This paper evaluates three alias analyses based on programming language types. The first analysis uses type compatibility to determine aliases. The second extends the first by using additional high-level information such as field names. The third extends the second with a flow-insensitive analysis. Although other researchers suggests using types to disambiguate memory references, none evaluates its effectiveness. We perform both static and dynamic evaluations of type-based alias analyses for Modula-3, a statically-typed type-safe language. The static analysis reveals that type compatibility alone yields a very imprecise alias analysis, but the other two analyses significantly improve alias precision. We use redundant load elimination (RLE) to demonstrate the effectiveness of the three alias algorithms in terms of the opportunities for optimization, the impact on simulated execution times, and to compute an upper bound on what a perfect alias analysis would yield. We show modest dynamic...

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Persistent systems manage main memory as a cache for efficient access to frequently-accessed persistent data. Good cache management requires some knowledge of the semantics of the applications running against it. We are attacking the performance problems of persistence for Java through analysis, profiling, and optimisation of Java classes and methods executing in an orthogonally persistent setting. Knowledge of application behaviour is derived through analysis and profiling, and applied by both a static bytecode transformer and the run-time system to optimise the actions of Java programs as they execute against persistent storage. Our prototype will unify distinct persistence optimisations within a single optimisation framework, deriving its power from treatment of the entire persistent application, consisting of both program code and data stored in the database, for wholeapplication analysis, profiling and optimisation. Keywords: persistence, Java, bytecode, program analysis, dynami...

Constraint-based type inference infers types with subtyping constraints. Such types can capture detailed data and control flow information about the analyzed program. In the presence of polymorphism, existing constraint-based type inference algorithms sacrifice much precision for efficiency. This paper presents both theoretical and practical results on developing precise and efficient polymorphic constraint-based type inference for object-oriented languages. We develop a novel theoretical framework for polymorphic constraint-based type inference. A concrete type inference algorithm can be obtained by instantiating the framework with a particular strategy for handling polymorphism. We define well-known algorithms such as Shivers’ nCFA and Agesen’s Cartesian Product Algorithm (CPA) as instantiations of the framework. We prove the soundness of the framework, which entails the soundness of every algorithm defined as an instantiation of the framework. Using the framework, we develop a novel algorithm, Data Polymorphic CPA (DCPA), which extends Agesen’s CPA Algorithm to achieve high precision in the presence of Data Polymorphism. We further study the construction of practical constraint-based type inference systems for realistic programming languages. We have constructed a type inference system for the full Java language. The system includes implementations of the 0CFA, CPA and DCPA algorithms, and it incorporates a number of novel implementation techniques for achieving scalability. The system is used to statically verify the correctness of Java downcasts. Benchmark results on realistic Java applications show that the DCPA algorithm has good precision and efficiency: it is significantly more accurate than existing algorithms and its efficiency is comparable to CPA.

This paper evaluates three alias analyses based on programming language types. The first analysis uses type compatibility to determine aliases. The second extends the first by using additional high-level information such as field names. The third extends the second with a flow-insensitive analysis. Although other researchers suggests using types to disambiguate memory references, none evaluates its effectiveness. We perform both static and dynamic evaluations of type-based alias analyses for Modula-3, a statically-typed type-safe language. The static analysis reveals that type compatibility alone yields a very imprecise alias analysis, but the other two analyses significantly improve alias precision. We use redundant load elimination (RLE) to demonstrate the effectiveness of the three alias algorithms in terms of the opportunities for optimization, the impact on simulated execution times, and to compute an upper bound on what a perfect alias analysis would yield. We show modest dynamic...