Tom Rothamel

Parametric regular path queries

Regular path queries are a way of declaratively expressing queries on graphs as regular-expression-like patterns that are matched against paths in the graph. There are two kinds of queries: existential queries, which specify properties about individual paths, and universal queries, which specify properties about all paths. They provide a simple and convenient framework for expressing program analyses as queries on graph representations of programs, for expressing verification (model-checking) problems as queries on transition systems, for querying semi-structured data, etc. Parametric regular path queries extend the patterns with variables, called parameters, which significantly increase the expressiveness by allowing additional information along single or multiple paths to be captured and relate.This paper shows how a variety of program analysis and model-checking problems can be expressed easily and succinctly using parametric regular path queries. The paper describes the specification, design, analysis, and implementation of algorithms and data structures for efficiently solving existential and universal parametric regular path queries. Major contributions include the first complete algorithms and data structures for directly and efficiently solving existential and universal parametric regular path queries, detailed complexity analysis of the algorithms, detailed analytical and experimental performance comparison of variations of the algorithms and data structures, and investigation of efficiency tradeoffs between different formulations of queries.

Alias analysis for optimization of dynamic languages

Dynamic languages such as Python allow programs to be written more easily using high-level constructs such as comprehensions for queries and using generic code. Efficient execution of programs then requires powerful optimizations - incrementalization of expensive queries and specialization of generic code. Effective incrementalization and specialization of dynamic languages require precise and scalable alias analysis. This paper describes the development and experimental evaluation of a may-alias analysis for a full dynamic object-oriented language, for program optimization by incrementalization and specialization. The analysis is flow-sensitive; we show that this is necessary for effective optimization of dynamic languages. It uses precise type analysis and a powerful form of context sensitivity, called trace sensitivity, to further improve analysis precision. It uses a compressed representation to significantly reduce the memory used by flow-sensitive analyses.We evaluate the effectiveness of this analysis and 17 variants of it for incrementalization and specialization of Python programs, and we evaluate the precision, memory usage, and running time of these analyses on programs of diverse sizes. The results show that our analysis has acceptable precision and efficiency and represents the best trade-off between them compared to the variants.

Generating optimized code from SCR specifications

A promising trend in software development is the increasing adoption of model-driven design. In this approach, a developer first constructs an abstract model of the required program behavior in a language, such as Statecharts or Stateflow, and then uses a code generator to automatically transform the model into an executable program. This approach has many advantages---typically, a model is not only more concise than code and hence more understandable, it is also more amenable to mechanized analysis. Moreover, automatic generation of code from a model usually produces code with fewer errors than hand-crafted code.One serious problem, however, is that a code generator may produce inefficient code. To address this problem, this paper describes a method for generating efficient code from SCR (Software Cost Reduction) specifications. While the SCR tabular notation and tools have been used successfully to specify, simulate, and verify numerous embedded systems, until now SCR has lacked an automated method for generating optimized code. This paper describes an efficient method for automatic code generation from SCR specifications, together with an implementation and an experimental evaluation. The method first synthesizes an execution-flow graph from the specification, then applies three optimizations to the graph, namely, input slicing, simplification, and output slicing, and then automatically generates code from the optimized graph. Experiments on seven benchmarks demonstrate that the method produces significant performance improvements in code generated from large specifications. Moreover, code generation is relatively fast, and the code produced is relatively compact.

Generating incremental implementations of object-set queries

High-level query constructs help greatly improve the clarity of programs and the productivity of programmers, and are being introduced to increasingly more languages. However, the use of high-level queries in programming languages can come at a cost to program efficiency, because these queries are expensive and may be computed repeatedly on slightly changed inputs. For efficient computation in practical applications, a powerful method is needed to incrementally maintain query results with respect to updates to query parameters. This paper describes a general and powerful method for automatically generating incremental implementations of high-level queries over objects and sets in object-oriented programs, where a query may contain arbitrary set enumerators, field selectors, and additional conditions. The method can handle any update to object fields and addition and removal of set elements, and generate coordinated maintenance code and invocation mechanisms to ensure that query results are computed correctly and efficiently. Our implementation and experimental results for example queries and updates confirm the effectiveness of the method.

Core role-based access control: efficient implementations by transformations

This paper describes a transformational method applied to the core component of role-based access control (RBAC), to derive efficient implementations from a specification based on the ANSI standard for RBAC. The method is based on the idea of incrementally maintaining the result of expensive set operations, where a new method is described and used for systematically deriving incrementalization rules. We calculate precise complexities for three variants of efficient implementations as well as for a straightforward implementation based on the specification. We describe successful prototypes and experiments for the efficient implementations and for automatically generating efficient implementations from straightforward implementations.

Efficient runtime invariant checking: a framework and case study

This paper describes a general and powerful framework for efficient runtime invariant checking. The framework supports (1) declarative specification of arbitrary invariants using high-level queries, with easy use of information from any data in the execution, (2) powerful analysis and transformations for automatic generation of instrumentation for efficient incremental checking of invariants, and (3) convenient mechanisms for reporting errors, debugging, and taking preventive or remedial actions, as well as recording history data for use in queries. We demonstrate the advantages and effectiveness of the framework through implementations and case studies with abstract syntax tree transformations, authentication in a SMB client, and the BitTorrent peer-to-peer file distribution protocol.

Efficient implementation of tuple pattern based retrieval

Tuple pattern based retrieval is a language construct that matches a tuple pattern against a set of tuples to retrieve components of those tuples. This high-level abstraction allows programs to be written more easily and clearly than otherwise. This paper describes a clean and automatic method for transforming tuple pattern based retrievals into efficient implementations. The paper also presents two systems that implement the method, and describes successful experience and experiments in generating efficient implementations for graph algorithms, program analysis, security, and other applications.

Composing transformations for instrumentation and optimization

When transforming programs for complex instrumentation and optimization, it is essential to understand the effect of the transformations, to best optimize the transformed programs, and to speedup the transformation process. This paper describes a powerful method for composing transformation rules to achieve these goals. We specify the transformations declaratively as instrumentation rules and invariant rules, the latter for transforming complex queries in instrumentation and in programs into efficient incremental computations. Our method automatically composes the transformation rules and optimizes the composed rules before applying the optimized composed rules. The method allows (1) the effect of transformations to be accumulated in composed rules and thus easy to see, (2) the replacements in composed rules to be optimized without the difficulty of achieving the optimization on large transformed programs, and (3) the transformation process to be sped up by applying a composed rule inone pass of program analyses and transformations instead of applying the original rules in multiple passes. We have implemented the method for Python. We successfully used it for instrumentation, in ranking peers in BitTorrent; and for optimization of complex queries, in the instrumentation of BitTorrent, in evaluating connections of network hosts using NetFlow, and in generating efficient implementations of Constrained RBAC.

Analysis and Transformations for Efficient Query-Based Debugging

This paper describes a framework that supports powerful queries in debugging tools, and describes in particular the transformations, alias analysis, and type analysis used to make the queries efficient. The framework allows queries over the states of all objects at any point in the execution as well as over the history of states. The transformations are based on incrementally maintaining the results of expensive queries studied in previous work. The alias analysis extends the flow-sensitive intraprocedural analysis to an efficient flow-sensitive interprocedural analysis for an object-oriented language with also a form of context sensitivity. We also show the power of the framework and the effectiveness of the analyses through case studies and experiments with XML DOM tree transformations, an FTP client, and others. We were able to easily determine the sources of all injected bugs, and we also found an actual bug in the case study on the FTP client.

Implementing incrementalization across object abstraction

We have implemented an interpreter (InvTS) for a declarative rule language (InvTL) supporting invariant-driven transformations of object-oriented programs. Using a library of rules, it can perform incrementalization across object abstractions, allowing the programmer to write clear, straightforward code while relying on InvTS to generate sophisticated and efficient implementations.