algorithm for Java programs. The algorithm is based on the abstraction of points-to escape graphs, which characterize how local variables and elds in objects refer to other objects. Each points-to escape graph also contains escape information, which characterizes how objects allocated in one region of the program can escape to be accessed by another region. The algorithm is designed to analyze arbitrary regions of complete or incomplete programs, obtaining complete information for objects that do not escape the analyzed regions.

This paper presents the first scalable context-sensitive, inclusionbased pointer alias analysis for Java programs. Our approach to context sensitivity is to create a clone of a method for every context of interest, and run a context-insensitive algorithm over the expanded call graph to get context-sensitive results. For precision, we generate a clone for every acyclic path through a program’s call graph, treating methods in a strongly connected component as a single node. Normally, this formulation is hopelessly intractable as a call graph often has 10 14 acyclic paths or more. We show that these exponential relations can be computed efficiently using binary decision diagrams (BDDs). Key to the scalability of the technique is a context numbering scheme that exposes the commonalities across contexts. We applied our algorithm to the most popular applications available on Sourceforge, and found that the largest programs, with hundreds of thousands of Java bytecodes, can be analyzed in under 20 minutes. This paper shows that pointer analysis, and many other queries and algorithms, can be described succinctly and declaratively using Datalog, a logic programming language. We have developed a system called bddbddb that automatically translates Datalog programs into highly efficient BDD implementations. We used this approach to develop a variety of context-sensitive algorithms including side effect analysis, type analysis, and escape analysis.

Many maintenance tasks address concerns, or features, that are not well modularized in the source code comprising a system. Existing approaches available to help software developers locate and manage scattered concerns use a representation based on lines of source code, complicating the analysis of the concerns. In this paper, we introduce the Concern Graph representation that abstracts the implementation details of a concern and makes explicit the relationships between different parts of the concern. The abstraction used in a Concern Graph has been designed to allow an obvious and inexpensive mapping back to the corresponding source code. To investigate the practical tradeoffs related to this approach, we have built the Feature Exploration and Analysis tool (FEAT) that allows a developer to manipulate a concern representation extracted from a Java system, and to analyze the relationships of that concern to the code base. We have used this tool to find and describe concerns related to software change tasks. We have performed case studies to evaluate the feasibility, usability, and scalability of the approach. Our results indicate that Concern Graphs can be used to document a concern for change, that developers unfamiliar with Concern Graphs can use them effectively, and that the underlying technology scales to industrial-sized programs.

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This paper addresses the problem of resolving virtual method and interface calls in Java bytecode. The main focus is on a new practical technique that can be used to analyze large applications. Our fundamental design goal was to develop a technique that can be solved with only one iteration, and thus scales linearly with the size of the program, while at the same time providing more accurate results than two popular existing linear techniques, class hierarchy analysis and rapid type analysis. We present two variations of our new technique, variable-type analysis and a coarser-grain version called declared-type analysis. Both of these analyses are inexpensive, easy to implement, and our experimental results show that they scale linearly in the size of the program. We have implemented our new analyses using the Soot framework, and we report on empirical results for seven benchmarks. We have used our techniques to build accurate call graphs for complete applications (including librarie...

Most points-to analysis research has been done on different systems by different groups, making it difficult to compare results, and to understand interactions between individual factors each group studied. Furthermore, points-to analysis for Java has been studied much less thoroughly than for C, and the tradeoffs appear very different. We introduce Spark, a flexible framework for experimenting with points-to analyses for Java. Spark supports equality- and subset-based analyses, variations in field sensitivity, respect for declared types, variations in call graph construction, off-line simplification, and several solving algorithms. Spark is composed of building blocks on which new analyses can be based. We demonstrate Spark in a substantial study of factors affecting precision and efficiency of subsetbased points-to analyses, including interactions between these factors. Our results show that Spark is not only flexible and modular, but also offers superior time/space performance when compared to other points-to analysis implementations.

Interprocedural analyses enable optimizing compilers to more precisely model the effects of non-inlined procedure calls, potentially resulting in substantial increases in application performance. Applying interprocedural 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 construction 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 written 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. 1 Introduction Interprocedural analysis can enable subs...

. This paper presents Soot, a framework for optimizing Java TM bytecode. The framework is implemented in Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of Java's stack-based bytecode; Jimple, a typed three-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple. Our approach to class le optimization is to rst convert the stack-based bytecode into Jimple, a three-address form more amenable to traditional program optimization, and then convert the optimized Jimple back to bytecode. In order to demonstrate that our approach is feasible, we present experimental results showing the eects of processing class les through our framework. In particular, we study the techniques necessary to effectively translate Jimple back to bytecode, without losing performance. Finally, we demonstrate that class le optimization can be quite eective by showing the resul...

This paper presents Soot, a framework for optimizing Java bytecode. The framework is implemented in Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of bytecode which is simple to manipulate; Jimple, a typed 3-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple suitable for decompilation. We describe the motivation for each representation, and the salient points in translating from one representation to another. In order to demonstrate the usefulness of the framework, we have implemented intraprocedural and whole program optimizations. To show that whole program bytecode optimization can give performance improvements, we provide experimental results for 12 large benchmarks, including 8 SPECjvm98 benchmarks running on JDK 1.2 for GNU/Linuxtm . These results show up to 8% improvement when the optimized bytecode is run using the interpreter and up to 21% when run...

The goal of points-to analysis for Java is to determine the set of objects pointed to by a reference variable or a reference object field. This information has a wide variety of client applications in optimizing compilers and software engineering tools. In this paper we present a points-to analysis for Java based on Andersen's points-to analysis for C [5]. We implement the analysis by using a constraint-based approach which employs annotated inclusion constraints. Constraint annotations allow us to model precisely and efficiently the semantics of virtual calls and the flow of values through object fields. By solving systems of annotated inclusion constraints, we have been able to perform practical and precise points-to analysis for Java.