Abstract. One problem with the constraint-based approaches to synthesis that have become popular over the last few years is that they only scale to relatively small routines, on the order of a few dozen lines of code. This paper presents a mechanism for modular reasoning that allows us to break larger synthesis problems into small manageable pieces. The approach builds on previous work in the verification community of using high-level specifications and partially interpreted functions (we call them models) in place of more complex pieces of code in order to make the analysis modular. The main contribution of this paper is to show how to combine these techniques with the counterexample guided synthesis approaches used to efficiently solve synthesis problems. Specifically, we show two new algorithms; one to efficiently synthesize functions that use models, and another one to synthesize functions while ensuring that the behavior of the resulting function will be in the set of behaviors allowed by the model. We have implemented our approach on top of the open-source Sketch synthesis system, and we demonstrate its effectiveness on several Sketch benchmark problems.

Real-time collaboration between a requester and crowd workers expands the scope of tasks that crowdsourcing can be used for by letting requesters and crowd workers interactively create various artifacts (e.g., a sketch prototype, writing, or program code). In such systems, it is increasingly common to allow requesters to verbally describe their requests, receive responses from workers, and provide immediate and continuous feedback to enhance the overall outcome of the two groups' real-time collaboration. This work is motivated by the lack of a deep understanding of the challenges that end users of such systems face in their communication with workers and the need of design implications that can address such challenges for other similar systems. In this paper, we investigate how requesters verbally communicate and collaborate with crowd workers to solve a complex task. Using a crowd-powered UI design tool, we conducted a qualitative user study to explore how requesters with varying expertise communicate and collaborate with crowd workers. Our work also identifies the unique challenges that collaborative crowdsourcing systems pose: potential expertise differences between requesters and crowd workers, the asymmetry of two-way communication (e.g., speech versus text), and the shared artifact's concurrent modification by two disparate groups. Finally, we make design recommendations that can inform the design of future real-time collaboration processes in crowdsourcing systems.

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Prior work in programming-by-demonstration (PBD) has explored ways to enable end-users to create custom automation without needing to write code. We propose a new end-user specification model -asking the end-user to explicitly identify parts of their natural language query that can be generalized. We built a PBD system, ParamMacros, where users first generalize a concrete natural language question -identifying parameters and their possible values -and then create a demonstration of how to answer the question on the website of interest. ParamMacros then infers a generalized program by using the user-provided parameter values to identify relevant patterns in the website's structure. In a lab study we found that participants were able to meaningfully parameterize natural language queries and felt such a parameterization and demonstration process would be useful for creating custom automation.

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