Improving the adaptability of multi-mode systems via program steering

ABSTRACT

A multi-mode software system contains several distinct modes of operation and a controller for deciding when to switch between modes. Even when developers rigorously test a multi-mode system before deployment, they cannot foresee and test for every possible usage scenario. As a result, unexpected situations in which the program fails or underperforms (for example, by choosing a non-optimal mode) may arise. This research aims to mitigate such problems by creating a new mode selector that examines the current situation, then chooses a mode that has been successful in the past, in situations like the current one. The technique, called program steering, creates a new mode selector via machine learning from good behavior in testing or in successful operation. Such a strategy, which generalizes the knowledge that a programmer has built into the system, may select an appropriate mode even when the original controller cannot. We have performed experiments on robot control programs written in a month-long programming competition. Augmenting these programs via our program steering technique had a substantial positive effect on their performance in new environments.

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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.4 Software/Program Verification

General Terms:
Design, Experimentation, Performance, Reliability

Keywords:
adaptability, mode selection, multi-mode systems, program steering