This paper presents a program analysis to estimate uncaught exceptions in ML programs. This analysis relies on unification-based type inference in a non-standard type system, using rows to approximate both the flow of escaping exceptions (a la effect systems) and the flow of result values (a la control-flow analyses). The resulting analysis is efficient and precise; in particular, arguments carried by exceptions are accurately handled
. Traditional type inference and type checking algorithms work well with correctly typed programs, b...
We add functional continuations and prompts to a language with an ML-style type system. The operator...
We present a system for extending standard type systems with flow-sensitive type qualifiers. Users a...
This paper presents a program analysis to estimate uncaught exceptions in ML programs. This analysis...
International audienceThis paper presents a program analysis to estimate un-caught exceptions in ML ...
We present a static analysis that detects potential runtime exceptions that are raised and never han...
AbstractWe present a static analysis that detects potential runtime exceptions that are raised and n...
We present in this paper an extension to the ML type system by which it is possible to statically es...
AbstractWe present a static analysis that detects potential runtime exceptions that are raised and n...
We describe our experiences with an exception analysis tool for Standard ML. Information about excep...
This paper presents a program analysis to estimate un aught ex eptions in ML programs. This analysis...
We present a static analysis to automatically generate test data that raise exceptions in the input ...
Most statically typed functional programming languages allow programmers to write partial functions:...
In this paper we present the first exception analysis for a non-strict language. We augment a simply...
ML's exception handling makes it possible to describe exceptional execution flows conveniently,...
. Traditional type inference and type checking algorithms work well with correctly typed programs, b...
We add functional continuations and prompts to a language with an ML-style type system. The operator...
We present a system for extending standard type systems with flow-sensitive type qualifiers. Users a...
This paper presents a program analysis to estimate uncaught exceptions in ML programs. This analysis...
International audienceThis paper presents a program analysis to estimate un-caught exceptions in ML ...
We present a static analysis that detects potential runtime exceptions that are raised and never han...
AbstractWe present a static analysis that detects potential runtime exceptions that are raised and n...
We present in this paper an extension to the ML type system by which it is possible to statically es...
AbstractWe present a static analysis that detects potential runtime exceptions that are raised and n...
We describe our experiences with an exception analysis tool for Standard ML. Information about excep...
This paper presents a program analysis to estimate un aught ex eptions in ML programs. This analysis...
We present a static analysis to automatically generate test data that raise exceptions in the input ...
Most statically typed functional programming languages allow programmers to write partial functions:...
In this paper we present the first exception analysis for a non-strict language. We augment a simply...
ML's exception handling makes it possible to describe exceptional execution flows conveniently,...
. Traditional type inference and type checking algorithms work well with correctly typed programs, b...
We add functional continuations and prompts to a language with an ML-style type system. The operator...
We present a system for extending standard type systems with flow-sensitive type qualifiers. Users a...