@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 {MTMT:31336850,
	title = {A public unified bug dataset for java and its assessment regarding metrics and bug prediction},
	journal = {SOFTWARE QUALITY JOURNAL},
	volume = {28},
	year = {2020},
	pages = {1447-1506},
	abstract = {Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction. We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction. We found that there are statistically significant differences in the values of the original and the newly calculated metrics; furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.},
	keywords = {Bug dataset, Bug prediction, Code metrics, Static code analysis},
	issn = {0963-9314},
	doi = {10.1007/s11219-020-09515-0},
	url = {https://m2.mtmt.hu/api/publication/31336850},
	author = {Ferenc, Rudolf and T{\'o}th, Zolt{\'a}n G{\'a}bor and Lad{\'a}nyi, Gergely and Siket, Istv{\'a}n and Tibor Gyim{\'o}thy}
}
