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Ondřej Lhoták

Researcher at University of Waterloo

Publications -  68
Citations -  2887

Ondřej Lhoták is an academic researcher from University of Waterloo. The author has contributed to research in topics: Scala & Call graph. The author has an hindex of 19, co-authored 63 publications receiving 2683 citations. Previous affiliations of Ondřej Lhoták include McGill University.

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Proceedings ArticleDOI

Adding trace matching with free variables to AspectJ

TL;DR: A new history-based language feature called tracematches is presented that enables the programmer to trigger the execution of extra code by specifying a regular pattern of events in a computation trace by exploiting the introduction of free variables in the matching patterns.
Book ChapterDOI

Scaling Java points-to analysis using SPARK

TL;DR: SPARK is introduced, a flexible framework for experimenting with points-to analyses for Java that supports equality- and subset-based analyses, variations in field sensitivity, respect for declared types, variationsIn call graph construction, off-line simplification, and several solving algorithms.
Proceedings ArticleDOI

abc: an extensible AspectJ compiler

TL;DR: This paper outlines the design of abc, focusing mostly on how the design supports extensibility, and provides a general overview of how to use abc to implement an extension.
Journal ArticleDOI

In defense of soundiness: a manifesto

TL;DR: Static program analysis is a key component of many software development tools, including compilers, development environments, and verification tools as mentioned in this paper, and it is often expected to be sound in that their result models all possible executions of the program under analysis.
Book ChapterDOI

Context-Sensitive points-to analysis: is it worth it?

TL;DR: The results of an empirical study evaluating the precision of subset-based points-to analysis with several variations of context sensitivity on Java benchmarks of significant size indicate that object-sensitive analysis implementations are likely to scale better and more predictably than the other approaches.