Warning Classification Method Based On Artificial Neural Network Using Topics of Source Code


KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 11, pp. 273-280, Nov. 2020
https://doi.org/10.3745/KTCCS.2020.9.11.273,   PDF Download:
Keywords: software engineering, Static Analysis, Warning Classification, Artificial neural network, topic modeling
Abstract

Automatic Static Analysis Tools help developers to quickly find potential defects in source code with less effort. However, the tools reports a large number of false positive warnings which do not have to fix. In our study, we proposed an artificial neural network-based warning classification method using topic models of source code blocks. We collect revisions for fixing bugs from software change management (SCM) system and extract code blocks modified by developers. In deep learning stage, topic distribution values of the code blocks and the binary data that present the warning removal in the blocks are used as input and target data in an simple artificial neural network, respectively. In our experimental results, our warning classification model based on neural network shows very high performance to predict label of warnings such as true or false positive.


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Cite this article
[IEEE Style]
J. Lee, "Warning Classification Method Based On Artificial Neural Network Using Topics of Source Code," KIPS Transactions on Computer and Communication Systems, vol. 9, no. 11, pp. 273-280, 2020. DOI: https://doi.org/10.3745/KTCCS.2020.9.11.273.

[ACM Style]
Jung-Been Lee. 2020. Warning Classification Method Based On Artificial Neural Network Using Topics of Source Code. KIPS Transactions on Computer and Communication Systems, 9, 11, (2020), 273-280. DOI: https://doi.org/10.3745/KTCCS.2020.9.11.273.