@article {ban2018prediction,
	title = {Prediction Models for Performance, Power, and Energy Efficiency of Software Executed on Heterogeneous Hardware},
	journal = {Journal of Supercomputing},
	year = {2018},
	note = {(Accepted for publication.)},
	pages = {25},
	publisher = {springer},
	author = {B{\'a}n, D{\'e}nes and Rudolf Ferenc and Siket, Istv{\'a}n and {\'A}kos Kiss and Tibor Gyim{\'o}thy}
}
@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}
}
@article {USH15,
	title = {Performance Comparison of Query-based Techniques for Anti-pattern Detection},
	journal = {Information and Software Technology},
	volume = {65},
	number = {C},
	year = {2015},
	month = {sep},
	pages = {147{\textendash}165},
	abstract = {Context Program queries play an important role in several software evolution tasks like program comprehension, impact analysis, or the automated identification of anti-patterns for complex refactoring operations. A central artifact of these tasks is the reverse engineered program model built up from the source code (usually an Abstract Semantic Graph, ASG), which is traditionally post-processed by dedicated, hand-coded queries. Objective Our paper investigates the costs and benefits of using the popular industrial Eclipse Modeling Framework (EMF) as an underlying representation of program models processed by four different general-purpose model query techniques based on native Java code, OCL evaluation and (incremental) graph pattern matching. Method We provide in-depth comparison of these techniques on the source code of 28 Java projects using anti-pattern queries taken from refactoring operations in different usage profiles. Results Our results show that general purpose model queries can outperform hand-coded queries by 2{\textendash}3 orders of magnitude, with the trade-off of an increased in memory consumption and model load time of up to an order of magnitude. Conclusion The measurement results of usage profiles can be used as guidelines for selecting the appropriate query technologies in concrete scenarios.},
	keywords = {Anti-patterns, Columbus, EMF-IncQuery, OCL, Performance measurements, refactoring},
	issn = {0950-5849},
	doi = {10.1016/j.infsof.2015.01.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0950584915000051?via\%3Dihub},
	author = {Ujhelyi, Zolt{\'a}n and Sz{\H o}ke, G{\'a}bor and Horv{\'a}th, {\'A}kos and Csisz{\'a}r, Norbert Istv{\'a}n and L{\'a}szl{\'o} Vid{\'a}cs and Varr{\'o}, D{\'a}niel and Rudolf Ferenc}
}
@conference {BFS15,
	title = {Prediction Models for Performance, Power, and Energy Efficiency of Software Executed on Heterogeneous Hardware},
	booktitle = {Proceedings of the 13th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA 2015)},
	year = {2015},
	month = {aug},
	pages = {178{\textendash}183},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Helsinki, Finland},
	abstract = {Heterogeneous environments are becoming commonplace so it is increasingly important to understand how and where we could execute a given algorithm the most efficiently. In this paper we propose a methodology that uses both static source code metrics and dynamic execution time, power and energy measurements to build configuration prediction models. These models are trained on special benchmarks that have both sequential and parallel implementations and can be executed on various computing elements, e.g., on CPUs or GPUs. After they are built, however, they can be applied to a new system using only the system{\textquoteright}s static metrics which are much more easily computable than any dynamic measurement. We found that we could predict the optimal execution configuration fairly accurately using static information alone.},
	keywords = {configuration selection, Green computing, heterogeneous architecture, performance optimization, power-aware execution},
	doi = {10.1109/Trustcom.2015.629},
	url = {http://ieeexplore.ieee.org/document/7345645/},
	author = {D{\'e}nes B{\'a}n and Rudolf Ferenc and Istv{\'a}n Siket and {\'A}kos Kiss}
}
@conference {6080791,
	title = {A probabilistic software quality model},
	booktitle = {Software Maintenance (ICSM), 2011 27th IEEE International Conference on},
	year = {2011},
	month = {sept.},
	pages = {243 -252},
	keywords = {cost estimation, costing, development cost, IEC standards, ISO standards, ISO/IEC 9126 standard, maintainability, probabilistic software quality model, probability, risk estimation, select:quality, software change, software maintenance, software quality, software quality selected},
	issn = {1063-6773},
	doi = {10.1109/ICSM.2011.6080791},
	author = {Tibor Bakota and P Heged{\H u}s and K{\"o}rtv{\'e}lyesi, P and Rudolf Ferenc and Tibor Gyim{\'o}thy}
}
