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About 10,000 papers for “Code generation”

Code-Centric Code Generation

Daniel Busch, Steven Smyth, Tim Tegeler et al. · Lecture notes in computer science · 2024

Cited by 1

Code Generation

Hanspeter Mössenböck · Compiler Construction · 2025

Cited by 0

Code Generation

Ekwueme, Benjamin N. · Zenodo (CERN European Organization for Nuclear Research) · 2023

Cited by 0Open access

Code Generation

Stephen L. Campbell, Jean‐Philippe Chancelier, Ramine Nikoukhah · 2009

Cited by 0

Code Generation

Okan Topçu, Halit Oğuztüzün · Simulation foundations, methods and applications · 2017

Cited by 0

Code Generation

Antti Laaksonen · Undergraduate topics in computer science · 2026

Cited by 0

Code generation

Andreas Gampe, Jeffery Ronne, David Niedzielski et al. · 2009

As a safety measure, the Java programming language requires bounds checking of array accesses. This usually translates to dynamic checks each time an array element is accessed. Static analysis can help eliminate some of those checks by proving them to be redundant, reducing the runtime overhead. Compilation of Java programs is usually method-based, and dynamic dispatch complicates interprocedural analysis. The result is a severely restricted static analysis. This paper presents a novel combination of two techniques to alleviate this problem. By assuming constraints that cannot safely be inferred from the program, the amount of provable safe bounds can be greatly extended. These constraints, called speculations, can be derived automatically from the program code by an analyzer, which assumes that there will be no violation of the array bounds. To ensure that the speculations hold at runtime, additional checks have to be injected into the code. Finding good speculations that benefit the runtime performance can be expensive. This paper shows that the speculation technique can be combined with a verifiable annotation framework, allowing most of the work to be shifted to compile-time. Experimental results show that this combination of techniques increases the number of eliminated bounds checks and can result in speedups that approach unconditional bounds check removal.

Cited by 0

Code generation

R. G. Stone, D. J. Cooke · Cambridge University Press eBooks · 1987

In this chapter we address the issues of coding from a PDL into various real imperative programming languages. The PDL stage described in the previous chapter contains a complete (imperative) solution to the original problem so that the coding can now be finished without reference to the original problem. The intention in this chapter is to show that the final code generation can be accomplished using coding templates. Coding templates are shown for a variety of programming languages in common use.

Cited by 0

Cited by 0Open access

QR code generation - Python code

Basu, Nabanita · Figshare · 2024

Medical symptoms and diagnosis recording and sharing.

Cited by 0Open access

Cited by 0

Search 250 million research papers

Code-Centric Code Generation

Code Generation

Code Generation

Code Generation

Code Generation

Code Generation

Code Generation

Code generation

Code generation

Code generation

Code Generation

Code Generation

QR code generation - Python code

QR code generation - Python code

Code Generation Techniques

SPMD Code Generation

Machine-Code Generation

Code Generation Overview

Intermediate-Code Generation

Machine-code generation