Monthly Archives: January 2020


One of the joys of reading is the books you come across by accident. Reading a couple of Tim Wu’s excellent books (viz. “The Attention Merchants” and “The Master Switch”) led me to books on solitude, including “Solitude: In Pursuit of a Singular Life in a Crowded World” by Michael Harris.

It seemed timely to read on this topic, as I’ve been implementing a “digital declutter” after recently reading Digital Minimalism by Cal Newport. I’m fortunate to live in a beautiful and peaceful location so I’m being much more mindful of making the most of the spot to deliberate separate myself from technology sometimes and take in the simple pleasures of time spent watching the ocean and listening to the birds.

The inspiration for writing “Solitude” came from the author reading about Dr Edith Bone. Hers is a remarkable story (and worth reading about in itself) of seven years spent in solitary confinement.

A little reading – and a hero in Dr. Bone – had turned malaise into a mission. I wanted to become acquainted again with the still night, with my own hapless daydreaming, with the bare self I had (for how long?) been running from. I kept asking myself: why I am so afraid of my own quiet company? This book is the closest I’ve come to an answer.

Aligning closely with Wu’s work, Harris discusses the rise of social media and the “connectedness” it was designed to create. But we all know by now that the “likes” and sharing are highly addictive, triggering small but frequent dopamine hits. This has had a devastating impact on our ability to find solitude:

We’re given opportunities to practise being alone every day, almost every hour. Go on a drive. Sit on a lawn. Stick your phone in a drawer. Once we start looking, we find solitude is always just below the surface of things. I thought at first that solitude was a lost art. Now I know that’s too pretty a term, too soft a metaphor.

Solitude has become a resource.

Like all resources, it can be harvested and hoarded, taken up by powerful forces without permission or inquiry, and then transformed into private wealth, until the fields of empty space we once took for granted first dwindle, then disappear.

Harris goes on to ask the question: what is solitude for? He comes up with three answers: the formulation of fresh ideas, self-knowledge, and (paradoxically) bonding with others.

Taken together, these three ingredients build a rich interior life. It turns out that merely escaping crowds was never the point of solitude at all: rather, solitude is a resource – an ecological niche – inside of which these benefits can be reaped. And so it matters enormously when that resource is under attack.

Our modern, hyperconnected, “always on” world sees solitude under constant threat and it takes a determined effort to find it in our lives:

Our online crowds are so insistent, so omnipresent, that we must now actively elbow out the forces that encroach on solitude’s borders, or else forfeit to them a large portion of our mental landscape.

It turns out that some research has already been done around daydreaming. MRI scanning reveals that daydreaming “constitutes an intense and heterogeneous set of brain functions” and:

…this industrious activity plays out while the conscious mind remains utterly unaware of the work – so our thoughts (sometimes really great thoughts) emerge without our anticipation or understanding. They emerge from the blue. Daydreaming thoughts may look like “pointless fantasizing” or “complex planning” or “the generation of creative ideas”. But, whatever their utility, they arrive unbidden.

Einstein believed that “the daydreaming mind’s ability to link things is, in fact, our only path toward fresh ideas.” Harris describes his own attempts to daydream during a three-hour wander and he says of this experience:

I start to see time-devouring apps like Candy Crush as pacifiers for a culture unwilling or unable to experience a finer, adult form of leisure. We believed those who told us that the devil loves idle hands. And so we gave our hands over for safekeeping. We long for constant proof of our effectiveness, our accomplishments. And perhaps it’s this longing for proof, for glittering external validation, that makes our solitude so vulnerable to those who would harvest it.

The addictive nature of social media (see ludic loops) has seen us giving up what few moments of spare time we have:

To a media baron looking for short-term profits, a daydreaming mind must look like an awful waste. All that time and attention left to wander, directionless! Making use of the blank spaces in a person’s life – draining the well of reverie – has become one of the missions of modernity.

But we do need to break out of this cycle and, bizarrely, doing so is seen as an odd and disruptive thing to do (e.g. I see the disbelief every time I mention to someone that I’m not, and never have been, “on Facebook”):

Choosing a mental solitude, then, is a disruptive act, a true sabotage of the schemes of ludic loop engineers and social media barons. Choosing solitude is a gorgeous waste.

Harris then discusses how we’ve all become part of the crowd and true marks of individualism are being eroded as a result:

…today we need to safeguard our inner weirdo, seal it off and protect it from being buffeted. Learn an old torch song that nobody knows; read a musty out-of-print detective novel; photograph a honey-perfect sunset and show it to no-one. We may need to build new and stronger weirdo cocoons, in which to entertain our private selves. Beyond the sharing, the commenting, the constant thumbs-upping, beyond all that distracting gilt, there are stranger things to be loved.

Harris explores the impact that technologies like Google Maps have had on our ability to truly lose ourselves and wander freely in nature, activities that have historically yielded great insights but are much more difficult to achieve in our hyper-connected and increasingly urban lives. He goes on to look at reading and writing – and the socialization of those activities. Proust once defined reading as “that fruitful miracle of a communication in the midst of solitude” but even this is under threat:

But that solitary reading experience is now endangered, and so is the empathy it fosters. Our stories are going social. We can assume that, in thirty years, readers and writers will use platform technologies to constantly interact with and shape each other, for better or worse. Authors will enlist crowd-sourcing and artificial intelligence to help them write their stories.

In his final chapter, Harris tells the story of his seven-day experience of solitude in a cabin in the woods, offline and alone:

Near the end of this lonely week my thoughts stop floating so much and return to the problem of solitude in a digital culture. Only now, out on the meditative trail I’ve been hiking before and after my crackers-and-apple lunch, I’m thinking about it differently, more expansively. Things here call for wide lenses.

From this dirt vantage, all that clicking and sharing and liking and posting looks like a pile of iron shackles. We are the ones creating the content, yet we’re never compensated with anything but the tremulous, fast-evaporating pleasures that social grooming delivers. Validation and self-expression, we are told, are far greater prizes than the measly cash that flows upward to platform owners…. [these] systems we live by can expropriate no value from solitude, and so they abhor it.

I enjoyed reading this book, it’s written in a very approachable style with many personal anecdotes (which you may or may not find interesting in themselves). I took this read as a reminder to make room for “daydreaming”, be that looking out over the ocean or simply not pulling out my phone during a short tram ride. Nicholas Carr says it well in the Foreword of the book:

Solitude is refreshing. It strengthens memory, sharpens awareness, and spurs creativity. It makes us calmer, more attentive, clearer headed. Most important of all, it relieves the pressure of conformity. It gives us the space we need to discover the deepest sources of passion, enjoyment, and fulfillment in our lives. Being alone frees us to be ourselves – and that makes us better company when we rejoin the crowd.

I also recently read another book on the same topic, but given a much more serious treatment by Raymond Kethledge & Mike Erwin, in the shape of “Lead Yourself First: Inspiring Leadership Through Solitude” – I highly recommend this book.

“The Influence of Organizational Structure on Software Quality: An Empirical Case Study” (Microsoft Research and subsequent blogs)

A Microsoft Research paper from back in 2008 has recently been getting a lot of renewed attention after a blog post about it did the rounds on Twitter, Reddit, etc. The paper is titled “The Influence of Organizational Structure on Software Quality: An Empirical Case Study” and it looks at defining metrics to measure organizational complexity and whether those metrics are better at predicting “failure-proneness” of software modules (specifically, those comprising the Windows Vista operating system) than other metrics such as code complexity .

The authors end up defining eight such “organizational metrics”, as follows:

  • Number of engineers – “the absolute number of unique engineers who have touched a binary and are still employed by the company”. The claim here is that higher values for this metric result in lower quality.
  • Number of ex-engineers – similar to the first metric, but defined as “the total number of unique engineers who have touched a binary and have left the company as of the release date of the software system”. Again, higher values for this metric should result in lower quality.
  • Edit frequency – “the total number times the source code, that makes up the binary, was edited”. Again, the claim is that higher values for this metric suggest lower quality.
  • Depth of Master Ownership – “This metric (DMO) determines the level of ownership of the binary depending on the number of edits done. The organization level of the person whose reporting engineers perform more than 75% of the rolled up edits is deemed as the DMO.” Don’t ask me, read the paper for more on this one, but the idea is that the lower the level of ownership, the higher the quality.
  • Percentage of Org contributing to development – “The ratio of the number of people reporting at the DMO level owner relative to the Master owner org size.” Higher values of this metric are claimed to point to higher quality.
  • Level of Organizational Code Ownership – “the percent of edits from the organization that contains the binary owner or if there is no owner then the organization that made the majority of the edits to that binary.” Higher values of this metric are again claimed to point to higher quality.
  • Overall Organization Ownership – “the ratio of the percentage of people at the DMO level making edits to a binary relative to total engineers editing the binary.” Higher values of this metric are claimed to point to higher quality.
  • Organization Intersection Factor – “a measure of the number of different organizations that contribute greater than 10% of edits, as measured at the level of the overall org owners.” Low values of this metric indicate higher quality.

These metrics are then used in a statistical model to predict failure-proneness of the over 3,000 modules comprising the 50m+ lines of source code in Windows Vista. The results apparently indicated that this organizational structure model is better at predicting failure-proneness of a module than any of these more common models: code churn, code complexity, dependencies, code coverage, and pre-release bugs.

I guess this finding is sort of interesting, if not very surprising or indeed helpful.

One startling omission from this paper is what constitutes a “failure”. There are complicated statistical models built from these eight organizational metrics and comparisons made to other models (and really the differences in the predictive power between all of them are not exactly massive), but nowhere does the paper explain what a “failure” is. This seems like a big problem to me. I literally don’t know what they’re counting – which is maybe just a problem for me – but, much more significantly, I don’t know whether what the different models are counting are the same things (which would be a big deal in comparing the outputs from these models against one another).

Now, a lot has changed in our industry since 2008 in terms of the way we build, test and deploy software. In particular, agile ways of working are now commonplace and I imagine this has a significant organizational impact, so these organizational metrics might not offer as much value as they did when this research was undertaken (if indeed they did even then).

But, after reading this paper and the long discussions that have ensued online recently after it came back into the light, I can’t help but ask myself what value we get from becoming better at predicting which modules have “bugs” in them. On this, the paper says:

More generally, it is beneficial to obtain early estimates of software quality (e.g. failure-proneness) to help inform decisions on testing, code inspections, design rework, as well as financial costs associated with a delayed release.

I get the point they’re making here but the information provided by this organizational metric model is not very useful in informing such decisions, compared to, say, a coherent testing story revealed by exploratory testing. Suppose I predict that module X likely has bugs in it, then what? This data point tells me nothing in terms of where to look for issues or whether it’s worth my while to do so based on my mission to my stakeholders.

We spend a lot of time and effort in software development as a whole – and testing specifically – trying to put numbers against things – perhaps as a means of appearing more scientific or accurate. When faced with questions about quality, though, such measurements are problematic and I thank James Bach for his very timely blog post in which he encourages us to assess quality rather than measure it – I suggest that taking the time to read his blog post is time better spent than trying to make sense of over-complicated and meaningless pseudo-science such as that presented in the paper I’ve reviewed here.

(The original 11-page MS Research paper can be found at