One of the primary challenges in building and evolving large object-oriented systems is understanding aliasing between objects. Unexpected aliasing can lead to broken invariants, mistaken assumptions, security holes, and surprising side effects, all of which may lead to software defects and complicate software evolution.This paper presents AliasJava, a capability-based alias annotation system for Java that makes alias patterns explicit in the source code, enabling developers to reason more effectively about the interactions in a complex system. We describe our implementation, prove the soundness of the annotation system, and give an algorithm for automatically inferring alias annotations. Our experience suggests that the annotation system is practical, that annotation inference is efficient and yields appropriate annotations, and that the annotations can express important invariants of data structures and of software architectures.
Interprocedural analyses enable optimizing compilers to more precisely model the effects of non-inlined procedure calls, pot+ially resulting in substantial increases in application performance. Applying interprocedura! analysis to programs written in object-oriented or functional languages is complicated by the difficulty of constructing an accurate program call graph. This paper presents a parameterized algorithmic framework for call graph cohstruction in the presence'of message sends and/or firstclass functions. We use this framework to describe and to implement a number of well-known and new algorithms. We then empirically assess these algorithms by applying them to a suite of medium-sized programs writtgn in Cecil and Java, reporting on the relative cost of the analyses, the relative precision of the constructed call graphs, and the impact of this precision on the effectiveness of a number of interprocedural optimizations.
SELF's debugging system provides complete source-level debugging ( expected behavior ) with globally optimized code. It shields the debugger from optimizations performed by the compiler by dynamically deoptimizing code on demand. Deoptimization only affects the procedure activations that are actively being debugged; all other code runs at full speed. Deoptimization requires the compiler to supply debugging information at discrete interrupt points ; the compiler can still perform extensive optimizations between interrupt points without affecting debuggability. At the same time, the inability to interrupt between interrupt points is invisible to the user. Our debugging system also handles programming changes during debugging. Again, the system provides expected behavior: it is possible to change a running program and immediately observe the effects of the change. Dynamic deoptimization transforms old compiled code (which may contain inlined copies of the old version of the changed procedure) into new versions reflecting the current source-level state. To the best of our knowledge, SELF is the first practical system providing full expected behavior with globally optimized code.
Dataflow analyses can have mutually beneficial interactions. Previous efforts to exploit these interactions have either (1) iteratively performed each individual analysis until no further improvements are discovered or (2) developed "superanalyses" that manually combine conceptually separate analyses. We have devised a new approach that allows analyses to be defined independently while still enabling them to be combined automatically and profitably. Our approach avoids the loss of precision associated with iterating individual analyses and the implementation difficulties of manually writing a super-analysis. The key to our approach is a novel method of implicit communication between the individual components of a super-analysis based on graph transformations. In this paper, we precisely define our approach; we demonstrate that it is sound and it terminates; finally we give experimental results showing that in practice (1) our framework produces results at least as precise as iterating the individual analyses while compiling at least 5 times faster, and (2) our framework achieves the same precision as a manually written super-analysis while incurring a compiletime overhead of less than 20%.
The use of dynamically-dispatched procedure calls is a key mechanism for writing extensible and flexible code in object-oriented languages. Unfortunately, dynamic dispatching imposes a runtime performance penalty. Some recent implementations of pure object-oriented languages have utilized profile-guided receiver class prediction to reduce this performance penalty, and some researchers have argued for applying receiver class prediction in hybrid languages like C++. We performed a detailed examination of the dynamic profiles of eight large object-oriented applications written in C++ and Cecil, determining that the receiver class distributions are strongly peaked and stable across both inputs and program versions through time. We describe techniques for gathering and manipulating profile information at varying degrees of precision, particularly in the presence of optimizations such as inlining. Our implementation of profile-guided receiver class prediction improves the performance of large Cecil applications by more than a factor of two over solely static optimizations.
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