Deep Learning-Based Defect Prediction for Mobile Applications

Jorayeva, Manzura; Akbulut, Akhan; Çatal, Çağatay; Mishra, Alok

doi:10.3390/s22134734

Cited by 9 publications

(1 citation statement)

References 34 publications

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“…The scheme has been implemented using multiple multi-layer perceptron (MLP) configuration over multiple standard datasets. Adoption of deep learning is also witnessed in work of Jorayeva et al [43] where LSTM is integrated with convolution neural network considering the user case of open-source Android application software defects. The analysis also has perform comparative analysis for ANN, convolution neural network (CNN), and LSTM to show that performance of CNN and LSTM is always better than ANN while CNN is slightly more better than LSTM.…”

Section: Existing Studies Deploying Deep Learning Approachesmentioning

confidence: 99%

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Rai,

Arcot Prashant

2024

IJECE

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Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

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Section: Existing Studies Deploying Deep Learning Approachesmentioning

confidence: 99%

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Rai,

Arcot Prashant

2024

IJECE

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Just-in-time defect prediction for mobile applications: using shallow or deep learning?

Dinter

Çatal

Giray³

et al. 2023

Software Qual J

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Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.

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