An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors

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Abstract
Software Defects Prediction represents an essential activity during software development that contributes to continuously improving software quality and software maintenance and evolution by detecting defect-prone modules in new versions of a software system. In this paper, we are conducting an in-depth analysis on the software features’ impact on the performance of deep learning-based software defect predictors. We further extend a large-scale feature set proposed in the literature for detecting defect-proneness, by adding conceptual software features that capture the semantics of the source code, including comments. The conceptual features are automatically engineered using Doc2Vec, an artificial neural network based prediction model. A broad evaluation performed on the Calcite software system highlights a statistically significant improvement obtained by applying deep learning-based classifiers for detecting software defects when using conceptual features extracted from the source code for characterizing the software entities.
Funding Information
  • Ministry of Research, Innovation and Digitization, CNCS/CCCDI–UEFISCDI, within PNCDI III (PN-III-P4-ID-PCE-2020-0800)

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