Name
Abstract | ||
|---|---|---|
While many programmers appreciate the benefits of lazy programming at an abstract level, determining which parts of a concrete program to evaluate lazily poses a significant challenge for most of them. Over the past thirty years, experts have published numerous papers on the problem, but developing this level of expertise requires a significant amount of experience. We present a profiling-based technique that captures and automates this expertise for the insertion of laziness annotations into strict programs. To make this idea precise, we show how to equip a formal semantics with a metric that measures waste in an evaluation. Then we explain how to implement this metric as a dynamic profiling tool that suggests where to insert laziness into a program. Finally, we present evidence that our profiler's suggestions either match or improve on an expert's use of laziness in a range of real-world applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2535838.2535887 | POPL |
Keywords | Field | DocType |
present evidence,concrete program,lazy programming,significant amount,dynamic profiling tool,formal semantics,strict program,laziness annotation,abstract level,significant challenge,profiling,code refactoring | Programming language,Computer science,Profiling (computer programming),Laziness,Code refactoring,Semantics of logic | Conference |
Volume | Issue | ISSN |
49 | 1 | 0362-1340 |
Citations | PageRank | References |
1 | 0.36 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Chang | 1 | 24 | 4.07 |
| Matthias Felleisen | 2 | 3001 | 272.57 |