Analysis

Thursday, 30 October – 10:30-11:30

Declaring and Checking Non-null Types in an Object-Oriented Language

Manuel Fähndrich, Microsoft Research, maf@microsoft.com
Rustan Leino, Microsoft Research, leino@microsoft.com

Distinguishing non-null references from possibly null references at the type level can detect null-related errors in object-oriented programs at compile-time. This paper gives a proposal for retrofitting a language such as C# or Java with non-null types. It addresses the central complications that arise in constructors, where declared non-null fields may not yet have been initialized, but the partially constructed object is already accessible. The paper reports experience with an implementation for annotating and checking null-related properties in C# programs.

Object Equality Profiling

Darko Marinov, MIT Laboratory for Computer Science, marinov@lcs.mit.edu
Robert O'Callahan, IBM T.J. Watson Research Center, roca@us.ibm.com

We present Object Equality Profiling (OEP), a new technique for helping programmers discover optimization opportunities in programs. OEP discovers opportunities for replacing a set of equivalent object instances with a single representative object. Such a set represents an opportunity for automatically or manually applying optimizations such as hash consing, heap compression, lazy allocation, object caching, invariant hoisting, and more. To evaluate OEP, we implemented a tool to help programmers reduce the memory usage of Java programs. Our tool performs a dynamic analysis that records all the objects created during a particular program run. The tool partitions the objects into equivalence classes, and uses collected timing information to determine when elements of an equivalence class could have been safely collapsed into a single representative object without affecting the behavior of that program run. We report the results of applying this tool to benchmarks, including two widely used Web application servers. Many benchmarks exhibit significant amounts of object equivalence, and in most benchmarks our profiler identifies optimization opportunities clustered around a small number of allocation sites. We present a case study of using our profiler to find simple manual optimizations that reduce the average space used by live objects in two SpecJVM benchmarks by 47% and 38% respectively.