Human and Machine Checking

In a post late last night, James Bach and Michael Bolton refined their definitions of testing and checking, and introduced a new distinction between human and machine checking.  What follows is an exploration of that latter distinction. These views are only partially baked, and I welcome your comments.

Recently Service Nova Scotia, which operates the registry of motor vehicles in the province of Nova Scotia, was featured on CBC radio. A number of customers had complained about the vetting of slogans used on personalized licence plates. According to local regulations it is unacceptable to display a plate that features obscene, violent, sexual or alcoholic references. As part of their vetting process, Service Nova Scotia uses software: a requested slogan is entered into a computer which then executes a check by comparing the slogan with a blacklist. If a match is found, the slogan is rejected. This is an example of machine checking: it is a process whereby a tool collects observations (reading the database field that holds the slogan), subjects those observations to an algorithmic decision rule (is the slogan in the blacklist?) and returns an evaluation (true or false). This is not the end of the story. Service Nova Scotia accepts that no machine can detect every case where a slogan may be inappropriate: a blacklist cannot possible contain every combination of letters and numbers that might be considered by someone to be offensive, slogans are also “checked” by a human. Of course, problems occasionally sneak through: there is a “TIMEUP” driving around Nova Scotia. Look at that closely; there are a couple of ways one could read that. One suggests a ticking clock; the other an invitation involving whips and chains.

This is a check in the natural sense, in the sense that the term is used in everyday English: “Please check this slogan”, “I checked the slogan, and it was fine”. But is it a human check per Bolton and Bach’s definition? Not necessarily: the definition turns on whether humans are attempting to apply algorithmic rules or whether they are doing something else1.

Let’s explore that distinction by way of another example. Imagine a computer program that produces a list of people’s names, where initials are allowed. An excerpt from such output might look something like this:

  • S.P. Maxwell
  • Judith Irving
  • Roderick Judge
  • 1-555-123-4567
  • Sally-Ann Hastings

I’d be surprised if you didn’t spot a bug! 1-555-123-4567 looks a lot like a (fake) telephone number rather than a name. In fact, whilst looking at this list you might be alert to a number of items that are clearly not names:

  • 37 Main St.
  • *%^&$
  • 

These are examples of items that could be detected by checking, in that one can form a rule to highlight them:

  • Names should only contain letters, periods or hyphens2.

Please go back and evaluate the list of names using that rule. Congratulations! You just performed a human check. Now try to evaluate this list using the same rule:

  • Rufus Mulholland the 3rd
  • F.U. Buddy
  • Tasty Tester
  • Metallica Jones

In this case you have again performed a human check, in that you attempted to apply the rule to your observations. What differentiates this from a machine check is that you may have done something that a machine, applying the rule, could not have:

  • You may have recognized that the suffix “the 3rd” is perfectly acceptable in a name, even though it violates the rule.
  • You may have recognized that “F.U. Buddy” is potentially offensive, and that “Tasty Tester” and “Metallica Jones”3 are unlikely names, even though they do not violate the rule.

So what happened here? As you observed the list of names, you glossed over the first item, then on the second, third and fourth items you experienced a flash of recognition as your expectations were violated: you have brought your tacit knowledge to bear. Specifically, you have drawn on collective tacit knowledge4, knowledge that is rooted in society. It is entirely reasonable to assume that some readers of this blog, who are not native English speakers or who are unfamiliar with Western popular music, would have reacted differently to this list.

What does this have with attempting to check? The distinction relates to the difference between machine and human evaluation. A machine can evaluate observations against a rule in the same sense that a conditional expression evaluates to true or false. And of course, a human can do this too. What a machine cannot do, and a human will struggle not to do, is to connect observations to value.  When a human is engaged in checking this connection might be mediated through a decision rule: is this output of check a good result or a bad one? In this case we might say that the human’s attempt to check has succeeded but that at the point of evaluation the tester has stepped out from checking and is now testing. Alternatively, a human might connect observations to value in a way such that the checking rule is bypassed. As intuition kicks in and the tester experiences a revelation (“That’s not right!”) the attempt to check has failed in that the rule has not been applied, but never mind: the tester has found something interesting. Again, the tester has stepped out from checking and into testing. This is the critical distinction between human and machine checking: that a human – even when attempting to apply a rule – has the capacity5 to connect observations to value within the frame of a project, a business, or society in general. A human, on being exposed to a threat to value, can abort a check and revert to testing. In contrast, all a machine check can do is report the result of observations being subjected to a rule.

This has important implications. Computers are wondrous things; they can reliably execute tasks with speed, precision and accuracy that are unthinkable in a human. But when it comes to checking, they can only answer questions that we have thought to program them to ask. When we attempt to substitute a machine check for a human check, we are throwing away the opportunity to discover information that only a human could uncover.

Notes

1 In case you are wondering, humans at Service Nova Scotia are “doing something else”. They do not attempt to apply an explicit decision rule; they “eyeball” the slogan and reject it if anything strikes them as being inappropriate. No rule is involved, no attempt to check is made.

2 I’m sure you can come up with lots of ways in which this rule is inadequate.

3 We need to be careful with this one: some fans are obsessive: in the UK there is at least one called, after changing his name by deed poll, “Status Quo”.

4 Tacit and Explicit Knowledge, Harry Collins (2010).

5 I describe this as a capacity: it not a guarantee that a human will recognize all threats to value. Recall that such recognition is tied to collective tacit knowledge, knowledge that is rooted in society. Your exposure to different projects, different environments, different cultures, has a bearing on the problems that you will recognize. For example: I used to work for a Telecommunications operator in the UK. On one occasion a disgruntled employee reputedly changed the account details of one of our major customers, a police force, such that bills would be addressed to “P.C. Plod”. British readers are likely to recognize this as a potentially embarrassing problem. For those of you who haven’t spent any time in the UK there is a good chance that this would be invisible to you: “Plod” is a mildly offensive term for a police officer and suggests a slowness of wit.

Consistency

A few months ago, I met a colleague from a distant part of the organization.

“My biggest problem”, he said, “is a lack of consistency in test analysis. No two of my testers do it the same”.

“Really?” I enquired, “How do you mean?”

“Well, they document things differently, but that isn’t the problem…given the same specification they’ll come up with different tests. They approach things differently on each project. I need them to be doing the same things every time: I need consistency, I need consistent processes and methods.”

“Well, consistency can be important” I replied. “Will consistently crap work for you?”

“No, of course not. We need to be doing good testing.”

“So a consistent approach might be less important than consistent results?”

“Er, maybe. Doesn’t one imply the other?”

And therein lies the problem. With simple systems it’s reasonable to assume that you will always get the same output for a given set of inputs. Think of domestic light: you flick the switch and the light comes on. Flick it again and the light goes off. Even adding a little complexity by means of another staircase switch doesn’t add much in the way of complication. Now we don’t know whether switch-up or switch-down equates to on or off, but flicking the switch still toggles the state of the light.

If only software development was like this! Such simplicity is beguiling, and many of our mental models are rooted in the assumption that this is how the world works. It doesn’t of course.

Unfortunately, software projects are nothing like this. Imagine a lighting circuit with many, many switches. Imagine you don’t know how many switches there are, or indeed where they are. Imagine that you have no knowledge of the starting state of the circuit, and therefore which switches need flicking to get the lights on. Imagine that some of the switches are analogue dimmers rather than their binary cousins. Imagine that there are other actors who are also flicking switches or changing the configuration of the circuit. Imagine that those involved have different ideas as to which light should come on, or how bright it should be: the streetlight folk and the reading light people just can’t agree, and nobody’s talking to team ambience. Now imagine that you’ve been asked to assess the quality of the lighting once someone has figured out how to turn the damned thing on.

Now subject this scenario to a consistent set of inputs. Try to form some repeatable algorithm to reliably “solve” this problem. You can’t, it’s impossible, it’s an intractable problem. This feels a little more like the testing I know, though still a hopeless simplification.

“That,” I explained, “is why we need to emphasize skill over Method. Software projects are complex systems, and repeating the same inputs doesn’t work very well. Mechanical, repeatable processes are no guarantee of success. We need testers who are able to navigate that maze, and figure out what needs doing given.”

I made a few notes, wrote a few tweets, and got back to business. Thoughts about consistency however, never strayed far away. Then, earlier this week, they paid me another visit.

I’ve been moonlighting as a lecturer at Dalhousie University. At the start of the course I set the students an assignment (based on one of Kaner’s from BBST Test Design) to analyze a specification and identify a list of test ideas. This week’s lecture was on test design: for the first half of the lecture we discussed some of the better known, and more mechanistic, test design techniques. Then I asked the class to break into groups, compare and contrast their test ideas, and present on any similarities and differences.

“We had a few ideas in common,” said the first student “and a load of differences”. He went on to list them.

“You came up with different ideas?” I asked, feigning horror.

“Er, yes”.

“How can that be? I’ve just spent a good part of the last 3 hours describing a set of techniques that should guarantee the same set of tests. What happened?”

“Well, um, we came up with a lot of ideas that we wouldn’t have using those techniques.”

“Good, I was hoping as much.”

“?”

“Tell me why you think there were differences. Why wouldn’t these techniques have suggested those ideas?” I asked.

“Well, I guess we used our imaginations. And we’ve all got different backgrounds, so we all thought of different things.”

“Exactly: the techniques we’ve discussed are mechanistic. They might give you consistency, but they remove imagination –and your own experiences- from the test design. Now, tell me, which would you prefer: testing based on your original list of ideas, or based on the ideas of the group as a whole? Which do you think would provide better information to the project?”

“The group’s ideas” he said without hesitation.

“Why?”

“Well some of us focused on specific things, the overall list is better…better-rounded. We’d miss lots of stuff with our own.”

“So this inconsistency between individuals, is it a good thing or a bad thing?”

“I think it’s good. Those techniques might be useful for some problems, but by sharing our different ideas, I think we can test better.”

“Thank you” I said, mission accomplished.