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301 results on '"Kong, Weiqiang"'

20. Compiler Testing: A Systematic Literature Analysis

Author

Tang, Yixuan, Ren, Zhilei, Kong, Weiqiang, and Jiang, He

Subjects
Computer Science - Software Engineering
Abstract

Compilers are widely-used infrastructures in accelerating the software development, and expected to be trustworthy. In the literature, various testing technologies have been proposed to guarantee the quality of compilers. However, there remains an obstacle to comprehensively characterize and understand compiler testing. To overcome this obstacle, we propose a literature analysis framework to gain insights into the compiler testing area. First, we perform an extensive search to construct a dataset related to compiler testing papers. Then, we conduct a bibliometric analysis to analyze the productive authors, the influential papers, and the frequently tested compilers based on our dataset. Finally, we utilize association rules and collaboration networks to mine the authorships and the communities of interests among researchers and keywords. Some valuable results are reported. We find that the USA is the leading country that contains the most influential researchers and institutions. The most active keyword is "random testing". We also find that most researchers have broad interests within small-scale collaborators in the compiler testing area., Comment: 20 pages, 15 figures

Published
2018

30. ROSF: Leveraging Information Retrieval and Supervised Learning for Recommending Code Snippets

Author

Jiang, He, Nie, Liming, Sun, Zeyi, Ren, Zhilei, Kong, Weiqiang, Zhang, Tao, and Luo, Xiapu

Subjects
Computer Science - Software Engineering
Abstract

When implementing unfamiliar programming tasks, developers commonly search code examples and learn usage patterns of APIs from the code examples or reuse them by copy-pasting and modifying. For providing high-quality code examples, previous studies present several methods to recommend code snippets mainly based on information retrieval. In this paper, to provide better recommendation results, we propose ROSF, Recommending cOde Snippets with multi-aspect Features, a novel method combining both information retrieval and supervised learning. In our method, we recommend Top-Kcode snippets for a givenfree-form query based on two stages, i.e., coarse-grained searching and fine-grained re-ranking. First, we generate a code snippet candidate set by searching a code snippet corpus using an information retrieval method. Second, we predict probability values of the code snippets for different relevance scores in the candidate set by the learned prediction model from a training set, re-rank these candidate code snippets according to the probability values, and recommend the final results to developers. We conduct several experiments to evaluate our method in a large-scale corpus containing 921,713 real-world code snippets. The results show that ROSF is an effective method for code snippets recommendation and outperforms the-state-of-the-art methods by 20%-41% in Precision and 13%-33% in NDCG, Comment: 14 pages, 6 figures, IEEE Transactions on Services Computing, 2016

Published
2017
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31. A Dual Relaxation Method for Neural Network Verification.

Author

Xiong, Huanzhang, Hou, Gang, Qin, Yueyuan, Wang, Jie, and Kong, Weiqiang

Abstract

In the robustness verification of neural networks, formal methods have been used to give deterministic guarantees for neural networks. However, recent studies have found that the verification method of single-neuron relaxation in this field has an inherent convex barrier that affects its verification capability. To address this problem, we propose a new verification method by combining dual-neuron relaxation and linear programming. This method captures the dependencies between different neurons in the same hidden layer by adding a two-neuron joint constraint to the linear programming model, thus overcoming the convex barrier problem caused by relaxation for only a single neuron. Our method avoids the combination of exponential inequality constraints and can be computed in polynomial time. Experimental results show that we can obtain tighter bounds and achieve more accurate verification than single-neuron relaxation methods. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Software for Wearable Devices: Challenges and Opportunities

Author

Jiang, He, Chen, Xin, Zhang, Shuwei, Zhang, Xin, Kong, Weiqiang, and Zhang, Tao

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computers and Society
Abstract

Wearable devices are a new form of mobile computer system that provides exclusive and user-personalized services. Wearable devices bring new issues and challenges to computer science and technology. This paper summarizes the development process and the categories of wearable devices. In addition, we present new key issues arising in aspects of wearable devices, including operating systems, database management system, network communication protocol, application development platform, privacy and security, energy consumption, human-computer interaction, software engineering, and big data., Comment: 6 pages, 1 figure, for Compsac 2015

Published
2015

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