The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and *** previous software defect prediction st...
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The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and *** previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data ***,target projects often lack sufficient data,which affects the performance of the transfer learning *** addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning *** address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual *** method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code *** the model training process,target project data are not required as prior *** the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the *** approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction *** evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE *** experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction ***,SDP-SL can effectively enhance within-and cross-project defect predictions.
The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In pra...
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The (k, z)-Clustering problem in Euclidean space d has been extensively studied. Given the scale of data involved, compression methods for the Euclidean (k, z)-Clustering problem, such as data compression and dimensio...
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Let φ: V × V → W be a bilinear map of finite vector spaces V and W over a finite field Fq. We present asymptotic bounds on the number of isomorphism classes of bilinear maps under the natural action of GL(V) an...
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Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. With the increasing interest in interconnection networks, an extens...
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Modeling various types of users’ interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existi...
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As an emerging replicated transactional processing system, blockchain is being adopted by more and more industries. Some blockchain-based applications require low latency and high throughput. However, the ordering pha...
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This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurat...
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Diffusion models have revolutionized image synthesis applications. Many studies focus on using approximate computation such as model quantization to reduce inference costs on mobile devices. However, due to their exte...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-de...
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Unmanned Aerial Vehicles (UAVs) have extensive applications such as logistics transportation and aerial photography. However, UAVs are sensitive to winds. Traditional control methods, such as proportional- integral-derivative controllers, generally fail to work well when the strength and direction of winds are changing frequently. In this work deep reinforcement learning algorithms are combined with a domain randomization method to learn robust wind-resistant hovering policies. A novel reward function is designed to guide learning. This reward function uses a constant reward to maintain a continuous flight of a UAV as well as a weight of the horizontal distance error to ensure the stability of the UAV at altitude. A five-dimensional representation of actions instead of the traditional four dimensions is designed to strengthen the coordination of wings of a UAV. We theoretically explain the rationality of our reward function based on the theories of Q-learning and reward shaping. Experiments in the simulation and real-world application both illustrate the effectiveness of our method. To the best of our knowledge, it is the first paper to use reinforcement learning and domain randomization to explore the problem of robust wind-resistant hovering control of quadrotor UAVs, providing a new way for the study of wind-resistant hovering and flying of UAVs. IEEE
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