  Block-based visual programming environments play a critical role in
introducing computing concepts to K-12 students. One of the key pedagogical
challenges in these environments is in designing new practice tasks for a
student that match a desired level of difficulty and exercise specific
programming concepts. In this paper, we formalize the problem of synthesizing
visual programming tasks. In particular, given a reference visual task $\rm
T^{in}$ and its solution code $\rm C^{in}$, we propose a novel methodology to
automatically generate a set $\{(\rm T^{out}, \rm C^{out})\}$ of new tasks
along with solution codes such that tasks $\rm T^{in}$ and $\rm T^{out}$ are
conceptually similar but visually dissimilar. Our methodology is based on the
realization that the mapping from the space of visual tasks to their solution
codes is highly discontinuous; hence, directly mutating reference task $\rm
T^{in}$ to generate new tasks is futile. Our task synthesis algorithm operates
by first mutating code $\rm C^{in}$ to obtain a set of codes $\{\rm C^{out}\}$.
Then, the algorithm performs symbolic execution over a code $\rm C^{out}$ to
obtain a visual task $\rm T^{out}$; this step uses the Monte Carlo Tree Search
(MCTS) procedure to guide the search in the symbolic tree. We demonstrate the
effectiveness of our algorithm through an extensive empirical evaluation and
user study on reference tasks taken from the \emph{Hour of Code: Classic Maze}
challenge by \emph{Code.org} and the \emph{Intro to Programming with Karel}
course by \emph{CodeHS.com}.
