Cognitive radio networks (CRNs) are getting increasingly famous due to their potential to deliver excessive-bandwidth offerings to numerous users. Despite this, the signal satisfactory of those networks can be fantast...
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This paper proposes an innovative approach for enhancing the efficiency and security of manufacturing supply chains by integrating the Internet of Things, blockchain technology, and genetic algorithm-based consensus m...
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Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retri...
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Dockerfiles can be affected by bad design choices, known as Dockerfile smells. Hadolint is currently the reference tool able to detect them, and it is widely used both by researchers and practitioners. The literature ...
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ISBN:
(纸本)9798350363982;9798400705878
Dockerfiles can be affected by bad design choices, known as Dockerfile smells. Hadolint is currently the reference tool able to detect them, and it is widely used both by researchers and practitioners. The literature shows that these smells are commonly diffused in Dockerfiles, but it is still not clear how developers perceive them as bad practices. This paper aims to investigate the relevance of the Dockerfile smells captured by hadolint from the perspective of expert Dockerfile developers. We first perform a mining study in which we extract the change history of Dockerfiles maintained by experts to understand what smells have been more frequently introduced in their history. Next, we ran a survey in which we asked expert Dockerfile developers to evaluate Dockerfiles affected by different smells. We obtained 94 responses for 17 smells, representative of 24 Dockerfile smells. We found that experts prioritize a small part of the evaluated smells over others. Besides, they report additional bad practices not mapped as smells in any existing catalog. Thus, we propose a ranked catalog containing 26 additional Dockerfile smells, which can be used as a guide for novices to understand which aspects to focus on to write good-quality Dockerfiles.
Effective sharing and reuse practices have long been hallmarks of proficient softwareengineering. Yet the exploratory nature of data science presents new challenges and opportunities to support sharing and reuse of a...
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ISBN:
(纸本)9781665495905
Effective sharing and reuse practices have long been hallmarks of proficient softwareengineering. Yet the exploratory nature of data science presents new challenges and opportunities to support sharing and reuse of analysis code. To better understand current practices, we conducted interviews (N=17) and a survey (N=132) with data scientists at Microsoft, and extract five commonly used strategies for sharing and reuse of past work: personal analysis reuse, personal utility libraries, team shared analysis code, team shared template notebooks, and team shared libraries. We also identify factors that encourage or discourage data scientists from sharing and reusing. Our participants described obstacles to reuse and sharing including a lack of incentives to create shared code, difficulties in making data science code modular, and a lack of tool interoperability. We discuss how future tools might help meet these needs.
In the field of softwareengineering, accurate and efficient measurement of system size is crucial for project management and decision-making. Traditional software system size evaluation methods, such as expert decisi...
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ISBN:
(纸本)9798400707032
In the field of softwareengineering, accurate and efficient measurement of system size is crucial for project management and decision-making. Traditional software system size evaluation methods, such as expert decision-making methods, COCOMO models, function point methods, and analogical methods, have problems such as relying on manual labor, low efficiency, and susceptibility to subjective factors. To address these issues, this paper proposes a Dalse (Domain Adaptive Learning System Evaluation) automated method for evaluating software function size. Based on the IFPUG function point standard, this method constructs a BiLSTM-CRF model for identifying function types, and introduces domain adaptive learning technology to solve the problem of insufficient labeled text data for domain-specific annotation. This enables the method to flexibly adapt to the characteristics of software in specific domains, achieving automated extraction and quantitative calculation of software function points. Experimental validation shows that the method's precision, recall, and F1 score reached 83%, 80%, and 82%, respectively, which is a 5% improvement compared to the accuracy of pure BiLSTM-CRF function point recognition.
Immersive technologies (IT) create different experiences by combining the physical world with digital or simulated reality. Virtual augmented and mixed reality are the main types of immersive technologies. Since the b...
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This article introduces a Generative Adversarial Network tailored for medical imaging, in particular designed to synthesize retinal images alongside side their segmented mask. This innovation addresses the challenge o...
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This paper presents the genetic algorithm-based optimal design method of the resonant converters. The developed software finds an optimal parameter set of the resonant converter for achieving the rated output power wi...
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we propose a cutting-edge solution that leverages passive adaptive methods based on ensemble learning to effectively detect anomalous traffic in data streams. Our approach tackles the issue of concept drift by integra...
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