@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}
}
@conference {GBS16,
	title = {Enforcing Techniques and Transformation of {C/C++} Source Code to Heterogeneous Hardware},
	booktitle = {Proceedings of the 16th IEEE International Conference on Scalable Computing and Communication (ScalCom 2016)},
	year = {2016},
	month = {jul},
	pages = {1173{\textendash}1180},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Toulouse, France},
	abstract = {Besides well-known CPU based architectures, the so-called accelerators (GPU, DSP, FPGA) are about to gain ground in everyday programming, computing tasks. However, programming such computation units is quite different from traditional programming for CPUs, and special skills are required from the developers. In this paper we present techniques, tooling support for the developers in the first step of re-engineering for parallelism in heterogeneous parallel platforms, namely to assure that the code to be offloaded to an accelerator conforms to its specific requirements by identifying the possible violations in the source code, and also by providing automatic code transformations for their elimination.},
	keywords = {C/C++, Cevelop, code transformation, Eclipse CDT, heterogeneous hardware, REPARA, Static Analysis},
	doi = {10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0180},
	url = {http://ieeexplore.ieee.org/document/7816976/},
	author = {Gyimesi, G{\'a}bor and D{\'e}nes B{\'a}n and Istv{\'a}n Siket and Rudolf Ferenc and Brugnoni, Silvano and Corbat, Thomas and Sommerlad, Peter and Suter, Toni}
}
@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}
}
