“I believe that economists put decimal points in their forecasts to show they have a sense of humor.” – William Gilmore Simms (great quote, questionable quotee).
The unglamour of SQA
Before you can be strategic you have to be competent.
That’s according to Keep the Joint Running: A Manifesto for 21st Century Information Technology, (me, 2012), the source of all IT management wisdom worth wisdoming.
An unglamorous but essential ingredient of IT organizational competence is software quality assurance (SQA), the nuts-and-bolts discipline that makes sure a given application does what it’s supposed to do and doesn’t do anything else.
SQA isn’t just one practice. It’s several. It checks:
Software engineering – whether code adheres to the overall system architecture, is properly structured, and conforms to coding style standards.
Unit testing – whether a module correctly turns each possible input into the expected output.
Integration testing – whether a module interacts properly with all the other modules the team is creating.
Regression testing – whether the new modules break anything that’s already in production.
Stress testing – whether the whole system will perform well enough once everyone starts to bang on it.
User acceptance – whether the new modules are aesthetically pleasing enough; also, whether they do what the business needs them to do – do they, that is, effectively support, drive, and manage the business processes they’re supposed to support, drive, and manage.
Ideally, IT’s SQA function will establish and maintain automated test suites for all production applications and keep them current, to ensure efficient and correct unit, integration, regression, and stress testing.
In practice, creating and managing automated test suites is really, really hard.
This looks like a fabulous opportunity for generative AI, doesn’t it? Instead of asking it to generate a mathematical proof in the style of William Shakespeare, point your generative AI tool of choice to your library of production application code and tell it to … generate? … an automated test suite.
Generative AI, that is, could take one of the most fundamental but time-consuming and expensive aspects of IT competence and turn it into a button-push.
Except for this annoying tidbit that’s been an issue since the earliest days of “big data,” generative AI’s forgotten precursor: How to perform SQA on big data analytics, let alone on generative AI’s responses to the problems assigned of it.
Way, way, way back we had data warehouses. Data warehouses start with data cleansing, so your business statisticians could rely on both the content and architecture of the data they analyzed.
But data warehouse efforts were bulky. They took too long, were anything but flexible, and frequently collapsed under their own weight, which is why big data, in the form of Hadoop and its hyperscale brethren, became popular. You just dumped your data into some data lakes, deferring data cleansing and structuring … turning that data into something analyzable … until the time came to analyze it. It was schema on demand, shifting responsibility from the IT-based data warehouse team to the company’s newly re-named statisticians, now “data scientists.”
The missing piece: SQA.
In scientific disciplines, researchers rely on the peer review process to spot bad statistics, along with all the other flaws they might have missed.
In a business environment, responsibility for detecting even such popular and easily anticipated management practices as solving for the number has no obvious organizational home.
Which gets us to this week’s conundrum. We might call it SQA*2. Imagine you ask your friendly generative AI to automagically generate an automated test suite. It happily complies. The SQA*2 challenge? How do you test the generative AI’s automated test suite to make sure the flaws it uncovers are truly flaws, and that it doesn’t miss some flaws that are present – feed it into another generative AI?
Bob’s last word: It’s easy, and gratifying, to point out all the potential gaps, defects, fallacies, and potential pitfalls embedded in generative-AI implementations. In the generative-AI vs human beings competition, we can rely on confirmation bias to assure ourselves that generative-AI’s numerous flaws will be thoroughly explored.
But even in the technology’s current level of development, we Homo sapiens need to consider the don’t-have-to-outrun-the-bear aspect of the situation:
Generative-AI doesn’t have to be perfect. It just has to be better at solving a problem than the best human beings are.
This week’s SQA*2 example … the automated generation of automated test suites … exemplifies the challenge we carbon-based technologies are going to increasingly face as we try to justify our existence given our silicon-based competition.
Bob’s sales pitch: You are required to read Isaac Asimov’s short story in which he predicts the rise of generative AI. Titled “The Jokester,” it’s classic Asimov, and well worth your time and attention (and yes, I did say the same thing twice).
Now on CIO.com’s CIO Survival Guide: “5 IT management practices certain to kill IT productivity.” What’s it about? The headline is accurate.