Abstract
When developing a large scale software product, it is essential to share a common set of structural coding guidelines and standards among the project team members. In this paper, we propose MergeLogging, a deep learning-based merged network using various code representations for automated logging decisions or other tasks. MergeLogging archives the enhanced recommendation ability that utilizes orthogonal code features from code representations. Our case study with three open-source project datasets demonstrates that logging accuracy can reach as high as 93%.
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1E1A1A01075803, NRF-2018R1D1A1A02086102, NRF-2020R1A2C2009809).
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Lee, S., Lee, Y., Lee, CG., Woo, H. (2021). Deep Learning-Based Logging Recommendation Using Merged Code Representation. In: Kim, H., Kim, K.J. (eds) IT Convergence and Security. Lecture Notes in Electrical Engineering, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-15-9354-3_5
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DOI: https://doi.org/10.1007/978-981-15-9354-3_5
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