000			02192cam a2200349 a 4500
003			EG-GiCUC
005			20250223032835.0
008			211024s2021 ua f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.13.06.M.Sc.2021.Em.N
100	0		_aEman Hosam Adel Elsayed
245	1	0	_aNovel classification feature sets for source code plagiarism detection of java files / _cEman Hosam Adel Elsayed ; Supervised Magda B. Fayek , Amir F. Sorial , Mayada M. Ali
246	1	5	_aمجموعات مبتكرة من الخصائص لتصنيف و اكتشاف السرقة الأدبية لبرامج الجافا
260			_aCairo : _bEman Hosam Adel Elsayed , _c2021
300			_a85 P. ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering
520			_aIn programming learning environments, the pressure of delivering many assignments makes plagiarism become the easiest solution. This problem of plagiarism threatens the learning process and obstructs the evaluation fairness. Therefore, fast, automatic and accurate detection of source code plagiarism becomes of the essence.This research proposes novel classification feature sets to detect whether a Java file is plagiarized.The proposed feature sets are based on using histograms to summarize the similarity matrix of function signatures and comparing the lexical code similarity of each individual class pair. For testing the effectiveness, a source code plagiarism dataset that consists of 12K Java files was used. The results show a 4% improvement in F-Measure. A re-annotation to the dataset is performed and improves F-Measure by 7.5%.
530			_aIssued also as CD
653		4	_aSoftware Similarity Detection
653		4	_aSource Code Plagiarism Detection
653		4	_aSource Code Reuse Detection
700	0		_aAmir F. Sorial , _eSupervisor
700	0		_aMagda B. Fayek , _eSupervisor
700	0		_aMayada M. Ali , _eSupervisor
856			_uhttp://172.23.153.220/th.pdf
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c82777 _d82777