Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code

Miholca, Diana-Lucia; Czibula, Gabriela

doi:10.1007/978-3-030-29551-6_23

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The experimental results of previous studies [54], [56] we have conducted revealed that combining Doc2Vec and LSI is appropriate and increases the predictive performance.…”

Section: B Proposed Conceptual-based Featuresmentioning

confidence: 86%

“…In a previous study [56], we have also proposed a semantic features based hybrid SDP model combining Artificial Neural Networks with Gradual Relational Association Rules (GRARs). After encoding the source code and comments into fixed-length numeric vectors, GRARs mining has been employed to uncover interesting GRARs that are able to discriminate between defective and defect-free software components.…”

Section: Features Used For Sdpmentioning

confidence: 99%

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

2022

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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.

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“…The experimental results of previous studies [54], [56] we have conducted revealed that combining Doc2Vec and LSI is appropriate and increases the predictive performance.…”

Section: B Proposed Conceptual-based Featuresmentioning

confidence: 86%

Section: Features Used For Sdpmentioning

confidence: 99%

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

2022

Self Cite

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“…The structural class rates have increased with the Doc2Vec conversion. Contextual information rate was raised by the participation of semantics along with structural characteristics (Miholca et al, 2019) throughout the learning procedure. The files were transformed into token vectors by coding them using Abstract Syntax Trees (AST).…”

Section: Related Workmentioning

confidence: 99%

Optimized Ensemble Techniques For Precise Software Error Detection

Singh

2024

EATP

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On the Relevance of Graph2Vec Source Code Embeddings for Software Defect Prediction

Miholca,

Oneţ-Marian

2024

Communications in Computer and Information Science

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Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code

Cited by 6 publications

References 19 publications

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

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

Optimized Ensemble Techniques For Precise Software Error Detection

On the Relevance of Graph2Vec Source Code Embeddings for Software Defect Prediction

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