Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Concrete type inference: delivering object-oriented applications
Concrete type inference: delivering object-oriented applications
Back to the future: the story of Squeak, a practical Smalltalk written in itself
Proceedings of the 12th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Call graph construction in object-oriented languages
Proceedings of the 12th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Revised5 report on the algorithmic language scheme
ACM SIGPLAN Notices
Practical experience with an application extractor for Java
Proceedings of the 14th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Pointer analysis: haven't we solved this problem yet?
PASTE '01 Proceedings of the 2001 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Principles of Program Analysis
Principles of Program Analysis
The Java Language Specification
The Java Language Specification
The Cartesian Product Algorithm: Simple and Precise Type Inference Of Parametric Polymorphism
ECOOP '95 Proceedings of the 9th European Conference on Object-Oriented Programming
Precise Constraint-Based Type Inference for Java
ECOOP '01 Proceedings of the 15th European Conference on Object-Oriented Programming
Semantic Navigation of Large Code Bases in Higher-Order, Dynamically Typed Languages
WCRE '05 Proceedings of the 12th Working Conference on Reverse Engineering
Fast type reconstruction for dynamically typed programming languages
DLS '09 Proceedings of the 5th symposium on Dynamic languages
| Hi-index | 0.00 |
The DDP (Demand-driven/Pruning) analysis algorithm allows us to perform data-flow analyses of programming languages that are dynamically typed and have higher-order control flow, such as Smalltalk or Scheme. Because it is demand-driven and employs search pruning, it scales to large code bases. However, versions of the algorithm previously described [19] do not handle data polymorphism well, conservatively merging separate data flows that go through distinct instantiations of a collection type. In this paper, we describe a new extension to DDP that helps to disentangle these flows, permitting more precise results. The extension is based on source-tagging classes so that each reference to a class in the source code yields a subdivision of the type associated with that class. An initial implementation of this polyvariant analysis has been added to the DDP-based tool Chuck, a part of the integrated Squeak program-development environment; we show examples of the tool in action.