Synthesis of web layouts from examples

We present a new technique for synthesizing dynamic, constraint-based visual layouts from examples. Our technique tackles two major challenges of layout synthesis. First, realistic layouts, especially on the web, often contain hundreds of elements, so the synthesizer needs to scale to layouts of this complexity. Second, in common usage scenarios, examples contain noise, so the synthesizer needs to be tolerant to imprecise inputs. To address these challenges we propose a two-phase approach to synthesis, where a local inference phase rapidly generates a set of likely candidate constraints that satisfy the given examples, and then a global inference phase selects a subset of the candidates that generalizes to unseen inputs. This separation of concerns helps our technique tackle the two challenges: the local phase employs Bayesian inference to handle noisy inputs, while the global phase leverages the hierarchical nature of complex layouts to decompose the global inference problem into inference of independent sub-layouts. We implemented this technique in a tool called Mockdown and evaluated it on nine real-world web layouts, as well as a series of widespread layout components and an existing dataset of 644 Android applications. Our experiments show that Mockdown is able to synthesize a highly accurate layout for the majority of benchmarks from just three examples (two for Android layouts), and that it scales to layouts with over 600 elements, about 30x more than has been reported in prior work on layout synthesis.