On the use of deep learning in software defect prediction

Giray, Görkem; Bennin, Kwabena Ebo; Köksal, Ömer; Babur, Önder; Tekinerdoğan, Bedir

doi:10.1016/j.jss.2022.111537

Cited by 55 publications

(13 citation statements)

References 173 publications

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“…As a result, the research on just-in-time is valued. Deep learning and hybrid learning have produced numbers of state-of-art methods that can significantly improve prediction performance, aiming to predict defects of both cross-project and within-project [15]. In this study had used Cross-project defect prediction, which often reuse data from other projects.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, he have modeled the outcomes using PROMISE dataset in five different modules and repositories: CM1, JM1, KC1, KC2, and PC1. We implemented the dataset using four different classifiers: Bayes network, Random forest, SVM, and the Deep Learning based on F-measure, making it more robust and outperform all the models available [12] [15]. However, current studies on software defect prediction require some degree of heterogeneity of metric values that does not always lead to accurate predictions.…”

Section: Related Workmentioning

confidence: 99%

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An Empirical Study of Classification Models Using AUC-ROC Curve for Software Fault Predictions

Sasankar¹,

Sakarkar²

2023

IJSRCSEIT

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Software bug prediction is the process of identifying software modules that are likely to have bugs by using some fundamental project resources before the real testing starts. Due to high cost in correcting the detected bugs, it is advisable to start predicting bugs at the early stage of development instead of at the testing phase. There are many techniques and approaches that can be used to build the prediction models, such as machine learning. We have studied nine different types of datasets and seven types of machine learning techniques have been identified. As for performance measures, both graphical and numerical measures are used to evaluate the performance of models. A few challenges exist when constructing a prediction model. In this study, we have narrowed down to nine different types of datasets and seven types of machine learning techniques have been identified. As for the performance measure, both graphical and numerical measures are used to evaluate the performance of the models. There are a few challenges in constructing the prediction model. Thus, more studies need to be carried out so that a well-formed result is obtained. We also provide a recommendation for future research based on the results we got from this study.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Empirical Study of Classification Models Using AUC-ROC Curve for Software Fault Predictions

Sasankar¹,

Sakarkar²

2023

IJSRCSEIT

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“…So, fixing defects is an important part of software maintenance, but it also wastes time and resources. Detecting software faults before software deployment is crucial, as the correct detection of faulty software modules or components allows good use of resources and time [1] [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Software Defects based on Machine Learning

Elshamy¹,

AbouElenen²,

Elmougy³

2023

IJACSA

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Defects in software are one of the critical problems in software engineering community because they provide inaccurate results and negatively affect the quality and reliability of the software. These defects must be detected in the early stages of software development. Researchers had used Software Defect Detection (SDD) techniques to allow predicting module faultproneness. By implementing the hyperparameter optimization techniques and exploiting data imbalances in predicting defects, this paper proposes and develops an SDD model with high performance and generalization capability. To classify defects in software modules, machine learning algorithms and ensemble learning techniques are used on the balanced datasets. The balanced datasets are obtained through using a hybrid of synthetic minority oversample (SMOTE) and Support Vector Machine (SVM). To obtain the optimal hyperparameters needed for the used classifiers and for the dataset balanced algorithms, Non-dominated Sorting Genetic Algorithm II (NDSGA-II) is used. To reduce the time and save other used resources, Hyperband technique, which is a multi-fidelity optimization, is used in NDSGA-II. A 10-fold Cross Validation (CV) is applied to overcome the overfitting and underfitting problems. The accuracy, recall, F-measure, and ROC AUC metrics are used to evaluate the SDD model. The results show that the proposed model predicts defects more accurately than the compared studies.

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“…The primary deep learning (DL) models commonly employed for software defect prediction (SDP) tasks are the Arti cial Neural Network (ANN), Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Multi-Layer Perceptron (MLP) (Atif et al, 2021). Furthermore, a recent systematic literature review conducted by (Giray et al 2023) on SDP highlights that CNN is the most frequently utilized DL technique, followed by MLP and DBN. However, it is worth noting that there is limited research focusing on the application of DL autoencoders for SDP as modern feature extraction methods.…”

Section: Introductionmentioning

confidence: 99%

“…As illustrated in Table1, the most commonly used DL models for SDP task include Arti cial Neural Network (ANN), Convolutional Neural Network (CNN), Long short-term memory (LSTM), Multi-Layer Perceptron (MLP)(Atif et al 2021). In addition, A recent systematic literature review on SDP performed by(Giray et al, 2023) shows that the most frequently used DL techniques include CNN followed by MLP and DBN.…”

mentioning

confidence: 99%

Comparison of Feature Selection via Semi supervised denoising autoencoder and traditional approaches For Software Fault-prone Classification

Amara

Rabai

2023

Preprint

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Software quality is the capability of a software process to produce software product satisfying the end user. The quality of process or product entities is described through a set of attributes that may be internal or external. For the product entity, especially, the source code, different internal attributes are defined to evaluate its quality like complexity and cohesion. Concerning external attributes related to the product environment like reliability, their assessment is more difficult. Thus, they are usually predicted by the development of prediction models based on software metrics as independent variables and other measurable attributes as dependent variables. For instance, reliability like other external attributes is generally measured and predicted based on other quality attributes like defect density, defect count and fault-proneness. The success of machine learning (ML) and deep learning (DL) approaches for software defect and faulty modules classification as crucial attributes for software reliability improvement is remarkable. In recent years, there has been growing interest in exploring the use of deep learning autoencoders, a type of neural network architecture, for software defect prediction. Therefore, we aim in this paper to explore the semi-supervised denoising DL autoencoder in order to capture relevant features. Then, we evaluate its performance in comparison to traditional ML supervised SVM technique for fault-prone modules classification. The performed experiments based on a set of software metrics extracted from NASA projects achieve promising results in terms of accuracy and show that denoising DL autoencoder outperforms traditional SVM technique.

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On the use of deep learning in software defect prediction

Cited by 55 publications

References 173 publications

An Empirical Study of Classification Models Using AUC-ROC Curve for Software Fault Predictions

An Empirical Study of Classification Models Using AUC-ROC Curve for Software Fault Predictions

Automatic Detection of Software Defects based on Machine Learning

Comparison of Feature Selection via Semi supervised denoising autoencoder and traditional approaches For Software Fault-prone Classification

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