Synthesis of Interface Specifications for Java Classes

While a typical software component has a clearly specified (static) interface in terms of the methods and the input/output types they support, information about the correct sequencing of method calls the client must invoke is usually undocumented. In this paper, we propose a novel solution for automatically extracting such temporal specifications for Java classes. Given a Java class, and a safety property such as the exception E should not be raised , the corresponding (dynamic) interface is the most general way of invoking the methods in the class so that the safety property is not violated. Our synthesis method first constructs a symbolic representation of the finite state-transition system obtained from the class using predicate abstraction. Constructing the interface then corresponds to solving a partial-information two-player game on this symbolic graph. We present a sound approach to solve this computationally-hard problem approximately using algorithms for learning finite automata and symbolic model checking for branching-time logics. We describe an implementation of the proposed techniques in the tool JIST- Java Interface Synthesis Tool- and demonstrate that the tool can construct interfaces accurately and efficiently for sample Java2SDK library classes

Bounded Expectations: Resource Analysis for Probabilistic Programs

This paper presents a new static analysis for deriving upper bounds on the expected resource consumption of probabilistic programs. The analysis is fully automatic and derives symbolic bounds that are multivariate polynomials of the inputs. The new technique combines manual state-of-the-art reasoning techniques for probabilistic programs with an effective method for automatic resource-bound analysis of deterministic programs. It can be seen as both, an extension of automatic amortized resource analysis (AARA) to probabilistic programs and an automation of manual reasoning for probabilistic programs that is based on weakest preconditions. As a result, bound inference can be reduced to off-the-shelf LP solving in many cases and automatically-derived bounds can be interactively extended with standard program logics if the automation fails. Building on existing work, the soundness of the analysis is proved with respect to an operational semantics that is based on Markov decision processes. The effectiveness of the technique is demonstrated with a prototype implementation that is used to automatically analyze 39 challenging probabilistic programs and randomized algorithms. Experimental results indicate that the derived constant factors in the bounds are very precise and even optimal for many programs