Name
Title | ||
|---|---|---|
An in-Depth Analysis of the Software Features' Impact on the Performance of Deep Learning-Based Software Defect Predictors |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3181995 | IEEE ACCESS |
Keywords | DocType | Volume |
Software, Codes, Software systems, Feature extraction, Testing, Predictive models, Computer bugs, Deep learning, Doc2vec, latent semantic indexing, software defect prediction | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diana-Lucia Miholca | 1 | 7 | 3.47 |
| Vlad-Ioan Tomescu | 2 | 0 | 0.34 |
| Gabriela Czibula | 3 | 80 | 19.53 |