Takeaways from Elisabeth Hendrickson’s “There’s Always A Duck”

Elisabeth Hendrickson’s book “There’s Always A Duck” has been around for a number of years but I have only been able to read it recently. Now I know what she meant about ducks. They’re literally about ducks, but also about people too. People are different, and yet we share similarities. We experience things, we communicate with each other, and we learn and get better because of those experiences. Her book tells us stories of her adventures and the lessons she’s discovered along the way, and it was nice to have had a glimpse of what she saw and felt with her encounters with software project teams and everyone involved.

Some takeaways:

  • The vast majority of programmers I have met are diligent, capable folk. They truly care about the quality of their work and want the software they produce to be useful. They work hard to make sure they are implementing the right features and writing solid code.
  • The next time you’re tempted to think of your programmers as idiots, incompetents, or quality hostile, remember that no matter what else they may be, they’re people first. Even if it seems like they’re hostile or incapable, it is far more likely that they are having a very human reaction to a particularly bad situation.
  • And before you blame someone else for a mistake, remember the last time you made one. I’ve made some real whopper mistakes in my time. We all have, whether or not we choose to admit them or even remember them. It may be that some programmers don’t care about users, but it’s more likely that bugs are honest mistakes made under difficult circumstances.
  • Even when we are speaking the same language and about the same thing, it’s hard enough to communicate.
  • The point wasn’t to catch every possible error. What seems to go wrong most often? What errors are difficult to see at first glance, and thus require concentration to prevent? What causes the most damage when it happens?
  • Janet doesn’t know anything about the ins and outs of creating software. She probably doesn’t want to know. She just wants to serve her customers well. And this software is not helping. Back at corporate, the Steering Committee, Requirements Analysts, Designers, Programmers and Testers are congratulating themselves on a solid release. What they don’t see is Janet’s pain. The feedback loop is broken. The team back at corporate has no mechanism to find out whether the software is any good. Oh, sure, they’ll detect catastrophic problems that cause servers to go down. But they won’t see the little things that cause long queues at the front desk of the hotel.
  • Testers naturally notice details. Not only do we notice, but we think about what we noticed, we attempt to interpret our observations to explain why things might be that way, we ask others if they noticed, we question our assumptions, and we poke and prod at things to see if our understanding is correct. We use our observations to inform us, and in doing so discover underlying causes and effects we otherwise might miss.
  • I sometimes fall into the trap of thinking that the first problem I see must be THE problem that needs to be solved. Perhaps the problem I spotted is indeed worth correcting, but I almost never manage to spot the true critical issue at first glance.
  • Both fear and excitement stem not from observable reality but rather from speculation. We are speculating that the bugs that we know about and have chosen not to fix are actually as unimportant to our users as they are to us. We are speculating that the fact we have not found any serious defects is because they don’t exist and not because we simply stopped looking. We are speculating that we knew what the users actually wanted in the first place. We are speculating that the tests we decided not to run wouldn’t have found anything interesting. We are speculating that the tests we did run told us something useful. None of it is real until it is in the hands of actual users. The experience those users report is reality. Everything else is speculation.
  • It’s not because Agile is about going faster. It’s because structuring our work so that we can ship a smaller set of capabilities sooner means that we can collapse that probability wave more often. We can avoid living in the land of speculation, fooling ourselves into thinking that the release is alive (or dead) based on belief rather than fact. In short, frequent delivery means we live in reality, not probability.
  • Hire the right people. If that means keeping a critical position on the team open longer than anticipated, so be it. It’s better to have an under- staffed team of highly motivated, talented, skilled people than a fully staffed but ineffective team. Remember that hiring mistakes often take only a few minutes to make, and months of wasted time to undo.
  • Listen. There are always signs when a project is in trouble: missed milestones, recurrent attitude problems, general confusion about the project. Sometimes these signs indicate a dysfunctional team, sometimes they’re just normal bumps along the road, and sometimes they are early warning signs of major problems. The only way to tell the difference is to listen carefully to what the team members have to say.
  • The best way to get people to accept change is to make it more fun, and more rewarding, to do things the new way.
  • Choose a path that takes you in the direction you want to go. Don’t choose a path simply because it takes you away from the swamp you want to avoid.
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