@article {MTMT:31361026,
	title = {An automatically created novel bug dataset and its validation in bug prediction},
	journal = {JOURNAL OF SYSTEMS AND SOFTWARE},
	volume = {169},
	year = {2020},
	abstract = {Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.;therefore, it is important to have tools to find these errors. One way of performing bug identifications to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning.We present the BugHunter Dataset: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest time frame we can identify for a bug{\textquoteright}s presence,regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74.},
	keywords = {Bug dataset, Bug prediction, Code metrics, GitHub, Machine-learning, Static code analysis},
	issn = {0164-1212},
	doi = {10.1016/j.jss.2020.110691},
	url = {https://m2.mtmt.hu/api/publication/31361026},
	author = {Ferenc, Rudolf and Gyimesi, P{\'e}ter and Gyimesi, G{\'a}bor and T{\'o}th, Zolt{\'a}n G{\'a}bor and Tibor Gyim{\'o}thy}
}
@article {GVS20,
	title = {BugsJS: a Benchmark and Taxonomy of JavaScript Bugs},
	journal = {Software Testing, Verification and Reliability},
	volume = {n/a},
	number = {n/a},
	year = {2020},
	note = {e1751 stvr.1751},
	pages = {e1751},
	doi = {10.1002/stvr.1751},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/stvr.1751},
	author = {Gyimesi, P{\'e}ter and Vancsics, B{\'e}la and Stocco, Andrea and Mazinanian, Davood and Besz{\'e}des, {\'A}rp{\'a}d and Ferenc, Rudolf and Mesbah, Ali}
}
@conference {TGF16,
	title = {A Public Bug Database of {GitHub} Projects and Its Application in Bug Prediction},
	booktitle = {Proceedings of the 16th International Conference on Computational Science and Its Applications (ICCSA 2016)},
	year = {2016},
	pages = {625{\textendash}638},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Beijing, China},
	abstract = {Detecting defects in software systems is an evergreen topic, since there is no real world software without bugs. Many different bug locating algorithms have been presented recently that can help to detect hidden and newly occurred bugs in software. Papers trying to predict the faulty source code elements or code segments in the system always use experience from the past. In most of the cases these studies construct a database for their own purposes and do not make the gathered data publicly available. Public datasets are rare; however, a well constructed dataset could serve as a benchmark test input. Furthermore, open-source software development is rapidly increasing that also gives an opportunity to work with public data. In this study we selected 15 Java projects from GitHub to construct a public bug database from. We matched the already known and fixed bugs with the corresponding source code elements (classes and files) and calculated a wide set of product metrics on these elements. After creating the desired bug database, we investigated whether the built database is usable for bug prediction. We used 13 machine learning algorithms to address this research question and finally we achieved F-measure values between 0.7 and 0.8. Beside the F-measure values we calculated the bug coverage ratio on every project for every machine learning algorithm. We obtained very high and promising bug coverage values (up to 100\%).},
	keywords = {Bug database, Bug prediction},
	doi = {10.1007/978-3-319-42089-9_44},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-42089-9_44},
	author = {T{\'o}th, Zolt{\'a}n and Gyimesi, P{\'e}ter and Rudolf Ferenc}
}
@conference {GGT15,
	title = {Characterization of Source Code Defects by Data Mining Conducted on {GitHub}},
	booktitle = {Proceedings of the 15th International Conference on Computational Science and Its Applications (ICCSA 2015)},
	series = {Lecture Notes in Computer Science (LNCS)},
	volume = {9159},
	year = {2015},
	month = {jun},
	pages = {47{\textendash}62},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Banff, Alberta, Canada},
	abstract = {In software systems the coding errors are unavoidable due to the frequent source changes, the tight deadlines and the inaccurate specifications. Therefore, it is important to have tools that help us in finding these errors. One way of supporting bug prediction is to analyze the characteristics of the previous errors and identify the unknown ones based on these characteristics. This paper aims to characterize the known coding errors. Nowadays, the popularity of the source code hosting services like GitHub are increasing rapidly. They provide a variety of services, among which the most important ones are the version and bug tracking systems. Version control systems store all versions of the source code, and bug tracking systems provide a unified interface for reporting errors. Bug reports can be used to identify the wrong and the previously fixed source code parts, thus the bugs can be characterized by static source code metrics or by other quantitatively measured properties using the gathered data. We chose GitHub for the base of data collection and we selected 13 Java projects for analysis. As a result, a database was constructed, which characterizes the bugs of the examined projects, thus can be used, inter alia, to improve the automatic detection of software defects.},
	keywords = {Bug database, Data mining, GitHub},
	doi = {10.1007/978-3-319-21413-9_4},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-21413-9_4},
	author = {Gyimesi, P{\'e}ter and Gyimesi, G{\'a}bor and T{\'o}th, Zolt{\'a}n and Rudolf Ferenc}
}
