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This artifact, named Prosy, is an interactive command-line tool for synthesizing recursive tree-to-string functions (e.g. pretty-printers) from examples. Specifically, Prosy takes as input a Scala file containing a hierarchy of abstract and case classes, and synthesizes the printing function after interacting with the user. Prosy first pro-actively generates a finite set of trees such that their string representations uniquely determine the function to synthesize. While asking the output for each example, Prosy prunes away questions when it can infer their answers from previous answers. In the companion paper, we prove that this pruning allows Prosy not to require that the user provides answers to the entire set of questions, which is of size O(n^3) where n is the size of the input file, but only to a reasonably small subset of size O(n). Furthermore, Prosy guides the interaction by providing suggestions whenever it can.

Synthesis from examples enables non-expert users to generate programs by specifying examples of their behavior. A domain-specific form of such synthesis has been recently deployed in a widely used spreadsheet software product. In this paper we contribute to foundations of such techniques and present a complete algorithm for synthesis of a class of recursive functions defined by structural recursion over a given algebraic data type definition. The functions we consider map an algebraic data type to a string; they are useful for, e.g., pretty printing and serialization of programs and data. We formalize our problem as learning deterministic sequential top-down tree-to-string transducers with a single state (1STS). The first problem we consider is learning a tree-to-string transducer from any set of input/output examples provided by the user. We show that, given a set of input/output examples, checking whether there exists a 1STS consistent with these examples is NP-complete in general. In contrast, the problem can be solved in polynomial time under a (practically useful) closure condition that each subtree of a tree in the input/output example set is also part of the input/output examples. Because coming up with relevant input/output examples may be difficult for the user while creating hard constraint problems for the synthesizer, we also study a more automated active learning scenario in which the algorithm chooses the inputs for which the user provides the outputs. Our algorithm asks a worst-case linear number of queries as a function of the size of the algebraic data type definition to determine a unique transducer. To construct our algorithms we present two new results on formal languages. First, we define a class of word equations, called sequential word equations, for which we prove that satisfiability can be solved in deterministic polynomial time. This is in contrast to the general word equations for which the best known complexity upper bound is in linear space. Second, we close a long-standing open problem about the asymptotic size of test sets for context-free languages. A test set of a language of words L is a subset T of L such that any two word homomorphisms equivalent on T are also equivalent on L. We prove that it is possible to build test sets of cubic size for context-free languages, matching for the first time the lower bound found 20 years ago.

Efficient implementations of concurrent objects such as semaphores, locks, and atomic collections including stacks and queues are vital to modern computer systems. Programming them is however error prone. To minimize synchronization overhead between concurrent object-method invocations, implementors avoid blocking operations like lock acquisition, allowing methods to execute concurrently. However, concurrency risks unintended inter-operation interference. Their correctness is captured by observational refinement which ensures conformance to atomic reference implementations. Formally, given two libraries L_1 and L_2 implementing the methods of some concurrent object, we say L_1 refines L_2 if and only if every computation of every program using L_1 would also be possible were L_2 used instead. Linearizability, being an equivalent property, is the predominant proof technique for establishing observational refinement: one shows that each concurrent execution has a linearization which is a valid sequential execution according to a specification, given by an abstract data type or atomic reference implementation. However, checking linearizability is intrinsically hard. Indeed, even in the case where method implementations are finite-state and object specifications are also finite-state, and when a fixed number of threads (invoking methods in parallel) is considered, the linearizability problem is EXPSPACE-complete, and it becomes undecidable when the number of threads is unbounded. These results show in particular that there is a complexity/decidability gap between the problem of checking linearizability and the problem of checking reachability (i.e., the dual of checking safety/invariance properties), the latter being, PSPACE-complete and EXPSPACE-complete in the above considered cases, respectively. We address here the issue of investigating cases where tractable reductions of the observational refinement/linearizability problem to the reachability problem, or dually to invariant checking, are possible. Our aim is (1) to develop algorithmic approaches that avoid a systematic exploration of all possible linearizations of all computations, (2) to exploit existing techniques and tools for efficient invariant checking to check observational refinement, and (3) to establish decidability and complexity results for significant classes of concurrent objects and data structures. We present two approaches that we have proposed recently. The first approach introduces a parameterized approximation schema for detecting observational refinement violations. This approach exploits a fundamental property of shared-memory library executions: their histories are interval orders, a special case of partial orders which admit canonical representations in which each operation o is mapped to a positive-integer-bounded interval I(o). Interval orders are equipped with a natural notion of length, which corresponds to the smallest integer constant for which an interval mapping exists. Then, we define a notion of bounded-interval-length analysis, and demonstrate its efficiency, in terms of complexity, coverage, and scalability, for detecting observational refinement bugs. The second approach focuses on a specific class of abstract data types, including common concurrent objects and data structures such as stacks and queues. We show that for this class of objects, the linearizability problem is actually as hard as the control-state reachability problem. Indeed, we prove that in this case, the existence of linearizability violations (i.e., finite computations that are not linearizable), can be captured completely by a finite number of finite-state automata, even when an unbounded number of parallel operations is allowed (assuming that libraries are data-independent).