Requirements engineering (RE) is an essential part of softwareengineering (SE). As RE activities rely heavily on people, the success of RE is influenced by the human aspects of the team involved. In this research, we...
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ISBN:
(纸本)9798350326970
Requirements engineering (RE) is an essential part of softwareengineering (SE). As RE activities rely heavily on people, the success of RE is influenced by the human aspects of the team involved. In this research, we develop a prototype application, called Motive Metrics, to improve RE activities by allowing managers to track developers' personality and motivation and monitor their impact on developer performance and satisfaction. The tool takes the form of an extension to Jira and was developed through rapid prototyping. The effectiveness and usability of the tool were evaluated by student teams split into managers and team members. When evaluating Motive Metrics, 45.5% of participants rated 4 out of 5 for the application's effectiveness in capturing personalities, but only 9.1% of participants rated 4 out of 5 for capturing motivations. Motive Metrics is likely ineffective in monitoring satisfaction and performance, as 45% of participants rated 1 out of 5 in comfort in sharing their responses. Our evaluation results also show that Motive Metrics might not be beneficial in tracking the influence of motivation on the outcome but slightly more beneficial in tracking the influence of personality on RE task performance.
Automatic production is closely linked with industrial robots. However, the precision of robot positioning significantly impacts the stability of the automation system. To address this issue, we establish an industria...
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Existing studies solve softwareengineering tasks using code infilling through LLMC. They utilize context information, which refers to data near the target code of infilling, as input prompts. Although prompts are ess...
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The authors believe that for society to continue improving, it needs to have a better waste classification system. This paper presents a computer vision model trained with a novel dataset comprising images of waste co...
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In collaborative software development, bots have become increasingly prevalent, making effective bot recommendation a key factor in enhancing development efficiency. This study aims to explore the application and effi...
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ISBN:
(纸本)9798350376975;9798350376968
In collaborative software development, bots have become increasingly prevalent, making effective bot recommendation a key factor in enhancing development efficiency. This study aims to explore the application and efficacy of deep learning models in bot recommendation. Focusing on the Code-BERT model, we conduct a comprehensive evaluation through comparison with baseline models, parameter tuning (including batch size and learning rate), and the incorporation of language data. Our findings demonstrate that under specific conditions, the CodeBERT model exhibits superior performance in bot recommendation tasks, with parameter adjustments and the inclusion of language data significantly impacting the model's effectiveness. These insights offer new perspectives and strategies for the effective recommendation of bots in open-source software platforms.
In this experience paper, we design, implement, and evaluate a new static type-error detection tool for Python. To build a practical tool, we first collected and analyzed 68 real-world type errors gathered from 20 ope...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
In this experience paper, we design, implement, and evaluate a new static type-error detection tool for Python. To build a practical tool, we first collected and analyzed 68 real-world type errors gathered from 20 open-source projects. This empirical investigation revealed four key static-analysis features that are crucial for the effective detection of Python type errors in practice. Utilizing these insights, we present a tool called Pyinder, which can successfully detect 34 out of the 68 bugs, compared to existing type analysis tools that collectively detect only 16 bugs. We also discuss the remaining 34 bugs that Pyinder failed to detect, offering insights into future directions for Python type analysis tools. Lastly, we show that Pyinder can uncover previously unknown bugs in recent Python projects.
Machine learning (ML) is increasingly used in high-stakes areas like autonomous driving, finance, and criminal justice. However, it often unintentionally perpetuates biases against marginalized groups. To address this...
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This article introduces the CM output-matching networks in the Balance architecture and discusses the theory and design process of a broadband RF-input Class-F PA balanced power amplifier. The harmonic balance network...
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This paper introduces a comprehensive framework for intent-based management of networks, security, and applications in software-defined vehicles (SDVs) within 5G networks. To address the complexities and operational c...
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