Empirical Study of Software Defect Prediction: A Systematic Mapping

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.

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An Empirical Study on Software Defect Prediction Using CodeBERT Model

Applied Sciences ◽

10.3390/app11114793 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4793

Author(s):

Cong Pan ◽

Minyan Lu ◽

Biao Xu

Keyword(s):

Deep Learning ◽

Software Engineering ◽

Empirical Study ◽

Empirical Studies ◽

Language Model ◽

Prediction Performance ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defect ◽

Cross Project

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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Training Data Selection Using Ensemble Dataset Approach for Software Defect Prediction

Cyber Security and Computer Science - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ◽

10.1007/978-3-030-52856-0_19 ◽

2020 ◽

pp. 243-256

Author(s):

Md Fahimuzzman Sohan ◽

Md Alamgir Kabir ◽

Mostafijur Rahman ◽

S. M. Hasan Mahmud ◽

Touhid Bhuiyan

Keyword(s):

Training Data ◽

Data Selection ◽

Defect Prediction ◽

Software Defect Prediction ◽

Software Defect ◽

Training Data Selection

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Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

Symmetry ◽

10.3390/sym12071147 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1147 ◽

Cited By ~ 2

Author(s):

Abdullateef O. Balogun ◽

Shuib Basri ◽

Saipunidzam Mahamad ◽

Said J. Abdulkadir ◽

Malek A. Almomani ◽

...

Keyword(s):

Feature Selection ◽

Empirical Study ◽

Prediction Models ◽

Empirical Studies ◽

Experimental Results ◽

Defect Prediction ◽

Software Defect Prediction ◽

Search Methods ◽

Software Defect ◽

The Impact

Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). Moreover, many empirical studies on the impact and effectiveness of FS methods on SDP models often lead to contradictory experimental results and inconsistent findings. These contradictions can be attributed to relative study limitations such as small datasets, limited FS search methods, and unsuitable prediction models in the respective scope of studies. It is hence critical to conduct an extensive empirical study to address these contradictions to guide researchers and buttress the scientific tenacity of experimental conclusions. In this study, we investigated the impact of 46 FS methods using Naïve Bayes and Decision Tree classifiers over 25 software defect datasets from 4 software repositories (NASA, PROMISE, ReLink, and AEEEM). The ensuing prediction models were evaluated based on accuracy and AUC values. Scott–KnottESD and the novel Double Scott–KnottESD rank statistical methods were used for statistical ranking of the studied FS methods. The experimental results showed that there is no one best FS method as their respective performances depends on the choice of classifiers, performance evaluation metrics, and dataset. However, we recommend the use of statistical-based, probability-based, and classifier-based filter feature ranking (FFR) methods, respectively, in SDP. For filter subset selection (FSS) methods, correlation-based feature selection (CFS) with metaheuristic search methods is recommended. For wrapper feature selection (WFS) methods, the IWSS-based WFS method is recommended as it outperforms the conventional SFS and LHS-based WFS methods.

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Software Defect Prediction Using Genetic Programming and Neural Networks

International Journal of Open Source Software and Processes ◽

10.4018/ijossp.2017100102 ◽

2017 ◽

Vol 8 (4) ◽

pp. 32-51 ◽

Cited By ~ 2

Author(s):

Mohammed Akour ◽

Wasen Yahya Melhem

Keyword(s):

Neural Networks ◽

Genetic Programming ◽

Detailed Analysis ◽

Software Quality ◽

The Other ◽

Defect Prediction ◽

Data Repository ◽

Software Defect Prediction ◽

Classification Methods ◽

Software Defect

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.

Download Full-text