@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}
}
@article {MTMT:3328346,
	title = {Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware},
	journal = {JOURNAL OF SUPERCOMPUTING},
	volume = {75},
	year = {2019},
	pages = {4001-4025},
	abstract = {Heterogeneous computer 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 gain ratio 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, GPUs, or FPGAs. After they are built, however, they can be applied to a new system using only the system{\textquoteright}s static source code metrics which are much more easily computable than any dynamic measurement. We found that while estimating a continuous gain ratio is a much harder problem, we could predict the gain category (e.g., {\textquotedblleft}slight improvement{\textquotedblright} or {\textquotedblleft}large deterioration{\textquotedblright}) of porting to a specific configuration significantly more accurately than a random choice, using static information alone. We also conclude based on our benchmarks that parallelized implementations are less maintainable, thereby supporting the need for automatic transformations.},
	issn = {0920-8542},
	doi = {10.1007/s11227-018-2252-6},
	url = {https://m2.mtmt.hu/api/publication/3328346},
	author = {B{\'a}n, D{\'e}nes and Ferenc, Rudolf and Siket, Istv{\'a}n and Kiss, {\'A}kos and Tibor Gyim{\'o}thy}
}
@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 {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}
}
