Point Lookout: a free weekly publication of Chaco Canyon Consulting
Volume 16, Issue 2;   January 13, 2016: When Fixing It Doesn't Fix It: Part II

When Fixing It Doesn't Fix It: Part II

by

When complex systems misbehave, repairs can require deep thought, inspiration, and careful reasoning. Here are guidelines for a systematic approach to repairing complex systems.
A curious baby

A curious baby. According to Prof. Laura E. Schulz, associate professor of cognitive science in the Brain and Cognitive Sciences department at MIT, babies' approach to learning about the world has much in common with the scientific method. In some sense, we would do better at debugging complex systems if we took a more childlike approach. Photo (cc) Creative Commons Attribution 2.0 by Kenny Louie courtesy Wikimedia.

To repair complex systems, many resort to "random twiddling and part replacement" (RTAPR) when they're under time and resource constraints. Sadly, RTAPR doesn't work very well. For example, consider a system that has six commercial off-the-shelf components. Let's suppose that it isn't working right. We decide to replace Module 2, which produces no change — the system continues to misbehave. Some might conclude that this proves that Module 2 is OK, but that conclusion might be mistaken. Suppose that the problem lies in the firmware of Module 2, which controls how it operates on the data it receives from Module 1. Since both of our Module 2 boxes contained the same firmware, the system behavior didn't change when we made the swap. A conclusion that Module 2 was not involved in the fault would therefore be incorrect.

A more careful approach can work better than RTAPR. Here are some guidelines that form the basis of what is usually called the scientific method.

Perform no random experiments
Random experiments, especially those involving system configuration changes, are unlikely to produce new knowledge. The more complicated the system, the less productive are random experiments.
Keep excellent records
Record the Random experiments, especially if
they involve system configuration
changes, are unlikely to
produce new knowledge
details of all experiments and results. Typically, you won't refer to these notes until you're completely stumped, but that happens with alarming frequency for complex systems. So write the notes so as to make them clear in that kind of desperate situation.
Try to replicate unwanted behavior
(a) If the unwanted behavior is reliably repeatable, observe the results of making a minimal change to the system. Any change in behavior can be revealing. (b) If the unwanted behavior isn't repeatable, try to find a system configuration that makes it repeatable, and then go to (a). In all such experiments, controlling the system's containing environment is essential.
Base all attempts on hypotheses
Because the input configuration for a complicated system is also complicated, proving that complicated systems work for all required inputs is difficult. Hypotheses about why the system isn't working are equally difficult to prove. Hypotheses can more readily be disproven than proven.
Therefore, have a testable hypothesis in mind whenever you change the system configuration. Testable hypotheses are of this form (for example): "The fault might be A. If experiment B produces behavior C, then the fault cannot be A." Repeating this process gradually eliminates possibilities until only the truth remains.
Fail forward
Devise hypotheses and experiments that cause your investigation to "fail forward." That is, favor experiments that produce useful knowledge whatever the outcome of the experiment. If you make a change and the system starts working, that should help explain what was wrong. And if that same change causes some other result, that, too, should be enlightening information.

Adhering to these guidelines can be difficult, especially under pressure. If deviation is required, make note of it, and note how deviations affect your conclusions. First in this series  Go to top Top  Next issue: Virtual Clutter: Part I  Next Issue

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