SMIE: Weakness is Power!: Auto-indentation with incomplete information
June 04, 2020 Β· Declared Dead Β· π The Art, Science, and Engineering of Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Stefan Monnier
arXiv ID
2006.03103
Category
cs.PL: Programming Languages
Citations
1
Venue
The Art, Science, and Engineering of Programming
Last Checked
4 months ago
Abstract
Automatic indentation of source code is fundamentally a simple matter of parsing the code and then applying language- and style-specific rules about relative indentation of the various constructs. Yet, in practice, full parsing is not always an option, either because of quirks of the language, or because the code is temporarily syntactically incorrect, or because of an incomplete or broken grammar. I present the design of Emacs's Simple-Minded Indentation Engine (SMIE), which gets its power from the weakness of the underlying parsing technique. It makes it possible to perform local parsing, which is hence unaffected by irrelevant surrounding code. This provides a form of graceful degradation in the face of incomplete, erroneous, or just plain problematic information.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted