Category: software engineering
A devops lesson from Michael Connelly’s Black Echo
Michael Connelly offers up a cautionary devops tale of what can happen when your alerts are too sensitive or generate too much noise:
â€œThe vaultâ€™s sensor alarm had repeatedly been going off all week. [The thieves], with their digging and their drills, must have been tripping the alarms. Four straight nights the cops are called out along with the manager. Sometimes three times in one night. They donâ€™t find anything and begin to think itâ€™s the alarm. The sound-and-movement sensor is off balance. So the manager calls the alarm company and they canâ€™t get anybody out until after the holiday weekend, you know, Labor Day. So this guy, the managerâ€”â€
â€œTurns the alarm off.â€ Bosch finished for her.
â€œYou got it. He decides he isnâ€™t going to get called out each night during the weekend. Heâ€™s supposed to go down to the Springs to his time-share condo and play golf. He turns the alarms off. Of course, he no longer works for WestLand National.â€
From The Black Echo – the first book in Connelly’s brilliant Harry Bosch series.
The Wisdom of the Crowd
From Wired magazine, I came across this fascinating online experiment, where Stanford researcher Erik Steiner is soliciting guesses from the Internet about how many coins are in the pictured coin jar. You can participate and submit your own guess before December 8th here.
I’ll be curious to see how this experiment pans out. His early update on the findings is interesting:
First, thanks for your participation. Second, some early returns…
So far, it turns out that the most accurate guessers are the people who spend the least amount of time thinking about it. Somewhat surprisingly, those that answered “I actually did some math” are the least accurate, on average.
At the risk of exposing my own confirmation bias, I’m not that surprised by the early findings as I suspect it is intuition â€“ gut feel or what Kahneman calls System 1 thinking â€“ at work. System 2 probably fails because there isn’t enough information to analytically come up with a solution.
My interest in the wisdom of the crowd is not just one of pop-science fascination, but I’ve always wondered about its applicability in forecasting large software projects. In a way, the agile world adopts crowd-sourced estimates with techniques like sprint poker and story point estimation. However, those are typically analytical exercises (System 2) and finer grained i.e., at the story level. Story point estimates can of course then be aggregated to come up with an estimate for the entire project. But, for very large projects â€“ think Obamacare or larger â€“ getting a backlog with enough detail and estimating each story can itself be a significant undertaking. And that is where I would be curious to look at research around crowd sourcing estimates for large software projects.
This is how I picture the experiment being structured: Engineers, product managers and program managers in an organization are provided with the project description and a way to anonymously provide a guesstimate. May be, they are even instructed not to discuss the project amongst themselves before providing an estimate so as to not bias their individual estimates. Perhaps, a control question to reveal their biases  would also be in order. This would not work in small organizations as you wouldn’t have enough of a “crowd” to crowd-size. The aggregated estimate (mean, geometric mean?) would then have to be compared against the traditionally calculated estimate or tracked against actual project completion.
Even if unsuccessful, these experiments could have interesting results â€“ do engineers tend to be more accurate or inaccurate compared to program managers, do experienced engineers tend to do better or worse than less experienced engineers at forecasting. Software estimation is notoriously hard and error prone and if successful, a crowd-sourced estimate could provide another useful data point to aid long term planning.
- The wisdom of the crowd has been proven to break down in the face of shared biases and social influence, which makes it particularly tricky to apply it to an organization where everyone typically shares the same biases. Kahneman talks about this some more in this interview. â†©
- Another study shows that the bias can be eliminated by identifying the independent thinkers or the not-so-easily influenced and aggregating their estimates. They even propose a way to identify the independent thinkers. â†©
- There are contradictory studies on the validity of the wisdom of the crowd as this post discusses a study where the geometric mean was used to massage away the wildly inaccurate guesses of the crowd. â†©
Juking The Stats
If youâ€™re a fan of the HBO show The Wire, â€œjuking the statsâ€ would be a familiar concept. In the show, Baltimore city cops â€“ under pressure from management to improve crime numbers â€“ resort to short term tactics that get better numbers but donâ€™t necessarily reduce crime. Reclassifying crimes to lower categories, increasing the arrest rate by arresting for minor offenses, under reporting crimes are all part of the play book. And as Pryzbylewski â€“ a former cop who becomes a teacher â€“ later finds out, the same story repeats itself in the city schools. Under pressure from the state, to improve standardized test scores teachers focus on teaching for the tests rather than actually educating their students.
Juking the stats is however, not just a great sound bite on a TV show. It is an all too real issue that plagues organizations â€“ public and private sector alike. Performance measurements introduce perverse incentives and it is human nature, when measured, to optimize for the metric against which they are being judged.
The world of software engineering is no stranger to this problem. Software engineering and its management is a complex beast and relative to other engineering disciplines is still in its infancy. We are still figuring out effective ways to track and measure performance. Most methods are far from perfect and suffer from unintended consequences.
In some agile organizations â€“ especially those that are new to agile â€“ measuring team performance by their sprint velocity has become common practice. Far too often, this leads to teams â€“ under pressure to deliver the committed story points in that sprint â€“ unintentionally cutting corners on critical aspects like quality and testing only to pay the price later.
Large engineering programs require teams to report status on a weekly basis, typically as red, yellow or green or some variation thereof. The stigma attached with reporting oneâ€™s status as red can lead to teams suppressing problems. Being honest about these issues ahead of time could have fixed those issues, but the pressure to not report red, means these issues remain buried until itâ€™s too late.
In less mature organizations, QA teams are sometimes incentivized by the number and priority of bugs that they open. This invariably leads to bug priority inflation and battles with the development teams. Low team morale is an inevitable side effect.
Then, there is the possibly apocryphal tale of IBM incentivizing programmers by lines of code only to result in programmers intentionally writing verbose code.
In all of these cases, you see teams when pressured by poorly designed incentives and metrics, lose sight of the long term goals and focus on the short term statisticsÂ â€“ sometimes overtly, but usually inadvertently. Qualitative attributes like software quality, good design and resilience end up taking a back seat. Measuring and tracking performance is a good thing and is essential for continuous improvement. However, itâ€™s just as important to be aware of the possibility that more often than not, unintended consequences may rear its ugly head. When it does, it is imperative that leaders react and be prepared to either fix the metric or dump them entirely.
â€œDon’t matter how many times you get burnt, you just keep doin’ the same.â€ â€“ Bodie
- If you’re not, you should be. Apart from having a great storyline and an excellent cast of characters, it is rich with lessons in economics, management and human behavior. â†©
- Crime Report Manipulation Is Common Among New York Police, Study Finds – NYT â†©
- Criticism for standardized testing as a measure for driving school funding as collected by Wikipedia â†©
- There are a number of examples of the unintended consequences of perverse incentives at play. One of the more interesting examples from Bill Bryson’s A Short History of Everything is the story of paleontologists paying the locals for each fossil fragment they turn in. The paleontologists later find that the locals were smashing larger bone fragments into smaller pieces to maximize gain and in the process rendering the fossils worthless. The authors of Freakonomics collect a few more examples in this NYT article. â†©
- Joshua Kerievsky makes an interesting case for doing away with story points. â†©
- albeit imagined â†©
- Epigraph from “Time after Time”, season 3, episode 1 of The Wire â†©