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Commit messages are important for developers to understand the content and the reason for code changes. However, poor and even empty commit messages widely exist. To improve the quality of commit messages and development efficiency, many commit message ...

Prevalent Fault Localization (FL) techniques rely on tests to localize buggy program elements. Tests could be treated as fuel to further boost FL by providing more debugging information. Therefore, it is highly valuable to measure the Fault Diagnosis ...

The downlink non-orthogonal multiple access (NOMA) transmissions from a holographic multi-input multi-output surface (HMIMOS) transmitter to multiple single-antenna users are investigated in this work. And we focus on the power saving design when the ...

Mutation testing is widely used to measure the test adequacy of a project. Despite its popularity, mutation testing is time-consuming and extremely expensive. To mitigate this problem, researchers propose Predictive Mutation Testing (PMT). Existing PMT ...

Energy management strategies are crucial for Plug-in Hybrid Electric Combine Harvester (PHECH). However, many existing approaches rely on rigid, pre-setting rules that struggle to adjust to the PHECH operational conditions. This paper first ...

In Continuous Integration (CI), accurate build prediction is crucial for minimizing development costs and enhancing efficiency. However, existing build prediction methods, typically based on predefined rules or machine learning classifiers employing ...

Fairness has been a critical issue that affects the adoption of deep learning models in real practice. To improve model fairness, many existing methods have been proposed and evaluated to be effective in their own contexts. However, there is still no ...

Machine translation is integral to international communication and extensively employed in diverse human-related applications. Despite remarkable progress, fairness issues persist within current machine translation systems. In this article, we propose ...

Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an ...

Pretrained models for code have exhibited promising performance across various code-related tasks, such as code summarization, code completion, code translation, and bug detection. However, despite their success, the majority of current models still ...

• A new structure of omnidirectional levelling system is proposed.
• The sensitivity analysis of the structural parameters for levelling system is carried out.
• The structural parameter optimization based on NSGA-Ⅱ algorithm is ...

To address the challenges posed by significant body inclination, suboptimal work quality, and safety concerns during agricultural machinery operations in hilly terrains, this study presents the design of an omnidirectional levelling system ...

We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distributed data. Unlike the conventional FL framework that assumes the client's ...

Neural code generation aims to automatically generate code snippets guided by Natural Language Descriptions (NLDs). In recent years, various neural code generation models for mainstream Programming Languages (PLs), such as Java and ...

• The use of naturally aged rice seeds for research is more realistic.
• Determination of rice seed viability using Near-infrared hyperspectral imaging.
• Establish the CNN model with real data, fake data, mixed real data and fake data.

Viability is a significant indicator of rice seeds, affecting rice yield and quality. Existing viability determination methods cannot meet the requirements of rapidity, non-destructive and accuracy. In this study, near-infrared hyperspectral ...

Recalibrating probabilistic classifiers is vital for enhancing the reliability and accuracy of predictive models. Despite the development of numerous recalibration algorithms, there is still a lack of a comprehensive theory that integrates calibration ...

Collaborative development is critical to improve the productivity. Multiple contributors work simultaneously on the same project and might make changes to the same code locations. This can cause conflicts and require manual intervention from developers ...

Automated program repair (APR) aims to reduce the effort of software development. With the development of deep learning, lots of DL-based APR approaches have been proposed using an encoder-decoder architecture. Despite the promising performance, these ...

The rise of Deep Learning (DL) has led researchers to explore techniques for Automatic Program Repair (APR) using DL. However, existing methods for APR suffer from issues such as small datasets, low accuracy, and long execution times. In this paper, we ...

Although Deep Neural Network (DNN) has led to unprecedented progress in various natural language processing (NLP) tasks, research shows that deep models are extremely vulnerable to backdoor attacks. The existing backdoor attacks mainly inject a ...$\mathit{}$

With the help of fog computing theory, this paper proposes Cluster Routing Optimized Algorithm of Nonlinear Event Migration Strategy, CR-NEMS. First, the fog node is used for high computing power and control ability to match and schedule sensor ...