBN: (纸本)9798350322637

Nowadays, machine learning (ML) is being used in software systems with multiple application fields, from medicine to software engineering (SE). On the one hand, the popularity of ML in the industry can be seen in the statistics showing its growth and adoption. On the other hand, its popularity can also be seen in research, particularly in SE, where multiple studies related to the use of machine learning in software engineering have been published in conferences and journals. At the same time, researchers and practitioners have shown that machine learning has some particular challenges and pitfalls. In particular, research has shown that ML-enabled systems have a different development process than traditional software, which also describes some of the challenges of ML applications. In order to mitigate some of the identified challenges and pitfalls, white and gray literature has proposed a set of recommendations based on their own experiences and focused on their domain (e.g., biomechanics), but for the best of our knowledge, there is no guideline focused on the SE community. This thesis aims to reduce the gap of not having clear guidelines in the SE community by using possible sources of practices such as question-and-answer communities and also previous research studies. As a result, we will present a set of practices with an SE perspective, for researchers and practitioners, including a tool for searching them.

关键词： machine learning good practices software engineering

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Towards Self-Adaptive machine learning-Enabled Systems Through QoS-Aware Model Switching 38

Towards Self-Adaptive Machine Learning-Enabled Systems Throu...

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

作者： Kulkarni, Shubham Marda, Arya Vaidhyanathan, Karthik IIIT Hyderabad Software Engn Res Ctr Hyderabad India

ISBN: (纸本)9798350329964

machine learning (ML), particularly deep learning, has seen vast advancements, leading to the rise of machine learning-Enabled Systems (MLS). However, numerous software engineering challenges persist in propelling these MLS into production, largely due to various run-time uncertainties that impact the overall Quality of Service (QoS). These uncertainties emanate from ML models, software components, and environmental factors. Self-adaptation techniques present potential in managing run-time uncertainties, but their application in MLS remains largely unexplored. As a solution, we propose the concept of a machine learning Model Balancer, focusing on managing uncertainties related to ML models by using multiple models. Subsequently, we introduce AdaMLS, a novel self-adaptation approach that leverages this concept and extends the traditional MAPE-K loop for continuous MLS adaptation. AdaMLS employs lightweight unsupervised learning for dynamic model switching, thereby ensuring consistent QoS. Through a self-adaptive object detection system prototype, we demonstrate AdaMLS's effectiveness in balancing system and model performance. Preliminary results suggest AdaMLS surpasses naive and single state-of-the-art models in QoS guarantees, heralding the advancement towards self-adaptive MLS with optimal QoS in dynamic environments.

关键词： Self Adaptation Self-adaptive systems software Architecture ML-Enabled Systems ML4SA Unsupervised learning Object Detection

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Seldonian Toolkit: Building software with Safe and Fair machine learning 23

Seldonian Toolkit: Building Software with Safe and Fair Mach...

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

作者： Hoag, Austin Kostas, James E. da Silva, Bruno Castro Thomas, Philip S. Brun, Yuriy Berkeley Existential Risk Initiat Walnut CA 91789 USA Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9798350322637

We present the Seldonian Toolkit, which enables software engineers to integrate provably safe and fair machine learning algorithms into their systems. software systems that use data and machine learning are routinely deployed in a wide range of settings from medical applications, autonomous vehicles, the criminal justice system, and hiring processes. These systems, however, can produce unsafe and unfair behavior, such as suggesting potentially fatal medical treatments, making racist or sexist predictions, or facilitating radicalization and polarization. To reduce these undesirable behaviors, software engineers need the ability to easily integrate their machine-learning-based systems with domain-specific safety and fairness requirements defined by domain experts, such as doctors and hiring managers. The Seldonian Toolkit provides special machine learning algorithms that enable software engineers to incorporate such expert-defined requirements of safety and fairness into their systems, while provably guaranteeing those requirements will be satisfied. A video demonstrating the Seldonian Toolkit is available at https://***/wHR- hDm9jX4/.

关键词： algorithmic fairness high confidence fairness guarantees machine learning probabilistic verification Seldonian toolkit

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Curriculum for a New Five-Year Academic Program in Intelligent Systems engineering and software engineering

Curriculum for a New Five-Year Academic Program in Intellige...

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Solo, Ashu M. G. Maverick Trailblazers Inc Wilmington DE 19805 USA

ISBN: (纸本)9798350361513;9798350372304

This research paper proposes a curriculum for a five-year academic program with a bachelor's degree (honors) in intelligent systems engineering and software engineering and a master's degree in intelligent systems engineering and software engineering. This program includes courses in data structures, compiler design, operating system design, firmware design, database systems, computer graphics and virtual reality design, static and dynamic website design, development of chatbots and voice assistants, software engineering methodology, knowledge-based systems, fuzzy logic, neural networks, evolutionary computation, evolutionary multiojective optimization, machine learning, image processing, computer vision, pattern recognition, voice recognition, natural language processing, data science, control systems, intelligent control systems, robotics, digital signal processing, mathematics, engineering physics, biology, etc. These degrees will allow graduates to have a good understanding of all of the main branches of intelligent systems engineering and software engineering as well as other relevant subjects in electrical and computer engineering.

关键词： education intelligent systems computational intelligence soft computing artificial intelligence machine learning software development firmware development

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