Abstract

The development process involved developers contribution based his/her cognitive thinking in the real time process. The developer's performance is dynamic as per their cognitive load. The cognitive load is un-deterministic as well hidden and integrated in the developer's process. This paper attempt to identify software developer cognitive measure which influences the development process using neural network back propagation model . It describes the conceptual view on conventional construction of neural network for cognitive measure observation of software development processes. A neural network model designed to present the structure of developer's performance such as Regularity, Task Completion, Accuracy, Team Involvement and Reporting are used to generate the Performance and Cognitive Load of the output layer. To obtain the Performance and the Cognitive Load from the given input, the Cognitive work load such as physical ability, mental ability, temporal ability, effort, frustration and performance are assigned to a hidden layer. The observation and the results are described and discussed as part of the paper.

References

• M. Dawson, (2002). Computer modelling of cognition: Levels of analysis. In:Nadel, L. (ed. ), Encyclopedia of Cognitive Science. pp. 635-638. Macmillan,London, UK.
• Brain Drain: Evaluating the Impact of Increased Cognitive Load During Self-Paced Running Performance, J McCarron, TL Hodgson, MF Smith, Published in British Journal of sports Medicine, 2013.
• Analysis of Developers Cognitive Complexity Association for software development K. Banu , Research Scholar, Mother Teresa Women's University , Kodaikanal, Tamilnadu & Dr. N. Rama, Research Supervisor, Presidency College, Chennai -5
• Comparative Study on Multidimensional Developers Performance with Cognitive Load K. Banu , Research Scholar, Mother Teresa Women's University , Kodaikanal, Tamilnadu & Dr. N. Rama, Research Supervisor, Presidency College, Chennai -5
• Software Developers Performance relationship with Cognitive Load Using Statistical Measures K. Banu , Research Scholar, Mother Teresa Women's University , Kodaikanal, Tamilnadu & Dr. N. Rama, Research Supervisor, Presidency College, Chennai -5
• P. Dayan, (2003). Levels of analysis in neural modeling. In: L. Nadel (ed. ),Encyclopedia of Cognitive Science. Macmillan, London.
• S. Grossberg, (1982). Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control. Norwell, MA: Kluwer Academic Publishers.
• D. Hintzman, (1990). Human learning and memory: Connections and dissociations. In: Annual Review of Psychology, pp. 109-139. Annual Reviews Inc, Palo Alto, CA.
• R. D. Luce ,(1995) . Four tensions concerning mathematical modeling in psychology. Annual Review of Psychology, 46, 1-26. Annual Reviews Inc, PaloAlto, CA.
• D. Massaro, (1988). Some criticisms of connectionist models of human performance. Journal of Memory and Language, 27, 213-234.
• D. Meyer and D. Kieras, (1997). A computational theory of executive cognitive processes and human multiple-task performance: Part 1, basic mechanisms. Psychological Review. 104 (1), 3-65.
• T. Regier, (2003). Constraining computational models of cognition. In: L. Nadel (ed. ), Encyclopedia of Cognitive Science. Macmillan, London. pp. 611-15.
• F. E. Ritter, Shadbolt, N. , Elliman, D. , Young, R. , Gobet, F. , and Baxter,G. , (2003). Techniques for Modeling Human Performance in Synthetic Environments: A Supplementary Review. Human Systems Information Analysis Center, Wright-Patterson Air Force Base, Dayton, OH.
• R. Sun, (2005). Theoretical status of computational cognitive modeling. Technical report, Cognitive Science Department, Rensselaer Polytechnic Institute,Troy, New York.
• R. Sun (ed. ), (2006). Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, New York.
• R. Sun, (2007). The importance of cognitive architectures: An analysis based on CLARION. Journal of Experimental and Theoretical Artificial Intelligence, in press.
• J. Hertz, A. Krogh, R. G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, MA, 1991.
• D. P. Bertsekas, J. N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, Belmont, MA, 1996.
• Neural Networks on the NetBeans Platform , Zoran Severac published by Oracle, Feb 2011.
• Mohsen Hayati, Yazdan Shirvany Artificial Neural Network Approach for Short Term Load Forecasting, International Journal of Electrical, Computer and Systems Engineering Volume 1, Number 2 2007.