  Programmable robots are engaging and fun to play with, interact with the real
world, and are therefore well suited to introduce young learners to
programming. Introductory robot programming languages often extend existing
block-based languages such as Scratch. While teaching programming with such
languages is well established, the interaction with the real world in robot
programs leads to specific challenges, for which learners and educators may
require assistance and feedback. A practical approach to provide this feedback
is by identifying and pointing out patterns in the code that are indicative of
good or bad solutions. While such patterns have been defined for regular
block-based programs, robot-specific programming aspects have not been
considered so far. The aim of this paper is therefore to identify patterns
specific to robot programming for the Scratch-based mBlock programming
language, which is used for the popular mBot and Codey Rocky robots. We
identify: (1) 26 bug patterns, which indicate erroneous code; (2) three code
smells, which indicate code that may work but is written in a confusing or
difficult to understand way; and (3) 18 code perfumes, which indicate aspects
of code that are likely good. We extend the LitterBox analysis framework to
automatically identify these patterns in mBlock programs. Evaluated on a
dataset of 3,540 mBlock programs, we find a total of 6,129 instances of bug
patterns, 592 code smells and 14,495 code perfumes. This demonstrates the
potential of our approach to provide feedback and assistance to learners and
educators alike for their mBlock robot programs.
