Will Robots Replace Testers?

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A recent study from the University of Oxford makes for interesting reading:

  • Over the next two decades, 47% of jobs in the US may be under threat.
  • 702 occupations are ranked in order of their probability of computerisation. Telemarketers are deemed most likely (99%), recreational therapists least likely at 0.28%. Computer programmers appear to be 48% likely to be replaced.

If programmers have a 50/50 chance of being replaced by robots, we should think seriously on how the same might happen to testers.

Machine Learning in testing is an intriguing prospect but not imminent. However, the next generation of testing tools will look a lot different from the ones we use today.

For the past thirty years or so we have placed emphasis on test automation and checking. In the New Model for Testing, I call this ‘Applying’. We have paid much less attention to the other nine – yes, nine – test activities. As a consequence, we have simple robots to run tests, but nothing much to help us to create good tests for those robots to run. 

In this paper, I am attempting to identify the capabilities of the tools we need in the future.

The tools we use in testing today are limited by the approaches and processes we employ. Traditional testing is document-centric and aims to reuse plans as records of tester activity. That approach and many of our tools are stuck in the past. Bureaucratic test management tools have been one automation pillar (or millstone). The other pillar – test automation tools – derive from an obsession with the mechanical, purely technical execution activity and is bounded by an assertion that many vendors still promote – that testing is just bashing keys or touchscreens which tools can do just as well.

The pressure to modernise our approaches, to speed up testing and reduce the cost and dependency on less-skilled labour means we need some new ideas. I have suggested a refined approach using a Surveying metaphor. This metaphor enables us to think differently on how we use tools to support knowledge acquisition.

The Survey metaphor requires new collaborative tools that can capture information as it is gathered with little distraction or friction. But they can also prompt the user to ask questions, to document their thoughts, concerns, observations and ideas for tests. In this vision, automated tools get a new role – supportive of tester thinking, but not replacing it.

Your pair in the exploration and testing of systems might soon be a robot. Like a human partner, they will capture the knowledge you impart. Over time they will learn how to support and challenge you and help you to navigate through your exploration or Surveying activity. Eventually, your partner will suggest ideas that rival your own. But that is still some way off.

To download the full paper, go to the Tools Knowledge Base.

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