Review of Open Software Bug Datasets
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Review_of_Open_Software_Defect ...
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Final Accepted Manuscript
Affiliation
Systems and Industrial Engineering, University of ArizonaIssue Date
2024-02-14Keywords
Bug DatasetBug Localisation and Prediction
Literature Review
Software Bugs
Software Quality
Software Testing
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Springer Nature SwitzerlandCitation
Holek, T., Bures, M., Cerny, T. (2024). Review of Open Software Bug Datasets. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-031-45648-0_1Rights
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The localisation of the bug position in a source code and the prediction of which specific parts of a source code might be the cause of defects play an important role in maintaining software quality. Both approaches are based on applying information retrieval techniques and machine learning or deep learning methods. The prerequisite for using these approaches is the availability of a consistent bug dataset of sufficient size. This paper presents an overview of available public bug datasets and analyses their specific application areas. The paper also suggests possible future research directions in this field.Note
12 month embargo; first published 14 February 2024ISSN
2367-33709783031456473
9783031456480