The Prediction Of Software Complexity Based On Complexity Requirement Using Artificial Neural Network

被引：0
|
作者：
Purawinata, Wartika Memed [1 ,2 ]
Gaol, Ford Lumban [2 ]
Nugroho, Ariadi [2 ]
Abbas, Bahtiar Saleh [3 ]
机构：
[1] Indonesian Comp Univ, Fac Engn & Comp Sci, Bandung 40132, Indonesia
[2] Bina Nusantara Univ, BINUS Grad Program, Dept Comp Sci, Comp Sci, Jakarta 11480, Indonesia
[3] Bina Nusantara Univ, Fac Engn, Dept Ind Engn, Jakarta 11480, Indonesia
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS AND COMPUTATIONAL INTELLIGENCE (CYBERNETICSCOM) | 2017年
关键词：
prediction; complexity; requirement; software; artificial neural network;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the recent years, the productivity of software has grown in size, complexity, and also cost. As that software productivity growth, several problems has been appeared in software project management especially that correlated to complexity. One of complexity factors is requirement. A unit of requirement used as an option to the design phase of product development. The requirement is also a main option in verification process. So the the requirement complexity in this research is used as parameter to predict the software complexity. Because of the data pattern to connect between the requirement and the complexity is complex. So that this paper attempt to make a connectivity model between requirement complexity and prediction complexity of software using artificial neural network method with Levenberg Marquadt and Bayesian Regulation algorithm. So it can be seen comparison of experimental results by using the two algorithms.
引用
收藏
页码：73 / 78
页数：6
相关论文
共 50 条
  • [31] Experimental and comparison based study on diabetes prediction using artificial neural network
    Pradhan N.
    Dhaka V.S.
    Kulhari S.C.
    Recent Advances in Computer Science and Communications, 2020, 13 (06) : 1173 - 1179
  • [32] Prediction of extrusion pressure using an artificial neural network
    Li, YY
    Bridgwater, J
    POWDER TECHNOLOGY, 2000, 108 (01) : 65 - 73
  • [33] Using artificial neural network for reservoir eutrophication prediction
    Kuo, Jan-Tai
    Hsieh, Ming-Han
    Lung, Wu-Seng
    She, Nian
    ECOLOGICAL MODELLING, 2007, 200 (1-2) : 171 - 177
  • [34] Using artificial neural network models for eutrophication prediction
    Huo, Shouliang
    He, Zhuoshi
    Su, Jing
    Xi, Beidou
    Zhu, Chaowei
    2013 INTERNATIONAL SYMPOSIUM ON ENVIRONMENTAL SCIENCE AND TECHNOLOGY (2013 ISEST), 2013, 18 : 310 - 316
  • [35] Prediction of the plasma distribution using an artificial neural network
    Li Wei
    Chen Jun-Fang
    Wang Teng
    CHINESE PHYSICS B, 2009, 18 (06) : 2441 - 2444
  • [36] Prediction of semen quality using artificial neural network
    Badura, Anna
    Marzec-Wroblewska, Urszula
    Kaminski, Piotr
    Lakota, Pawel
    Ludwikowski, Grzegorz
    Szymanski, Marek
    Wasilow, Karolina
    Lorenc, Andzelika
    Bucinski, Adam
    JOURNAL OF APPLIED BIOMEDICINE, 2019, 17 (03) : 167 - 174
  • [37] The Prediction of Permeability Using an Artificial Neural Network System
    Pazuki, G. R.
    Nikookar, M.
    Dehnavi, M.
    Al-Anazi, B.
    PETROLEUM SCIENCE AND TECHNOLOGY, 2012, 30 (20) : 2108 - 2113
  • [38] PVT Properties Prediction Using Artificial Neural Network
    Rashidi, F.
    Rasouli, I.
    Khamehchi, E.
    PROCEEDINGS OF THE NINTH ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON COMBUSTION AND ENERGY UTILIZATION, 2008, : 78 - 81
  • [39] Prediction of Dissolved Oxygen Using Artificial Neural Network
    Areerachakul, Sirilak
    Junsawang, Prem
    Pomsathit, Auttapon
    COMPUTER COMMUNICATION AND MANAGEMENT, 2011, 5 : 524 - 528
  • [40] Prediction of chitosan modification based on artificial neural network
    Wu, Hao
    Zong, Zhimin
    OPTIK, 2022, 271
12345