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Pattern detection methods discover recurring solutions in a system's implementation, for example design patterns in object-oriented source code. Usually this is done with a pattern library. This has the disadvantage that the precise implementation of the patterns must be known in advance. The method used in our case study does not have this disadvantage. It uses a mathematical technique called Formal Concept Analysis and is applied to find structural patterns in two subsystems of a printer controller. The case study shows that it is possible to detect frequently used structural design constructs without upfront knowledge. However, even the detection of relatively simple patterns in relatively small pieces of software takes a lot of computing time. Since this is due to the complexity of the applied algorithms, applying the method to large software systems like the complete controller is not practical. They can be applied to its subsystems though, which are about five to ten percent of its size.

Behavior Trees are a promising approach to model the autonomous behaviour of robots in dynamic environments. Behavior Trees represent action selection decisions as a tree of decision nodes. The hierarchy of these decision nodes provides the planning of actions of the robot including its reactions on exceptions. Behavior Trees enable flexible planning and replanning of robot behavior while supporting better maintainable decision-making than traditional Finite State Machines. This paper presents an overview of lessons, which we have learned when applying Behavior Trees to various autonomous robots. We present these lessons as a sequence of steps that is meant to support robot software practitioners to develop their systems.