In computer science, a symbol table is a data structure used by a language translator such as a compiler or interpreter, where each identifier (or symbol), constant, procedure and function in a program's source code is associated with information relating to its declaration or appearance in the source. In other words, the entries of a symbol table store the information related to the entry's corresponding symbol.
A symbol table may only exist in memory during the translation process, or it may be embedded in the output of the translation, such as in an ABI for later use. For example, it might be used during an interactive debugging session, or as a resource for formatting a diagnostic report during or after execution of a program.
The minimum information contained in a symbol table used by a translator and intermediate representation (IR) includes the symbol's name and its location or address. For a compiler targeting a platform with a concept of relocatability, it will also contain relocatability attributes (absolute, relocatable, etc.) and needed relocation information for relocatable symbols. Symbol tables for high-level programming languages may store the symbol's type: string, integer, floating-point, etc., its size, and its dimensions and its bounds. Not all of this information is included in the output file, but may be provided for use in debugging. In many cases, the symbol's cross-reference information is stored with or linked to the symbol table. Most compilers print some or all of this information in symbol table and cross-reference listings at the end of translation.
Numerous data structures are available for implementing tables. Trees, linear lists and self-organizing lists can all be used to implement a symbol table. The symbol table is accessed by most phases of a compiler, beginning with lexical analysis, and continuing through optimization.
A compiler may use one large symbol table for all symbols or use separated, or hierarchical symbol tables for different scopes.
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In computer science, the syntax of a computer language is the rules that define the combinations of symbols that are considered to be correctly structured statements or expressions in that language. This applies both to programming languages, where the document represents source code, and to markup languages, where the document represents data. The syntax of a language defines its surface form. Text-based computer languages are based on sequences of characters, while visual programming languages are based on the spatial layout and connections between symbols (which may be textual or graphical).
An intermediate representation (IR) is the data structure or code used internally by a compiler or virtual machine to represent source code. An IR is designed to be conducive to further processing, such as optimization and translation. A "good" IR must be accurate – capable of representing the source code without loss of information – and independent of any particular source or target language. An IR may take one of several forms: an in-memory data structure, or a special tuple- or stack-based code readable by the program.
A debug symbol is a special kind of symbol that attaches additional information to the symbol table of an , such as a shared library or an executable. This information allows a symbolic debugger to gain access to information from the source code of the binary, such as the names of identifiers, including variables and routines. The symbolic information may be compiled together with the module's , or distributed in a separate file, or simply discarded during the compilation and/or linking.
Text, sound, and images are examples of information sources stored in our computers and/or communicated over the Internet. How do we measure, compress, and protect the informatin they contain?
Formally verifying the correctness of software network functions (NFs) is necessary for network reliability, yet existing techniques require full source code and mandate the use of specific data structures. We describe an automated technique to verify NF b ...
USENIX Association2022
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In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem. Our proposed solution is inspired from the compressed sensing paradigm and the sparse superposition (SS) codes. First, we introduce sparsity in the binary so ...
IEEE2018
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Encoding of a plurality of encoded symbols is provided wherein an encoded symbol is generated from a combination of a first symbol generated from a first set of intermediate symbols and a second symbol generated from a second set of intermediate symbols, e ...