In the real world, user productivity is the primary concern. Since the data collected was from unmodified, non-predictive command shells, we can only speculate as to the improvements in productivity afforded by the methods presented here. However, in the best case C4.5's predictions were correct up to 38% of the time. Recall that the lengths of the histories on average were 2184 commands, and the average command was 3.77 characters long. Assuming that a correct prediction could be inserted with a single character, and that commands recorded in the history were all typed explicitly (not using any historical shortcuts or command completion), this method would have saved just under 27% of the keystrokes typed, which is very close to the 28% expected if predictability were independent of command length.
| Figure 1: Macroaverage performance vs. training-window size | Table 1: Online predictive accuracy (for training-window size 1000) | ||||||||||||||||||
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This paper has described a number of methods for predicting a user's next
command in a UNIX command-line shell. We found that relatively
straightforward, knowledgefree methods were able to correctly predict the
next command that the user would execute up to 38% of the time,
potentially reducing the keystrokes needed by close to 27%. The best
performance was achieved using C4.5 to learn prediction rules based on
two previous commands, and a training window of either 500 or 1000 past
commands.