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Code Presence Using Code Sense


Affiliations
1 Department of Computer Science, Bharathiar University, India
2 School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India
     

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Programmers are tightly occupied with the development workflow. Conscious contemplation of the right code solution and proactive presence of the solution lists is presented in this paper. Refining and sensing the optimum solution lists and the proactive presence of code solutions is incorporated in this code sense algorithm. This architecture allows developers to generate and integrate best code solutions directly in to solution bases. Code Sense Interface can support collaboratively with various platforms which can be plugged in with any development interface. Using Code sense, proactive presence of code is implemented as an assistant named Code Proactive Assistant. Code suggestions for a particular requirement scenario are considered as the primary goal. In addition to showing the feasibility of this approach, it provides further evidence in support of the claim that integrating specialized code sense interfaces directly into the editor is valuable to professional developers.

Keywords

Code Sense, Code Proactive Assistant, Collaborative Learning.
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  • R. Robbes and M. Lanza, “How Program History Can Improve Code Completion”, Proceedings of 23rd IEEE/ACM International Conference on Automated Software Engineering, pp. 317-326, 2008.
  • D. Hou and D. Pletcher, “An Evaluation of the Strategies of Sorting, Filtering, and Grouping API Methods for Code Completion”, Proceedings of IEEE International Conference on Software Maintenance, pp. 233-242, 2011.
  • H.M. Lee, M. Antkiewicz and K. Czarnecki, “Towards a Generic Infrastructure for Framework-Specific Integrated Development Environment Extensions”, Proceedings of International Workshop on Domain-Specific Program Development, pp. 1-12, 2008.
  • S. Han, D.R. Wallace and R.C. Miller, “Code Completion from Abbreviated Input”, Proceedings of IEEE/ACM International Conference on Automated Software Engineering, pp. 332-343, 2009.
  • M. Bruch, M. Monperrus and M. Mezini, “Learning from Examples to Improve Code Completion Systems”, Proceedings of 7th European Conference on Software Engineering, pp. 213-222, 2009.
  • J. Brandt, M. Dontcheva, M. Weskamp and S.R. Klemmer, “Example-Centric Programming: Integrating Web Search into the Development Environment”, Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 513-522, 2010.
  • M. Mooty, A. Faulring, J. Stylos and B. Myers, “Calcite: Completing Code Completion for Constructors using Crowds”, Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 15-22, 2010.
  • Snipmatch, Available at: http://languageinterfaces. com/, Accessed at 2017.
  • B. Ellis, J. Stylos and B. Myers, “The Factory Pattern in API Design: A Usability Evaluation”, Proceedings of International Conference on Software Engineering, pp. 302-312, 2007.
  • M.A. Krishna Priya, “Trajectory Schema Service Frame Work for Software Development Organizations”, International Journal of Engineering and Technology, Vol. 7, no. 3, pp. 616-620, 2018.
  • M.A. Krishna Priya and Justus Selwyn, “Code Knowledge Acquisition for Knowledge Management Trajectory Framework”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, pp. 1-12, 2019.
  • M.A. Krishna Priya and Justus Selwyn, “Synthetization of Solution Knowledge Base”, International Journal of Analytical and Experimental Modal Analysis, Vol. 11, No. 10, pp. 1-9, 2019.
  • R. Software, “Abstract Syntax Tree (AST)”, Available at https://support.roguewave.com/documentation/klocwork/en/10-x/ abstractsyntaxtreeast/, Accessed at 2018.
  • J. Dean and S. Ghemawat, “Mapreduce: Simplified Data Processing on Large Clusters”, Communications of the ACM, Vol. 51, No. 1, pp. 107-113, 2008.
  • Developing Plugins, Available at https://docs.gradle.org, Accessed at 2018.
  • Jquery, “Jquery: The Write Less, Do More, Javascript Library”, Available at http://jquery.com/, Accessed at 2020.
  • Multi Integrated Development Environment, Available at https://www.ghs.com/products/MULTI_IDE.html, Accessed at 2020.
  • P. Miller, J. Pane, G. Meter and S. Vorthmann, “Evolution of Novice Programming Environments: The Structure Editors of Carnegie Mellon University”, Interactive Learning Environments, Vol. 4, No. 2, pp. 140-158, 1994.
  • S. Davis and G. Kiczales, “Registration-Based Language Abstractions”, Proceedings of ACM International Conference on Object Oriented Programming Systems Languages and Applications, pp. 754-773, 2010.
  • Jet Brains, “How to Check Your Regexps in Intellij Idea 11?”, Available at http://blogs.jetbrains.com/idea/tag/regexp/, Accessed at 2019.
  • Microsoft Magazine, “Custom Design-Time Control Features in Visual Studio.net”, Available at http://msdn.microsoft.com/en-us/ magazine/cc164048.aspx, Accessed at 2018.

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  • Code Presence Using Code Sense

Abstract Views: 185  |  PDF Views: 1

Authors

M. A. Krishna Priya
Department of Computer Science, Bharathiar University, India
Justus Selwyn
School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India

Abstract


Programmers are tightly occupied with the development workflow. Conscious contemplation of the right code solution and proactive presence of the solution lists is presented in this paper. Refining and sensing the optimum solution lists and the proactive presence of code solutions is incorporated in this code sense algorithm. This architecture allows developers to generate and integrate best code solutions directly in to solution bases. Code Sense Interface can support collaboratively with various platforms which can be plugged in with any development interface. Using Code sense, proactive presence of code is implemented as an assistant named Code Proactive Assistant. Code suggestions for a particular requirement scenario are considered as the primary goal. In addition to showing the feasibility of this approach, it provides further evidence in support of the claim that integrating specialized code sense interfaces directly into the editor is valuable to professional developers.

Keywords


Code Sense, Code Proactive Assistant, Collaborative Learning.

References