Tag Archives: Radical Ignorance

Interview with a CEO

“So, tell me: How are you going to guarantee the accuracy and integrity of the data?” he asked.

I glanced at the clock on the wall: 2:25 p.m. The CEO and I had been talking since 2:00, and he had to be at his next meeting in 5 minutes.

I felt frozen, like a tilted pinball machine. For a moment I wasn’t even sure I’d heard the question right. He couldn’t seriously be asking a tester for… whaaa??? I could feel my adrenal glands dumping their contents into my blood stream.

“This is the moment,” I thought. “The point when this interview goes South.”

Part of me wanted to simply stand up, shake the CEO’s hand, thank him for the opportunity, and walk out. I could still salvage a nice afternoon before I had to be back at the airport.

Time seemed to slow to an agonizing crawl. Involuntarily, I pondered the previous 12 hours…

2:45AM Wake up, shower… 3AM Dress up in suit and tie (20 minutes devoted to fighting with tie)… 3:45AM Drive to airport… 5AM Sit in terminal… 6AM Board flight to San Francisco… 9AM Arrive SFO… 9:15AM Sit in (completely stationary) BART train… 10:00AM Miss Caltrain connection… 10:30AM Arrive at office, thanks to a ride from their helpful administrative assistant… 10:45AM Interview with the head of products… 11:30AM Interview with the head of development… 12:15PM Lunch… 2:00PM Interview with CEO…

As the epinephrine circulated through my body, creating a sensation akin to somersaulting backwards, I began to feel resentful. I’d flown there on my own dime, after having already talked with these guys by phone for several hours. I was under the impression that the trip would be more of a “meet & greet the team” social hour. Not a repeat of the entire interview process, from square one. The Head of Products had given me several assurances that I was his top choice and that they’d only be asking me to fly out if the position were essentially mine to refuse.

So, there I was. The CEO sat across the table from me, expecting an answer.

What I wanted to say was that I was in no position to guarantee anything of the sort, given my radical ignorance of the data domain, the data’s source(s), the sources’ track record(s) for accuracy, or how the data get manipulated by the in-house systems.

What I wanted to say was that his question was prima facie absurd. That I, as a tester, couldn’t “guarantee” anything other than that I would use my skills and experience to find as many of the highest risk issues as quickly as possible in the given time frame. However, when you’re dealing with any black box, you can’t guarantee that you’ve found all the problems. Certainty is not in the cards.

What I wanted to say was that anyone who sat in front of the CEO claiming that they could guarantee the data’s accuracy and integrity was clearly a liar and should be drummed out of the profession of software testing.

I wanted to say all that and more, but I didn’t. Given the day’s exhausting schedule, all these thoughts were little more than fleeting, inchoate, nebulous impressions. Plus, it seemed highly unlikely that the CEO, who struck me as an impatient man (your typical “Type A” personality), would be interested in spending the remaining 4 or 5 minutes discussing epistemology with me. Honestly, I’m not sure what I said, exactly. The question, and the CEO’s demeanor while asking it, had drained away any enthusiasm I had for the position. In all likelihood, my response was along the lines of “I have no idea how to answer that question.”

Whatever I said, it was obviously not how to impress an MBA from Wharton. I didn’t get offered the job.

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Estimating Testing Times: Glorified Fortune-Telling?

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Hofstadter’s Law:
It always takes longer than you
expect, even when you take
into account Hofstadter’s Law.

Douglas Hofstadter

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A good friend of mine is a trainer for CrossFit, and has been for years. For a long time he trained clients out of his house, but his practice started outgrowing the space. His neighbors were complaining about the noise (if you’ve ever been in a CrossFit gym you can easily imagine that they had a point). Parking was becoming a problem, too.

So, in September, 2009, he rented a suite for a gym, in a building with an excellent location and a gutted interior–perfect for setting up the space exactly how he wanted it. It needed new flooring, plumbing, framing, drywall, venting, insulation, dropped ceiling, electricity, and a few other minor things. At the time, he told me they’d be putting the finishing touches on the build-out by mid-December. I remember thinking, “Wow. Three months. That’s a long time.”

As it turned out, construction wasn’t completed until late June, 2010, Seven months later than originally estimated.

Let’s think about that. Here’s a well-defined problem, with detailed plans (with drawings and precise measurements, even!) and a known scope, not prone to “scope creep.” The technology requirements for this kind of project are, arguably, on the low side–and certainly standardized and familiar. The job was implemented by skilled, experienced professionals, using specialized, efficiency-maximizing tools. And yet, it still took more than 3 times longer than estimated.

Contrast that with a software project. Often the requirements are incomplete, but even when they’re not, they’re still written in words, which are inherently ambiguous. What about tools? Sometimes even those have to be built, or existing tools need to be customized. And the analogy breaks down completely when you try to compare writing a line of code (or testing it) with, for example, hanging a sheet of drywall. Programmers are, by definition, attempting something that has never been done before. How do you come up with reasonable estimates in this situation?

This exact question was asked in an online discussion forum recently. A number of self-described “QA experts” chimed in with their answers. These all involved complex models, assumptions, and calculations based on things like “productivity factors,” “data-driven procedures,” “Markov chains,” etc. My eyes glazed over as I read them. If they weren’t all committing the Platonic fallacy then I don’t know what it is.

Firstly, at the start of any software project you are, as Jeffrey Friedman puts it, radically ignorant. You do not know what you do not know. The requirements are ambiguous and the code hasn’t even been written yet. This is still true for updates to existing products. You can’t be certain what effect the new features will have on the existing ones, or how many bugs will be introduced by re-factoring the existing features. How can you possibly know how many test cases you’re going to need to run? Are you sure you’re not committing the Ludic Fallacy when you estimate the “average time” per test case? Even if you’ve found the perfect estimation model (and how would you know this?), your inputs for it are bound to be wrong.

To attempt an estimate in that situation is to claim knowledge that you do not possess. Is that even ethical?

Secondly, your radical ignorance goes well beyond what the model’s inputs should be. What model takes into account events like the following (all of which actually happened, on projects I’ve been a part of)?

  1. The database containing the company’s live customer data–all of it–is inadvertently deleted by a programmer who thought at the time that he was working in the developer sandbox.
  2. The Director of Development, chief architect of the project, with much of the system design and requirements kept only in his head, fails to appear at work one day. Calls to his home go unanswered for two weeks. When someone finally gets in touch with him he says he won’t be coming back to work.
  3. A disgruntled programmer spends most of his time putting derogatory easter eggs in the program instead of actually working. When found by a particularly alert tester (sadly I can’t claim it was me) the programmer is fired.
  4. A version of the product is released containing an egregious bug, forcing the company to completely reassess  its approach to development (and blame the testers for missing the “obvious” bug, which then destroys morale and prompts a tester to quit).
  5. The company’s primary investor is indicted for running a ponzi scheme. The majority of the employees are simply let go, as there is not enough revenue from sales to continue to pay them.

The typical response from the “experts” has been, “Well, that’s where the ‘fudge factor’ comes in, along with the constant need to adjust the estimate while the project is underway.”

To that I ask, “Isn’t that just an implicit admission that estimates are no better than fortune-telling?”

I heard from Lynn McKee recently that Michael Bolton has a ready answer when asked to estimate testing time: “Tell me what all the bugs will be, first, then I can tell you how long it will take to test.”

I can’t wait to use that!

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