Intelligent Code Completion Algorithm
An Intelligent Code Completion Algorithm is a Code Completion Algorithm that can be implemented by Intelligent Code Completion System to solve a Intelligent Code Completion Task.
- Context:
- It can range from being a Programming Auto-Completion Algorithm to being a Integrated Development Environment Code Auto-Completion Algorithm.
- Example(s):
- Counter-Example(s):
- a Command-Line Auto-Completion Algorithm,
- a Context-Sensitive Code Completion Algorithm (Asaduzzaman, 2018),
- a Deep TabNine Code Completion Algorithm,
- a Natural Language Auto-Completion Algorithm,
- a Qt Creator Auto-Completion Algorithm,
- a Query Auto-Completion Algorithm,
- a Spelling Error Correction Algorithm,
- a Text Wikification Algorithm,
- a WikiText Auto-Completion Algorithm.
- See: Programming Language, Source Code, Auto-Completion Task, Reflection (Computer Science), Visual Studio, Code Completion, Variable (Programming), Subroutine, Method (Computer Science).
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Intelligent_code_completion Retrieved:2019-10-12.
- Intelligent code completion[1] is a context-aware code completion feature in some programming environments that speeds up the process of coding applications by reducing typos and other common mistakes. Attempts to do this are usually done through auto completion popups when typing, querying parameters of functions, query hints related to syntax errors, etc. Intelligent code completion and related tools serve as documentation and disambiguation for variable names, functions and methods using reflection. [2] Intelligent code completion appears in many program environments, [3] [4] an example implementation being Visual Studio's IntelliSense. The term was originally popularized as "picklist" and some implementations still refer to it as such. [5].(...)
Intelligent code completion works using an automatically generated in-memory database of classes, variable names, and other constructs that the application being edited defines or references. The "classic" implementation of IntelliSense works by detecting marker characters such as periods, or other separator characters, depending on the language. When the user types one of these characters immediately after the name of an entity having one or more accessible members (such as contained variables or functions), IntelliSense suggests matches in a pop-up window. The user can either accept the suggestion by typing a statement completion character
TaborEnter)—or a language-specific marker (such as the semicolon for C++), or continue typing the name. Over time, IntelliSense determines which variable or function the user most likely wants.
- Intelligent code completion[1] is a context-aware code completion feature in some programming environments that speeds up the process of coding applications by reducing typos and other common mistakes. Attempts to do this are usually done through auto completion popups when typing, querying parameters of functions, query hints related to syntax errors, etc. Intelligent code completion and related tools serve as documentation and disambiguation for variable names, functions and methods using reflection. [2] Intelligent code completion appears in many program environments, [3] [4] an example implementation being Visual Studio's IntelliSense. The term was originally popularized as "picklist" and some implementations still refer to it as such. [5].(...)
- ↑ Bruch et al. (2009)
- ↑ Autocomplete | Define Autocomplete at Dictionary.com. Dictionary.reference.com. Retrieved on 2014-04-04.
- ↑ FAQ - CodeBlocks. Wiki.codeblocks.org (2014-02-01). Retrieved on 2014-04-04.
- ↑ Qt Documentation - Completing Code. Retrieved on 2015-07-07.
- ↑ Using Dynamic Apex to retrieve Picklist Values | Developer Force Blog. Blogs.developerforce.com (2008-12-09). Retrieved on 2014-04-04.
2015
- (Proksch et al., 2015) ⇒ Sebastian Proksch, Johannes Lerch, and Mira Mezini. (2015). “Intelligent Code Completion with Bayesian Networks.” In: ACM Transactions on Software Engineering and Methodology (TOSEM) Journal, 25(1). doi:10.1145/2744200
2009
- (Bruch et al., 2009) ⇒ Marcel Bruch, Martin Monperrus, and Mira Mezini. (2009). “Learning from Examples to Improve Code Completion Systems.” In: Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering. ISBN:978-1-60558-001-2 doi:10.1145/1595696.1595728----