PyTraceBugs: A Large Python Code Dataset for Supervised Machine Learning in Software Defect Prediction

Akimova, Elena N.; Bersenev, Alexander; Deikov, Artem A.; Kobylkin, K. S.; Konygin, A. V.; Mezentsev, Ilya P.; Misilov, Vladimir E.

doi:10.1109/apsec53868.2021.00022

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The experimental dataset is derived from PyTraceBugs [33] and includes 23,736 defective snippets and 41,012 clean snippets. The non-defective snippets in this dataset are collected from stable projects in distinct GitHub repositories, while the defective snippets are selected from bug fixes and pull requests in various top repositories.…”

Section: Datasetmentioning

confidence: 99%

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Wang,

Lu,

Yang

et al. 2024

Int J Comput Intell Syst

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Software Defect Detection (SDD) has always been critical to the development life cycle. A stable defect detection system can not only alleviate the workload of software testers but also enhance the overall efficiency of software development. Researchers have recently proposed various artificial intelligence-based SDD methods and achieved significant advancements. However, these methods still exhibit limitations in terms of reliability and usability. Therefore, we introduce MSDD-(IA)3, a novel framework leveraging the pre-trained CodeT5+ and (IA)3 for parameter-efficient multi-classification SDD. This framework constructs a detection model based on pre-trained CodeT5+ to generate code representations while capturing defect-prone features. Considering the high overhead of pre-trained LLMs, we injects (IA)3 vectors into specific layers, where only these injected parameters are updated to reduce the training cost. Furthermore, leveraging the properties of the pre-trained CodeT5+, we design a novel feature sequence that enriches the input data through the combination of source code with Natural Language (NL)-based expert metrics. Our experimental results on 64K real-world Python snippets show that MSDD-(IA)3 demonstrates superior performance compared to state-of-the-art SDD methods, including PM2-CNN, in terms of F1-weighted, Recall-weighted, Precision-weighted, and Matthews Correlation Coefficient. Notably, the training parameters of MSDD-(IA)3 are only 0.04% of those of the original CodeT5+. Our experimental data and code can be available at (https://gitee.com/wxyzjp123/msdd-ia3/).

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Section: Datasetmentioning

confidence: 99%

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Wang,

Lu,

Yang

et al. 2024

Int J Comput Intell Syst

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“…Currently, machine learning has been widely applied in various stages of software engineering. By using machine learning, we can solve the problems of incomplete modelling and algorithm defects encountered in software development [10,11]. Machine learning can also perform data analysis tasks in software engineering, such as small dataset engineering problems [12], software requirements and code review problems [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Guan,

Xu,

Cai

2024

Mathematics

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To address the cost and efficiency issues of manually analysing requirement dependency in requirements engineering, a requirement dependency extraction method based on part-of-speech features and an improved stacking ensemble learning model (P-Stacking) is proposed. Firstly, to overcome the problem of singularity in the feature extraction process, this paper integrates part-of-speech features, TF-IDF features, and Word2Vec features during the feature selection stage. The particle swarm optimization algorithm is used to allocate weights to part-of-speech tags, which enhances the significance of crucial information in requirement texts. Secondly, to overcome the performance limitations of standalone machine learning models, an improved stacking model is proposed. The Low Correlation Algorithm and Grid Search Algorithms are utilized in P-stacking to automatically select the optimal combination of the base models, which reduces manual intervention and improves prediction performance. The experimental results show that compared with the method based on TF-IDF features, the highest F1 scores of a standalone machine learning model in the three datasets were improved by 3.89%, 10.68%, and 21.4%, respectively, after integrating part-of-speech features and Word2Vec features. Compared with the method based on a standalone machine learning model, the improved stacking ensemble machine learning model improved F1 scores by 2.29%, 5.18%, and 7.47% in the testing and evaluation of three datasets, respectively.

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Graph-Based Fault Localization in Python Projects with Class-Imbalanced Learning

Kulkarni,

Niranjan,

Saju

et al. 2024

Communications in Computer and Information Science

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PyTraceBugs: A Large Python Code Dataset for Supervised Machine Learning in Software Defect Prediction

Cited by 6 publications

References 13 publications

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Graph-Based Fault Localization in Python Projects with Class-Imbalanced Learning

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