Software defect prediction via LSTM

Deng, Jiehan; Lu, Lu; Qiu, Shaojian

doi:10.1049/iet-sen.2019.0149

Cited by 46 publications

(36 citation statements)

References 32 publications

(37 reference statements)

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“…Subsequently, they extracted semantic features from source code changes and extended change-level defect prediction [ 16 ]. Deng et al [ 17 ] proposed a defect-prediction framework based on bidirectional long short-term memory network (Bi-LSTM). The long-distance dependence of Bi-LSTM can better learn contextual semantic features in long-sequence data.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with coarse-grained prediction, fine-grained methods can render software testing more reasonable when allocating resources. Recent studies in Java software have shown that the file-level prediction model is more effective than that of the package-level [17][18][19][20][21][22], and the method-level [23][24][25] prediction model is more effective than those of the package-level and file-level. Pascarella et al [25] used different systems and periods to replicate the research of Giger et al [26] on method-level bug prediction, and they analysed the defect-prediction performance under actual conditions.…”

Section: Defect Prediction Based On Fine-grained Analysismentioning

confidence: 99%

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Fine-Grained Software Defect Prediction Based on the Method-Call Sequence

Yang

Huang

et al. 2022

Computational Intelligence and Neuroscience

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Currently, software defect-prediction technology is being extensively researched in the design of metrics. However, the research objects are mainly limited to coarse-grained entities such as classes, files, and packages, and there is a wide range of defects that are difficult to predict in actual situations. To further explore the information between sequences of method calls and to learn the code semantics and syntactic structure between methods, we generated a method-call sequence that retains the code context structure information and the token sequence representing semantic information. We embedded the token sequence into the method-call sequence and encoded it into a fixed-length real-valued vector. We then built a defect-prediction model based on the transformer, which maps the code-vector representation containing the method-call sequences to a low-dimensional vector space to generate semantic features and syntactic structure features and also predicts the defect density of the method-call sequence. We conducted experiments on 10 open-source projects using the ELFF dataset. The experimental results show that the method-call sequence-level prediction effect is better than the class-level effect, and the prediction results are more stable than those of the method level. The mean absolute error (MAE) value of our approach was 8% lower than that of the other deep-learning methods.

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Section: Related Workmentioning

confidence: 99%

Section: Defect Prediction Based On Fine-grained Analysismentioning

confidence: 99%

Fine-Grained Software Defect Prediction Based on the Method-Call Sequence

Yang

Huang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Unlike designed top-down hand-crafted features, the features are generated bottom-up from the source code, which could represent structural and semantics information of the source code. Recently, many deep learning models, including Deep Belief Networks [4,15], CNN [5][6][7]10,17,19,24,25], LSTM [11,12,14,16,18], Transformers [8], and other deep learning models [13,22] are used in software defect prediction.…”

Section: Deep Transfer Learningmentioning

confidence: 99%

An Empirical Study on Software Defect Prediction Using CodeBERT Model

Pan

2021

Applied Sciences

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Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

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“…The results showed that the proposed framework improved Precision by 4.8%, Recall by 2.4% and F-measure by 8.5% on average. Dam et al (2019) proposed the deep tree-based LSTM model, the model took a raw AST of a source file to be used as input. The model used two classifiers (LR and Random Forest (RF) Zhang et al, 2018a), to choose the best one of them.…”

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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Software defect prediction via LSTM

Cited by 46 publications

References 32 publications

Fine-Grained Software Defect Prediction Based on the Method-Call Sequence

Fine-Grained Software Defect Prediction Based on the Method-Call Sequence

An Empirical Study on Software Defect Prediction Using CodeBERT Model

A Systematic Literature Review of Software Defect Prediction Using Deep Learning

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