Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting t...
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Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. This exploration aims to guide further advancements in the field, fostering a deeper understanding and refining practices for effective bug localization. Our study suggests that the integration of DL in IRBL enhances the model’s capacity to extract semantic and syntactic information from both bug reports and source code, addressing issues such as lexical gaps, neglect of code structure information, and cold-start problems. Future research avenues for IRBL encompass exploring diversity in programming languages, adopting fine-grained granularity, and focusing on real-world applications. Most importantly, although some studies have started using large language models for IRBL, there is still a need for more in-depth exploration and thorough investigation in this area.
Non-annotated visual description (NaVD) aims to describe generic visuals without human-annotated pairwise data. The generic visuals refer to images and videos. Existing works mainly focus on one specific visual modali...
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Non-annotated visual description (NaVD) aims to describe generic visuals without human-annotated pairwise data. The generic visuals refer to images and videos. Existing works mainly focus on one specific visual modality, i.e., image or video. In this paper, we propose a new framework for this task, which can directly be applied to both image and video with the pipeline unchanged. Essentially, it is a unified framework that flexibly adapts to images and videos. Recently, contrastive visual-language pre-training models (CVLPs) have experienced rapid development, demonstrating powerful abilities to align vision and language. To continuously leverage advanced CVLPs, our framework is designed to work well with general CVLPs. It can easily use image-language CVLPs for image input and switch to video-language CVLPs for video input. Specifically, we propose a CVLP-based framework for NaVD, named CVLP-NaVD. It follows the paradigm of adversarial learning, containing a generator and a discriminator. The generator takes an image or a video as input and produces a corresponding language description, while the discriminator evaluates the generated sentence for its naturalness in human-like language. Apart from the naturalness, CVLPs play a crucial role in enhancing the alignment between visual and language signals during generation. Particularly, we explore three rewarding strategies to compute the alignment score, including directly calculating cosine similarity (i.e., VL-cross), projecting visual embeddings into the textual domain (i.e., VL-project) and their combination (i.e., VL-mix). The three strategies are fully examined in different scenarios. Finally, we conduct extensive experiments with various unpaired and unsupervised setups in both image and video captioning tasks. The experimental results demonstrate that our CVLP-NaVD outperforms the state-of-the-art methods significantly.
This book gathers the proceedings of the Sixth International Conference on Computational science and Technology 2019 (ICCST2019), held in Kota Kinabalu, Malaysia, on 29–30 August 2019. The respective contributions of...
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ISBN:
(数字)9789811500589
ISBN:
(纸本)9789811500572;9789811500602
This book gathers the proceedings of the Sixth International Conference on Computational science and Technology 2019 (ICCST2019), held in Kota Kinabalu, Malaysia, on 29–30 August 2019. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods.
This volume constitutes the refereed proceedings of the 12th International Symposium on Spatial and Temporal databases, SSTD 2011, held in Minneapolis, USA, in August 2011. The 24 revised full papers presented togethe...
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ISBN:
(数字)9783642229220
ISBN:
(纸本)9783642229213
This volume constitutes the refereed proceedings of the 12th International Symposium on Spatial and Temporal databases, SSTD 2011, held in Minneapolis, USA, in August 2011. The 24 revised full papers presented together with one keynote, 8 short papers, and 8 demonstration papers, were thoroughly reviewed and selected from a total of 63 research submissions, 21 vision and challenges submissions and 16 demonstration submissions. The papers are organized in topical sections on knowledge discovery; spatial networks; access methods; moving objects and sensor networks; multidimensional query processing; and temporal and streaming data.
Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data manag...
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Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.
Evolutionary computation (EC) techniques are e?cient, nature-inspired me- ods based on the principles of natural evolution and genetics. Due to their - ciency and simple underlying principles, these methods can be use...
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ISBN:
(数字)9783642122422
ISBN:
(纸本)9783642122415
Evolutionary computation (EC) techniques are e?cient, nature-inspired me- ods based on the principles of natural evolution and genetics. Due to their - ciency and simple underlying principles, these methods can be used for a diverse rangeofactivitiesincludingproblemsolving,optimization,machinelearningand pattern recognition. A large and continuously increasing number of researchers and professionals make use of EC techniques in various application domains. This volume presents a careful selection of relevant EC examples combined with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and should help and inspire researchers and professionals to develop e?cient EC methods for design and problem solving. All papers in this book were presented during EvoApplications 2010, which included a range of events on application-oriented aspects of EC. Since 1998, EvoApplications — formerly known as EvoWorkshops — has provided a unique opportunity for EC researchers to meet and discuss application aspects of EC and has been an important link between EC research and its application in a variety of domains. During these 12 years, new events have arisen, some have disappeared,whileothershavematuredtobecomeconferencesoftheirown,such as EuroGP in 2000, EvoCOP in 2004, and EvoBIO in 2007. And from this year, EvoApplications has become a conference as well.
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