PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Improved Method of Searching the Associative Rules while Developing the Software

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
As the delivery of good quality software in time is a very important part of the software development process, it's a very important task to organize this process very accurately. For this, a new method of the searching associative rules were proposed. It is based on the classification of all tasks on three different groups, depending on their difficulty, and after this, searching associative rules among them, which will help to define the time necessary to perform a specific task by the specific developer.
Twórcy
  • Vinnytsia National Technical University, Vinnitsa, Ukraine
  • Vinnytsia National Technical University, Vinnitsa, Ukraine
  • Kherson State University, Kherson, Ukraine
  • Lublin University of Technology, Lublin, Poland
  • East Kazakhstan State Technical University named after D. Serikbayev, Ust-Kamenogorsk, Kazakhstan
  • Institute of Information and Computational Technologies CS MES RK, Almaty, Kazakchstan
Bibliografia
  • [1] K. Wiegers, Software Requirements 3rd Edition, NY, USA: Microsoft Press, 2013.
  • [2] V. K. Batovrin, Explanatory dictionary on system and software engineering, Moscow: Press, pp. 280, 2012.
  • [3] S. Wang, D. Samadhiya, and D. Chen, “Software Development and Quality Problems and Solutions by TRIZ,” in International Symposium on Frontiers in Ambient and Mobile Systems, Ontario, 2011, pp. 730 – 735.
  • [4] M. Halkidi, D. Spinellis, G. Tsatsaronis, and M. Vazirgiannis “Data mining in software engineering,” in Intelligent Data Analysis, Amsterdam, 2011, pp. 413 – 441.
  • [5] S. J. Greenspan, and C. L. McGowan, “Structuring software development for reliability”, in Microelectronics Reliability, vol. 17, no. 1, pp. 1987, pp. 75 – 83.
  • [6] T. Xie, S. Thummalapenta, D. Lo, and C. Liu, “Data Mining for Software Engineering,” in Institutional Knowledge at Singapore Management University, Singapore, 2009, pp. 55 – 62.
  • [7] А. А. Barseagian, М. S. Kuprianov, V.V. Stepanenko, and I. I. Holod., Data mining technologies: Data Mining, Visual Mining, text mining, OLAP, Saint-Petersburg, 2007, pp. 384.
  • [8] I. A. Chubukova, “Data Mining,” in Internet-University of Information Technologies, Binom. Knowledge lab., 2008, pp. 384.
  • [9] D. Hand, H. Mannila, and P. Smyth, “Principles of data mining (adaptive computation and machine learning),” in Cambridge: The MIT Press, 2001, p. 546.
  • [10] A. Berson, and S. Smith., “Data Warehousing, Data Mining and OLAP,” 2007, pp. 612.
  • [11] T. O. Savchuk, and N. Pryimak., “Using the methods of data mining intelligence when testing software,” Intelligent Information Technologies, pp. 87, 2016.
  • [12] M. Sharma, and M. Kumari, “Bug Assignee Prediction Using Association Rule Mining,” Springer, pp. 444 – 457, 2015.
  • [13] C. Maffort, M. Valente, and M. Bigonha, “Mining Architectural Patterns Using Association Rules,” in International Conference on Software Engineering and Knowledge Engineering (SEKE'13), Boston, 2013.
  • [14] D. White, and J. Fortune, “Current practice in project management – an empirical study”, International Journal of Project Management , vol. 20, no. 1, pp. 1 – 11, 2002.
  • [15] M. Azzeh, D. Neagu, and P. Cowling, “Software Stage-Effort Estimation Using Association Rule Mining and Fuzzy Set Theory,” Badford, pp. 154 – 160, 2010.
  • [16] M. Sharma, M. Kumari, and V. Singh, “Bug Assignee Prediction Using Association Rule Mining,” in Department of Computer Science University of Delhi, Delhi, 2015, p. 445.
  • [17] T.A. Zayko, A. A. Oliinyk, and S. A. Subbotin, “Association rules in data mining,” Herald of the National University "KhPI", no. 39 (1012), pp. 82 – 96, 2013.
  • [18] C. Zhang, and S. Zhang, “Association Rule Mining, Models and Algorithms,” pp. 244, 2002.
  • [19] T.O Savchuk, and N. Pryimak., “Modeling of software development process with the markov processes,” Eastern-European journal of Enterprise technologies, pp. 33 – 38, 2017.
  • [20] T.O Savchuk, and N. Pryimak., “Submission of the selection of the method of generation of particular subjects for search of associative rules for the project development of the programmable supply,” Internet, Education, Science, pp. 43 – 44, 2018.
  • [21] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” SIGMOD, vol. 29, pp. 3 – 12, 2000.
  • [22] M. Kacprowicz, “An interval type-2 fuzzy systems in the management of emissions of nitrogen oxides,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska – IAPGOS, vol. 5, no. 1, pp. 20–23, 2015.
  • [23] V. Vassilenko, S. Valtchev, J. P. Teixeira, and S. Pavlov, “Energy harvesting: an interesting topic for education programs in engineering specialities,” Internet, Education, Science, pp. 149-156, 2016.
  • [24] M. Górecka, K. Górecki, “Comparison of selected tools for computer analysis of digital circuits,” Przeglad Elektrotechniczny, vol. 94, no. 4, pp. 72–75, 2015.
  • [25] A.P. Rotshtein, and H.B. Rakytyanska, “Diagnosis problem solving using fuzzy relations,” IEEE Transactions on Fuzzy Systems, vol. 16, pp. 664-675, 2008.
  • [26] L. I. Timchenko, “A multistage parallel-hierarchic network as a model of a neuronlike computation scheme,” Cybernetics and Systems Analysis, vol. 36, pp. 251-267, 2000.
  • [27] L. I. Timchenko, Y. F. Kutaev, V. P. Kozhemyako et al., “Method for training of a parallel-hierarchical network, based on population coding for processing of extended laser paths images,” Proceedings of SPIE 4790, 2002.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-41683fdb-fd8e-4754-a9b7-677b55e32391
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.