A GitHub-Based Data Collection Method for Software Defect Prediction

Xu, Jiaxi; Liang, Yan; Wang, Fei; Ai, Jun

doi:10.1109/dsa.2019.00020

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…It is also interesting to investigate if the combination of comments as natural language texts and source code could further improve software defect prediction performance. It would also be of great value to replicate our findings in just-in-time software defect prediction using the latest datasets, e.g., the GHPR dataset [50].…”

mentioning

confidence: 79%

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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confidence: 79%

An Empirical Study on Software Defect Prediction Using CodeBERT Model

Pan

2021

Applied Sciences

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“…GHPR dataset encompassing a wide range of features relevant to software defect prediction was utilized to construct efficient prediction models provided by Jiaxi Xu [7]. The dataset consists of 6052 instances used for the baseline approaches.…”

Section: Data Collectionmentioning

confidence: 99%

An empirically based object-oriented testing using Machine learning

Sindhu,

Peruri,

Yalavarthi

2024

EAI Endorsed Trans IoT

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INTRODUCTION: The rapid growth of machine learning has the potential to revolutionize various industries and applications by automating complex tasks and enhancing efficiency. Effective software testing is crucial for ensuring software quality and minimizing resource expenses in software engineering. Machine learning techniques play a vital role in software testing by aiding in test case prioritization, predicting software defects, and analyzing test results. OBJECTIVES: The primary objective of this study is to explore the use of machine learning algorithms for software defect prediction. METHODS: Machine Learning models including Random Forest Classifier, Logistic Regression, K Nearest Neighbors, Gradient Boosting Classifiers, Catboost Classifier, and Convolutional Neural Networks have been employed for the study. The dataset includes a wide range of features relevant to software defect prediction and evaluates the performance of different prediction models. The study also focussed on developing hybrid models using stacking classifiers, which combine multiple individual models to improve accuracy. RESULTS: The experimental results show that the hybrid models combining CatBoost and Convolutional Neural Network have outperformed individual models, achieving the highest accuracy of 89.5%, highlighting the effectiveness of combining machine learning algorithms for software defect prediction. CONCLUSION: In conclusion, this study sheds light on the pivotal role of machine learning in enhancing software defect prediction.

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“…All GHPR-related information is stored on GitHub, and this data can be accessed through the GitHub API. 5 Due to the ease of collecting information using the GitHub API, several studies have utilized it to gather data for various Software Engineering (SE) research tasks [12,13,15,16,31].…”

Section: Github Pull-request (Ghpr)mentioning

confidence: 99%

“…However, given that Ansible is a collection of code, its code quality directly correlates with the quality of service within the Edge-Cloud system. Consequently, various studies have been dedicated to ensuring the correctness of Ansible, thereby assuring service quality [5,7,10,22,24,31].…”

Section: Introductionmentioning

confidence: 99%

Exploring LLM-based Automated Repairing of Ansible Script in Edge-Cloud Infrastructures

Kwon,

Lee,

Kim

et al. 2023

JWE

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Edge-Cloud system requires massive infrastructures located in closer to the user to minimize latencies in handling Big data. Ansible is one of the most popular Infrastructure as Code (IaC) tools crucial for deploying these infrastructures of the Edge-cloud system. However, Ansible also consists of code, and its code quality is critical in ensuring the delivery of high-quality services within the Edge-Cloud system. On the other hand, the Large Langue Model (LLM) has performed remarkably on various Software Engineering (SE) tasks in recent years. One such task is Automated Program Repairing (APR), where LLMs assist developers in proposing code fixes for identified bugs. Nevertheless, prior studies in LLM-based APR have predominantly concentrated on widely used programming languages (PL), such as Java and C, and there has yet to be an attempt to apply it to Ansible. Hence, we explore the applicability of LLM-based APR on Ansible. We assess LLMs’ performance (ChatGPT and Bard) on 58 Ansible script revision cases from Open Source Software (OSS). Our findings reveal promising prospects, with LLMs generating helpful responses in 70% of the sampled cases. Nonetheless, further research is necessary to harness this approach’s potential fully.

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A GitHub-Based Data Collection Method for Software Defect Prediction

Cited by 10 publications

References 12 publications

An Empirical Study on Software Defect Prediction Using CodeBERT Model

An Empirical Study on Software Defect Prediction Using CodeBERT Model

An empirically based object-oriented testing using Machine learning

Exploring LLM-based Automated Repairing of Ansible Script in Edge-Cloud Infrastructures

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