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- W4285152037 abstract "<i>Software Defects Prediction</i> represents an essential activity during software development that contributes to continuously improving <i>software quality</i> 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." @default.
- W4285152037 created "2022-07-14" @default.
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- W4285152037 date "2022-01-01" @default.
- W4285152037 modified "2023-10-14" @default.
- W4285152037 title "An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors" @default.
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- W4285152037 doi "https://doi.org/10.1109/access.2022.3181995" @default.
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