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Moving targets: Testing software in the age of machine learning

Peter Varhol Principal, Technology Strategy Research

Testing systems that don’t always return the same answers require new approaches. This is especially true when testing systems whose responses adapt to what they have learned from previous transactions.

Software testing, in theory, is a fairly straightforward activity. For every input, there should be a defined and known output. We enter values, make selections, or navigate an application and compare the actual result with the expected one. If they match, we nod and move on. If they don’t, we possibly have a bug.

The point is, we already know what the output is supposed to be. Granted, sometimes an output is not well defined, there is some ambiguity, and you get disagreements on whether or not a particular result represents a bug.

But there is a type of software where having a defined output is no longer the case. Actually, two types. One is machine learning systems; the second is predictive analytics.

Difference between machine learning and predictive analytics

There is a difference between the two. Most machine learning systems are based on neural networks. A neural network is a set of layered algorithms whose variables can be adjusted via a learning process. The learning process involves using known data inputs to create outputs that are then compared with known results. When the algorithms reflect the known results with the desired degree of accuracy, the algebraic coefficients are frozen and production code is generated.

Today, this comprises much of what we understand as artificial intelligence.

By contrast, predictive analytics makes adjustments to the algorithms in production, based on results fed back into the software. In other words, the application better understands how to apply its rules based on how those rules have worked in the past.

Both of these types of systems have things in common. For one thing, neither produces an “exact” result. In fact, sometimes they can produce an obviously incorrect result. But they are extremely useful in a number of situations when data already exists on the relationship between recorded inputs and intended results.

Let me give you an example, based on my own experiences.

Developing a few guidelines

Years ago, I devised a neural network that worked as a part of an electronic wind sensor. This worked though the wind cooling the electronic sensor based on its decrease in temperature at specific speeds and directions. My neural network had three layers of algebraic equations, each with four to five separate equations in individual nodes, computing in parallel. They would use starting coefficients, then adjust those coefficients based on a comparison.

A neural network is a set of layered algorithms whose variables can be adjusted via a learning process. 

I then trained it. I had over 500 data points regarding known wind speed and direction and the extent to which the sensor cooled. The network I created passed each input into its equations, through the multiple layers, and produced an answer. At first, the answer from the network wasn't that close to the known correct answer. But after multiple iterations with the training data, the coefficients homed in on accurate and consistent results.

How do you test this? The product is actually tested during the training process, which takes time. Training either brings convergence to accurate results or it diverges. The question is, How do you evaluate the quality of the network?

Predictive analytics systems continue to adapt after deployment, using a feedback loop to adjust variables and coefficients within the algorithm.

Here are the guidelines I used.

  1. Have objective acceptance criteria. Know the amount of error you and your users are willing to accept.
  2. Test with new data. Once you’ve trained the network and frozen the architecture and coefficients, use fresh inputs and outputs to verify its accuracy.
  3. Don’t count on all results being accurate. That’s just the nature of the beast. And you may have to recommend throwing out the entire network architecture and starting over.
  4. Understand the architecture of the network as a part of the testing process. Few if any will be able to actually follow a set of inputs through the network of algorithms, but understanding how the network is constructed will help testers determine if another architecture might produce better results.
  5. Communicate the level of confidence you have in the results to management and users. Machine learning systems offer you a unique opportunity to describe confidence in statistical terms, so use them.

One important thing to note is that the training data itself could well contain inaccuracies. In this case, because of measurement error, the recorded wind speed and direction could be off or ambiguous. In other cases, the cooling of the filament likely has some error in its measurement.

What about predictive analytics?

Another type of network might be used for predictive analytics. These systems continue to adapt after deployment, using a feedback loop to adjust variables and coefficients within the algorithm.

One example is a system under development in the UK to implement demand-based pricing for train service. Its goal is to encourage riders to use the train during nonpeak times, so it dynamically adjusts pricing to make it financially attractive for riders to consider riding when the trains aren’t as crowded.

This type of application experiments with different pricing strategies and tries to optimize two different things: a balance of the ridership throughout the day, and acceptable revenue from the ridership. A true mathematical optimization isn’t possible, but the goal is to reach a state of spread-out ridership and revenue that at least covers costs.

How would you test this type of application? Once again, you need objective acceptance criteria before even starting the development effort. In this case, those acceptance criteria should involve some acceptable level of both ridership averages and revenue.

What testers need to know

Here are some other important considerations.

  1. You need test scenarios. By that I mean ridership, when people are willing to ride, and what they are willing to pay. Because people don’t know until they are actually placed in the position of deciding, you are going to have to build data models. Three may be sufficient, to represent expected best case, average case, and worst case.
  2. You will not reach mathematical optimization of either ridership or revenue. We are, after all, working with algorithms that produce approximations, not exact results. Determine what level of outcomes is acceptable for each scenario.
  3. Defects will be reflected in the inability of the model to achieve the goals of ridership and revenue.

Note that in both the machine learning and analytics application examples, the acceptance criteria aren’t expressed in terms of defect number, type, or severity. In fact, in most cases they are expressed in terms of the statistical likelihood of coming within a certain range. 

Best practices for testing nondeterministic applications

That evaluation of quality and risk isn’t a staple with most development and testing projects, and it is one that testers may be ill equipped to consider. How can testers provide better feedback on their efforts on such applications? First, evaluate the application according to the acceptance criteria. Second, be prepared to support those assertions in statistical terms; for example, be 95 percent confident that the application will produce an answer within a given range. Last, have a high-level understanding of the underpinnings of the application, so that any deficiencies might be able to be ascribed to a particular application component.

Both testing practices and results have to change to accommodate applications that don’t behave the same as traditional software. If you find yourself working on machine learning and predictive applications, these suggestions represent a good start in that direction.

Image source: Flickr

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