Seml: A Semantic LSTM Model for Software Defect Prediction

Liu, Hongliang; Yu, Yue; Jiang, Lin; Xie, Zhuosi

doi:10.1109/access.2019.2925313

Cited by 83 publications

(58 citation statements)

References 46 publications

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“…As the most commonly used measure in the F-measure family, F1 is the harmonic mean of recall and precision, which is defined as (6). Where Recall is the percentage of positive instances correctly classified, and Precision is the percentage of true positive instances in the instances predicted to be positive.…”

Section: B Evaluation Measurementmentioning

confidence: 99%

“…Unfortunately, reducing the high debugging costs and the number of software defects is a challenging problem, especially considering the limited testing resources of software team [3], [4], and often facing strong pressure for rapid delivery [3], [5]. Therefore, researchers have introduced machine learning methods to predict defects in software source code [6], such as Naive Bayes (NB) [7], [8], support vector machine (SVM) [7], decision trees [8], and neural networks [9]. Malhotra et al have been proposed for software defect detection based on the measure of internal metrics and defect data from similar projects or earlier releases to construct defect detection models [2].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Imbalanced Ensemble Learning in Software Defect Predication

et al. 2021

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With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the characteristics of imbalanced distribution of defect data sets have led to the deviation and loss of accuracy of most software defect prediction methods. This paper presents two novel approaches for learning from imbalanced data sets to produce a higher predictive accuracy over the minority class. These two methods differ in whether the oversampling and the misclassification cost information are utilized during training stage, and they are good at different aspects of imbalanced classification, one for dealing with highly imbalanced data sets and the other for moderately imbalanced data sets. Comparing with other state-of-the-art imbalance learning algorithms on imbalanced datasets, the experimental results show that these two methods have achieved excellent results in terms of G-mean and AUC measures, and more accurately identified the defective modules to reduce the cost of detection system.

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Section: B Evaluation Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Imbalanced Ensemble Learning in Software Defect Predication

et al. 2021

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“…For CPDP, the model achieved very high Recall across all projects. Liang et al (2019) combined word embedding with the LSTM model for defect prediction. The mapping table was used to map each token to realvalued vector.…”

Section: E Frameworkmentioning

confidence: 99%

A Systematic Literature Review of Software Defect Prediction Using Deep Learning

Fathy¹,

Abd-Elmegid²,

Bahaa³

et al. 2021

Journal of Computer Science

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The approaches associated with software defect prediction are used to reduce the time and cost of discovering software defects in source code and to improve the software quality in the organizations. There are two approaches to reveal the software defects in the source code. The first approach is concentrated on the traditional features such as lines of code, code complexity, etc. However, these features fail to extract the semantics of the source code. The second one is concentrated on revealing these semantics. This paper presents a Systematic Literature Review (SLR) of software defect prediction using deep learning models. This SLR is focused on identifying the studies that use the semantics of the source code for improving defect prediction. This SLR aims to analyze the used datasets, models and frameworks. Also, identifying the evaluation metrics to ensure their applicability in software defect prediction. IEEE Xplore, Scopus and Web of Science digital libraries were used to select the suitable primary studies. Forty (40) primary studies were selected that published by 15 December 2020 for analysis based on the quality criteria. The project levels that applied in the studies were: Within-project 52.5%, cross-project 17.5% and both within-project and cross-project 30%. The datasets used were: Promise dataset 68.18% and other datasets 31.82%. The most used deep learning model in the primary studies was: Convolutional Neural Network (CNN) by 35%. The most used evaluation metrics were: F-measure and Area Under the Curve (AUC). Software defect prediction using deep learning models is still a valuable topic and requires much research studies to enhance the performance of the defect prediction.

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“…Reference [41] They combined the word embedding and Long Short-Term Memory (LSTM) algorithm for defect prediction. The proposed model contains three steps; the first step extracts a token from its abstract syntax tree.…”

Section: Deep Learningmentioning

confidence: 99%

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

2020

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The discovery of software faults at early stages plays an important role in improving software quality; reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault. Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields. In this study, two deep learning algorithms are studied, Multi-layer perceptron's (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms. The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached. Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm. To validate our assumptions, the experiments are conducted on four common NASA datasets. The result shows how the addressed factors might increase or decrease the fault detection rate measurement. The improvement rate was as follows up to 43.5% for PC1, 8% for KC1, 18% for KC2 and 76.5% for CM1.

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Seml: A Semantic LSTM Model for Software Defect Prediction

Cited by 83 publications

References 46 publications

A Novel Imbalanced Ensemble Learning in Software Defect Predication

A Novel Imbalanced Ensemble Learning in Software Defect Predication

A Systematic Literature Review of Software Defect Prediction Using Deep Learning

The Influence of Deep Learning Algorithms Factors in Software Fault Prediction

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