@proceedings {16554,
	title = {Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions},
	year = {2019},
	pages = {8-14},
	publisher = {IEEE Press},
	keywords = {select:itsec},
	author = {Rudolf Ferenc and P{\'e}ter Heged{\H u}s and P{\'e}ter Gyimesi and G{\'a}bor Antal and D{\'e}nes B{\'a}n and Tibor Gyimothy}
}
@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 {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 {BaF14,
	title = {Recognizing Antipatterns and Analyzing Their Effects on Software Maintainability},
	booktitle = {Proceedings of the 14th International Conference on Computational Science and Its Applications (ICCSA 2014)},
	series = {Lecture Notes in Computer Science (LNCS)},
	volume = {8583},
	year = {2014},
	month = {jun},
	pages = {337{\textendash}352},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Guimar{\~a}es, Portugal},
	abstract = {Similarly to design patterns and their inherent extra information about the structure and design of a system, antipatterns - or bad code smells - can also greatly influence the quality of software. Although the belief that they negatively impact maintainability is widely accepted, there are still relatively few objective results that would support this theory. In this paper we show our approach of detecting antipatterns in source code by structural analysis and use the results to reveal connections among antipatterns, number of bugs, and maintainability. We studied 228 open-source Java based systems and extracted bug-related information for 34 of them from the PROMISE database. For estimating the maintainability, we used the ColumbusQM probabilistic quality model. We found that there is a statistically significant, 0.55 Spearman correlation between the number of bugs and the number of antipatterns. Moreover, there is an even stronger, -0.62 reverse Spearman correlation between the number of antipatterns and code maintainability. We also found that even these few implemented antipatterns could nearly match the machine learning based bug-predicting power of 50 class level source code metrics. Although the presented analysis is not conclusive by far, these first results suggest that antipatterns really do decrease code quality and can highlight spots that require closer attention.},
	keywords = {Antipatterns, Empirical validation, ISO/IEC 25010, OO design, Software maintainability, SQuaRE},
	doi = {10.1007/978-3-319-09156-3_25},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-09156-3_25},
	author = {D{\'e}nes B{\'a}n and Rudolf Ferenc}
}
@article {16326,
	title = {Myth or Reality? Analyzing the Effect of Design Patterns on Software Maintainability},
	journal = {Communications in Computer and Information Science},
	volume = {340},
	year = {2012},
	pages = {138{\textendash}145},
	issn = {1865-0929},
	author = {P Heged{\H u}s and D{\'e}nes B{\'a}n and Rudolf Ferenc and Tibor Gyim{\'o}thy}
}
