71,211 research outputs found

    RT-Syn: A real-time software system generator

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    This paper presents research into providing highly reusable and maintainable components by using automatic software synthesis techniques. This proposal uses domain knowledge combined with automatic software synthesis techniques to engineer large-scale mission-critical real-time software. The hypothesis centers on a software synthesis architecture that specifically incorporates application-specific (in this case real-time) knowledge. This architecture synthesizes complex system software to meet a behavioral specification and external interaction design constraints. Some examples of these external constraints are communication protocols, precisions, timing, and space limitations. The incorporation of application-specific knowledge facilitates the generation of mathematical software metrics which are used to narrow the design space, thereby making software synthesis tractable. Success has the potential to dramatically reduce mission-critical system life-cycle costs not only by reducing development time, but more importantly facilitating maintenance, modifications, and extensions of complex mission-critical software systems, which are currently dominating life cycle costs

    Learning to Infer Graphics Programs from Hand-Drawn Images

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    We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input
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