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[Author] Dongjin YU(2hit)

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  • Towards the Identification of Cross-Cutting Concerns: A Comprehensive Dynamic Approach Based on Execution Relations

    Dongjin YU  Xiang SU  Yunlei MU  

     
    PAPER-Software System

      Vol:
    E97-D No:5
      Page(s):
    1235-1243

    Aspect-oriented software development (AOSD) helps to solve the problem of low scalability and high maintenance costs of legacy systems caused by code scattering and tangling by extracting cross-cutting concerns and inserting them into aspects. Identifying the cross-cutting concerns of legacy systems is the key to reconstructing such systems using the approach of AOSD. However, current dynamic approaches to the identification of cross-cutting concerns simply check the methods' execution sequence, but do not consider their calling context, which may cause low precision. In this paper, we propose an improved comprehensive approach to the identification of candidate cross-cutting concerns of legacy systems based on the combination of the analysis of recurring execution relations and fan-ins. We first analyse the execution trace with a given test case and identify four types of execution relations for neighbouring methods: exit-entry, entry-exit, entry-entry and exit-exit. Afterwards, we measure the methods' left cross-cutting degrees and right cross-cutting degrees. The former ensures that the candidate recurs in a similar running context, whereas the latter indicates how many times the candidate cross-cuts different methods. The final candidates are then obtained from those high fan-in methods, which not only cross-cut others more times than a predefined threshold, but are always entered or left under the same running context. The experiment conducted on three open source systems shows that our approach improves the precision of identifying cross-cutting concerns compared with tradition ones.

  • Citation Count Prediction Based on Neural Hawkes Model

    Lisha LIU  Dongjin YU  Dongjing WANG  Fumiyo FUKUMOTO  

     
    PAPER-Biocybernetics, Neurocomputing

      Pubricized:
    2020/08/03
      Vol:
    E103-D No:11
      Page(s):
    2379-2388

    With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.