Barbara G. Ryder

A framework for reducing the cost of instrumented code

Instrumenting code to collect profiling information can cause substantial execution overhead. This overhead makes instrumentation difficult to perform at runtime, often preventing many known offline feedback-directed optimizations from being used in online systems. This paper presents a general framework for performing instrumentation sampling to reduce the overhead of previously expensive instrumentation. The framework is simple and effective, using code-duplication and counter-based sampling to allow switching between instrumented and non-instrumented code. Our framework does not rely on any hardware or operating system support, yet provides a high frequency sample rate that is tunable, allowing the tradeoff between overhead and accuracy to be adjusted easily at runtime. Experimental results are presented to validate that our technique can collect accurate profiles (93-98% overlap with a perfect profile) with low overhead (averaging 6% total overhead with a naive implementation). A Jalape~ no-specific optimization is also presented that reduces overhead further, resulting in an average total overhead of 3%.

Chianti: a tool for change impact analysis of java programs

This paper reports on the design and implementation of Chianti, a change impact analysis tool for Java that is implemented in the context of the Eclipse environment. Chianti analyzes two versions of an application and decomposes their difference into a set of atomic changes. Change impact is then reported in terms of affected (regression or unit) tests whose execution behavior may have been modified by the applied changes. For each affected test, Chianti also determines a set of affecting changes that were responsible for the tests modified behavior. This latter step of isolating the changes that induce the failure of one specific test from those changes that only affect other tests can be used as a debugging technique in situations where a test fails unexpectedly after a long editing session. We evaluated Chianti on a year (2002) of CVS data from M. Ernsts Daikon system, and found that, on average, 52% of Daikons unit tests are affected. Furthermore, each affected unit test, on average, is affected by only 3.95% of the atomic changes. These findings suggest that our change impact analysis is a promising technique for assisting developers with program understanding and debugging.

Parameterized object sensitivity for points-to analysis for Java

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. We present object sensitivity, a new form of context sensitivity for flow-insensitive points-to analysis for Java. The key idea of our approach is to analyze a method separately for each of the object names that represent run-time objects on which this method may be invoked. To ensure flexibility and practicality, we propose a parameterization framework that allows analysis designers to control the tradeoffs between cost and precision in the object-sensitive analysis.Side-effect analysis determines the memory locations that may be modified by the execution of a program statement. Def-use analysis identifies pairs of statements that set the value of a memory location and subsequently use that value. The information computed by such analyses has a wide variety of uses in compilers and software tools. This work proposes new versions of these analyses that are based on object-sensitive points-to analysis.We have implemented two instantiations of our parameterized object-sensitive points-to analysis. On a set of 23 Java programs, our experiments show that these analyses have comparable cost to a context-insensitive points-to analysis for Java which is based on Andersens analysis for C. Our results also show that object sensitivity significantly improves the precision of side-effect analysis and call graph construction, compared to (1) context-insensitive analysis, and (2) context-sensitive points-to analysis that models context using the invoking call site. These experiments demonstrate that object-sensitive analyses can achieve substantial precision improvement, while at the same time remaining efficient and practical.

A safe approximate algorithm for interprocedural aliasing

During execution, when two or more names exist for the same location at some program point, we call them aliases. In a language which allows arbitrary pointers, the problem of determining aliases at a program point is &rgr;-space-hard [Lan92]. We present an algorithm for the Conditional May Alias problem, which can be used to safely approximate Interprocedural May Alias in the presence of pointers. This algorithm is as precise as possible in the worst case and has been implemented in a prototype analysis tool for C programs. Preliminary speed and precision results are presented.

Change impact analysis for object-oriented programs

Small changes can have major and nonlocal effects in object-oriented languages, due to the use of subtyping and dynamic dispatch. This complicates life for maintenance programmers, who need to fix bugs or add enhancements to systems originally written by others. Change impact analysis provides feedback on the semantic impact of a set of program changes. This analysis can be used to determine the regression test drivers that are affected by a set of changes. Moreover, if a test fails, a subset of changes responsible for the failure can be identified, as well as a subset of changes that can be incorporated safely without affecting any test driver.

Constructing the Call Graph of a Program

The proliferation of large software systems written in high level programming languages insures the utility of analysis programs which examine interprocedural communications. Often these analysis programs need to reduce the dynamic relations between procedures to a static data representation. This paper presents one such representation, a directed, acyclic graph named the call graph of a program. We delineate the programs representable by an acyclic call graph and present an algorithm for constructing it using the property that its nodes may be linearly ordered. We prove the correctness of the algorithm and discuss the results obtained from an implementation of the algorithm in the PFORT Verifier [1].

Relevant context inference

Relevant context inference (RCI) is a modular technique for flow- and context-sensitive data-flow analysis of statically typed object-oriented programming languages such as C++ and Java. RCI can be used to analyze complete programs as well as incomplete programs such as libraries; this approach does not require that the entire program be memory-resident during the analysis. RCI is presented in the context of points-to analysis for a realistic subset of C++. The empirical evidence obtained from a prototype implementation argues the effectiveness of RCI.

Pointer-induced aliasing: a problem classification

A?iasing occurs at some program point during execution when two or more names exist for the same location. We have isolated various programming language mechanisms which create aliases. We have classified the complexity of the fllas problem induced by each mechanism alone and in combination, as AfP-hard, complement tip-hard, or polynomial (’P). We present our problem classification, give an overview of our proof that finding interprocedural aliases in the presence of single level pointers is in 7, and present a represent tive proof for the NP-hard problems.

Points-to analysis for Java using annotated constraints

The goal of point-to analysis for Java is to determine the set of objects pointed 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 point-to analysis for Java based on Andersens point-to analysis for C [5]. We implement the analysis by using a constraint-based approach which employs annotated inclusion constraints. Constraint annotations allow us 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 albe to perform practical and precies points-to analysis for Java

Properties of data flow frameworks

SummaryA comprehensive overview of data flow frameworks and their characterizing properties is presented, to clarify property definitions and demonstrate their interrelation. Properties ensuring the existence of a solution are differentiated from those guaranteeing particular convergence behavior for specific solution procedures. Examples illustrate the orthogonality of these precision and convergence properties. In addition, several data flow problems are categorized with respect to these properties.