By abstracting away the complexity of distributed systems, large-scale data processing platforms—MapReduce, Hadoop, Spark, Dryad, etc.—have provided developers with simple means for harnessing the power of the cloud. In this paper, we ask whether we can automatically synthesize MapReduce-style distributed programs from input–output examples. Our ultimate goal is to enable end users to specify large-scale data analyses through the simple interface of examples. We thus present a new algorithm and tool for synthesizing programs composed of efficient data-parallel operations that can execute on cloud computing infrastructure. We evaluate our tool on a range of real-world big-data analysis tasks and general computations. Our results demonstrate the efficiency of our approach and the small number of examples it requires to synthesize correct, scalable programs.
Many problems in program analysis, verification, and synthesis require inferring specifications of unknown procedures. Motivated by a broad range of applications, we formulate the problem of maximal specification inference: Given a postcondition Phi and a program P calling a set of unknown procedures F_1,…,F_n, what are the most permissive specifications of procedures F_i that ensure correctness of P? In other words, we are looking for the smallest number of assumptions we need to make about the behaviours of F_i in order to prove that $P$ satisfies its postcondition. To solve this problem, we present a novel approach that utilizes a counterexample-guided inductive synthesis loop and reduces the maximal specification inference problem to multi-abduction. We formulate the novel notion of multi-abduction as a generalization of classical logical abduction and present an algorithm for solving multi-abduction problems. On the practical side, we evaluate our specification inference technique on a range of benchmarks and demonstrate its ability to synthesize specifications of kernel routines invoked by device drivers.
The rise in efficiency of Satisfiability Modulo Theories (SMT) solvers has created numerous uses for them in software verification, program synthesis, functional programming, refinement types, etc. In all of these applications, SMT solvers are used for generating satisfying assignments (e.g., a witness for a bug) or proving unsatisfiability/validity(e.g., proving that a subtyping relation holds). We are often interested in finding not just an arbitrary satisfying assignment, but one that optimizes (minimizes/maximizes) certain criteria. For example, we might be interested in detecting program executions that maximize energy usage (performance bugs), or synthesizing short programs that do not make expensive API calls. Unfortunately, none of the available SMT solvers offer such optimization capabilities. In this paper, we present SYMBA, an efficient SMT-based optimization algorithm for objective functions in the theory of linear real arithmetic (LRA). Given a formula φ and an objective function t , SYMBA finds a satisfying assignment of φthat maximizes the value of t . SYMBA utilizes efficient SMT solvers as black boxes. As a result, it is easy to implement and it directly benefits from future advances in SMT solvers. Moreover, SYMBA can optimize a set of objective functions, reusing information between them to speed up the analysis. We have implemented SYMBA and evaluated it on a large number of optimization benchmarks drawn from program analysis tasks. Our results indicate the power and efficiency of SYMBA in comparison with competing approaches, and highlight the importance of its multi-objective-function feature.
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