Skip to main content

Failure, Am I?


It's nothing personal I'm sure but at this week's Cambridge Agile Exchange Ben Mancini told me that I'm a failure. Ouch!

Failure, he says, is "the state or condition of not meeting a desirable or intended objective ... may be viewed as the opposite of success" and his hypothesis is that failure provides more learning than success but that we talk more about success than failure.

In his talk, Ben showed examples of people who have succeeded despite repeated failure, talked about the negative effects of failure, ways to reframe perceived failure, and the benefits of pushing through failure's cloud to its silver lining. Along the way he shared some of his own failures and invited us to share personal faux pas with our neighbours in the audience then look for the learnings in them.

One of the reasons I attend meetups is to be provoked into thought so, in no particular order ...

In my time I've certainly done things that I'd have preferred not to, and some things that others would have preferred I hadn't, and some things that were indisputably not what was required or expected by anyone. Perhaps I'm over-sensitive, but I wonder whether that makes me a failure, or just someone who has, on occasion, failed? If I'm going to call something a failure, I find that intuitively I want to judge actions rather than people.

Ben's definition of failure comes from Wikipedia. At first flush it seems reasonable but read further down the Wikipedia page and you'll find nuance that again accords with my own instinct. As with so many things, failure is subject to the relative rule: failure is failure to somebody at some time. The same event might be viewed differently by different people or by an individual in the moment and then later.

It's easy to say "failure is a better teacher than success" but historically I've been sceptical about it, at least stated as baldly as that: is it really better every time, for any person, for all kinds of learning? I changed my position slightly after reading Matthew Syed's excellent book, Black Box Thinking. I think learning from an action — positive or negative — requires reflection about (for example) what was done, what happened, the relationship between the two, other things that were done contemporaneously and other things that might have happened.

A failure might provoke more of that kind of reflection, for sure. As Richard Cook writes in his paper How Complex Systems Fail:
    ... all practitioner actions are actually gambles, that is, acts that take place in the face of uncertain outcomes. The degree of uncertainty may change from moment to moment. That practitioner actions are gambles appears clear after accidents; in general, post hoc analysis regards these gambles as poor ones. But the converse: that successful outcomes are also the result of gambles; is not widely appreciated.

For me, the key here is post-hoc. The same kind of learning might be taken from positive events if we reviewed them. To reflect on Ben's hypothesis: do we really talk more about success than failure? Which we, measured how?

I find Cook compelling on sociotechnical modes of failure, see e.g. Everybody Flirts, Fail Over, and Read it and Weep, and his system-wide perspective prompts another interesting question: whose learning are we interested in? The individual or the system?

In the talk, James Dyson was used as an example of someone who had failed many times before succeeding. His quest to create a bagless vacuum cleaner is well-documented and for me is an example of one context in which I'm comfortable to say that failure (on some specific interpretation) is indisputably a learning experience.

Dyson created thousands of incrementally different prototypes, iterating his way to one that had all of the functionality that he needed. Was each attempt a failure? Or was each attempt a step towards his desired outcome, a quantum of success? Setting up actions as experiments means that getting a result at all is the goal. Generate-and-test is a legitimate strategy.

Related, which parent hasn't allowed an action to proceed, confident that it will not work, because a bruised knee or a burnt finger or a low mark in the test appears to be the way that the offspring wants to learn a particular lesson. Advice could have taught it, but a painful experience can help too. How should we view this kind of event? From the child's perspective, in the moment, it's a painful failure. From the parent's perspective, in the moment, it's vindication, success. Who is right? For how long?

Most parents would try to set up those kinds of outcomes in a safe way. Assuming that learning from failure does have high value, I wonder whether it is diminished by happening in a safe environment? Might the scale of learning increase with jeopardy? But some failure could be terminal: should we learn to walk the tightrope by crossing Niagara Falls?

As a manager I've used the safe space tactic although I try to be open and explicit about it. Perhaps I'll set it up as "I would be delighted if you showed me I was wrong" or "If this works for you, then I'll have learned something too." I think of this as a way of making the effort into an experiment.

Some jobs are apparently set up for failure: a salesperson might expect to sell once in 100 opportunities. Is that 99 failures per success? I've heard it cast in this way: each rejection means one less before the sale. There is no fear of failure with this philosophy and, while those of us who cringe at the idea of working in sales might find it hard to believe, that kind of approach can be learned.

I wonder how to control the learning that comes from failure. It's well-known that machine learning approaches which rely on being "taught" by trying, failing, and being corrected  can pick up on unexpected aspects of their training material. Is there an analogue for human learning? Ben listed a bunch of authors in his talk, people who'd tried, tried, and tried again to be published despite numerous rejections. What was their learning? To be resilient? To sell themselves well? To find their tribe? To get better at writing?

Could it be that some of those people learned nothing through failure to convince an editor that they had a story worth telling? Could they, for example, simply be already resilient folk with large egos that needed satisfying? What about survivorship bias? Where are all the people who failed as many times but didn't ultimately get published? What was their learning? Was it greater than those who were published? How is that even measured?

My goal in this post was to spend a limited time to work through the right-now thoughts that were spurred by Ben's talk. I think I achieved that. Having seen them, you might decide that all I have is shallow, half-cooked, or just plain nonsense. If so, have I failed? If you liked the notes, have I succeeded? Could either outcome make me a success or a failure? To who? On what basis?

Comments

Post a Comment

Popular posts from this blog

The Ideal Test Plan

A colleague pinged me the other day, asking about an "ideal test plan" and wondering whether I could suggest something. Not without a bit more information, I said. OK, they said. Who needs the plan, for what purpose? I asked. Their response: it's for internal use, to improve documentation, and provide a standard structure. We work in a medical context and have strict compliance requirements, so I wondered aloud whether the plan is needed for audit, or to show to customers? It's not, they replied, it's just for the team. Smiling now, I stopped asking questions and delivered the good news that I had what they were looking for. Yes? they asked, in anticipation. Naturally I paused for dramatic effect and to enhance the appearance of deep wisdom, before saying: the ideal plan is one that works for you. Which is great and all that, but not heavy on practical advice. --00-- I am currently running a project at the Association for Software Testing and there is a plan for

Notes on Testing Notes

Ben Dowen pinged me and others on Twitter last week , asking for "a nice concise resource to link to for a blog post - about taking good Testing notes." I didn't have one so I thought I'd write a few words on how I'm doing it at the moment for my work at Ada Health, alongside Ben. You may have read previously that I use a script to upload Markdown-based text files to Confluence . Here's the template that I start from: # Date + Title # Mission # Summary WIP! # Notes Then I fill out what I plan to do. The Mission can be as high or low level as I want it to be. Sometimes, if deeper context might be valuable I'll add a Background subsection to it. I don't fill in the Summary section until the end. It's a high-level overview of what I did, what I found, risks identified, value provided, and so on. Between the Mission and Summary I hope that a reader can see what I initially intended and what actually

69.3%, OK?

The Association for Software Testing is crowd-sourcing a book, Navigating the World as a Context-Driven Tester , which aims to provide responses to common questions and statements about testing from a context-driven perspective . It's being edited by Lee Hawkins who is posing questions on Twitter ,  LinkedIn ,  Slack , and the AST mailing list and then collating the replies, focusing on practice over theory. I've decided to contribute by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "What percentage of our test cases are automated?" There's a lot wrapped up in that question, particularly when it's a metric for monitoring the state of testing. It's not the first time I've been asked either. In my

Why Do They Test Software?

My friend Rachel Kibler asked me the other day "do you have a blog post about why we test software?" and I was surprised to find that, despite having touched on the topic many times, I haven't. So then I thought I'd write one. And then I thought it might be fun to crowdsource so I asked in the Association for Software Testing member's Slack, on LinkedIn , and on Twitter for reasons, one sentence each. And it was fun!  Here are the varied answers, a couple lightly edited, with thanks to everyone who contributed. Edit: I did a bit of analysis of the responses in Reasons to be Cheerful, Part 2 . --00-- Software is complicated, and the people that use it are even worse. — Andy Hird Because there is what software does, what people say it does, and what other people want it to do, and those are often not the same. — Andy Hird Because someone asked/told us to — Lee Hawkins To learn, and identify risks — Louise Perold sometimes: reducing the risk of harming people —

Testing is Knowledge Work

  The Association for Software Testing is crowd-sourcing a book, Navigating the World as a Context-Driven Tester , which aims to provide responses to common questions and statements about testing from a context-driven perspective . It's being edited by Lee Hawkins who is posing questions on Twitter ,  LinkedIn ,  Slack , and the AST mailing list and then collating the replies, focusing on practice over theory. I've decided to contribute by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "We need some productivity metrics from testers" OK. I'd like to help you meet your need if I can but to do that I'll need to ask a few questions. Let's start with these: Who needs the metrics? Is there a particular pr

My Favourite Tool

Last week I did a presentation to a software testing course at EC Utbildning in Sweden titled Exploring with Automation where I demoed ways in which I use software tools to help me to test. Following up later, one of the students asked whether I had a favourite tool. A favourite tool? Wow, so simple but sooo deep!  Asking for a favourite tool could make a great interview question, to understand the breadth and depth of a candidate's knowledge about tools, how they think about an apparently basic request with deep complexity beneath (favourite for what task, on what basis, in what contexts, over what timescale?  what is a tool anyway?) and how they formulate a response to take all of that into account. I could truthfully but unhelpfully answer this question with a curt Yes or No. Or I could try and give something more nuanced. I went for the latter. At an extremely meta level I would echo Jerry Weinberg in Perfect Software : The number one te

Trying to be CEWT

I attend, enjoy, hopefully contribute to, and get a lot from, the local tester meetups and Lean Coffee  in Cambridge. But I'd had the thought kicking around for a long time that I'd like to try a peer workshop inspired by MEWT , DEWT , LEWT and the like. I finally asked a few others, including the local meetup organisers, and got mostly positive noises, so I decided to give it a go. I wrote a short statement to frame the idea, based on LEWT's: CEWT ( Cambirdge Exploratory Workshop on Testing ) is an exploratory peer workshop. We take the view that discussions are more interesting than lectures. We enjoy diverse ideas, and limit some activities in order to work with more ideas. and proposed a mission for an initial attempt to validate it locally on a small scale. Other local testers helped to refine the details in usual the testing ways - you know: criticism, questions, thought experiments, challenges, comparisons, mockery and the rest - and a list of potential at

Fail Here or Fail There

The First Law of Systems-Survival, according to John Gall, is this: A SYSTEM THAT IGNORES FEEDBACK HAS ALREADY BEGUN THE PROCESS OF TERMINAL INSTABILITY Laws are all-caps in Systemantics . Not just laws, but also theorems, axioms, and corollaries. There are many of them so here's another (location 2393-2394): JUST CALLING IT “FEEDBACK” DOESN’T MEAN THAT IT HAS ACTUALLY FED BACK There was a point when I realised, as the capitalised aphorisms rolled by, that I was sinking into the warm and sweetly-scented comforting foamy bathwater of confirmatory bias. Seen, seen, seen! Tick, tick, tick! I took the opportunity to let myself know that I'd been caught in the act, and that I needed to get out of the tub and start to challenge the content.  Intervening at that moment was congruent: I was in a context where I would accept it and prepared to change because of it. Of course, I enjoyed the deep irony of nodding along with Gall when he talked about

Testing and Words

  The other day I got tagged on a Twitter thread started by Wicked Witch of the Test about people with a background in linguistics who’ve ended up in testing. That prompted me to think about the language concepts I've found valuable in my day job, then I started listing them, and then realised how many of them I've mentioned here over the years .   This post is one of an occasional series collecting some of those thoughts.  --00-- In The Complete Plain Words , Ernest Gowers notes, acidly, that: What appears to be a sloppy or meaningless use of words may well be a completely correct use of words to express sloppy or meaningless ideas. It surely sounds trite to say it but our choice of words can make a significant difference to how well our message is understood, and how we are judged. We choose from amongst those words we know, our lexicons . The more my lexicon agrees with yours, the greater our chance of us achieving a shared understanding when we converse. But lexic

Use the Force Multiplier

On Fridays I pair with doctors from Ada 's medical quality team. It's a fun and productive collaboration where I gain deeper insight into the way that diagnostic information is encoded in our product and they get to see a testing perspective unhindered by domain knowledge. We meet at the same time each week and decide late on our focus, choosing something that one of us is working on that's in a state where it can be shared. This week we picked up a task that I'd been hoping to get to for a while: exploring an API which takes a list of symptoms and returns a list of potential medical conditions that are consistent with those symptoms.  I was interested to know whether I could find small input differences that led to large output differences. Without domain knowledge, though, I wasn't really sure what "small" and "large" might mean. I prepared an input payload and wrote a simple shell script which did the following: make a