Lorin Hochstein

Ramblings about software, research and other things

Assumption of rationality

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Matthew Reed wrote a post about Lisa Servon’s book “The Unbanking of America”. This comment stood out for me (emphasis mine):

By treating her various sources as intelligent people responding rationally to their circumstances, rather than as helpless victims of evil predators, [Servon] was able to stitch together a pretty good argument for why people make the choices they make.

In its approach, it reminded me a little of Tressie McMillan Cottom’s “Lower Ed” or Matthew Desmond’s “Evicted.”  In their different ways, each book addresses a policy question that is usually framed in terms of smart, crafty, evil people taking advantage of clueless, ignorant, poor people, and blows up the assumption.  In no case are predators let off the hook, but the “prey” are actually (mostly) capable and intelligent people doing the best they can.  Understanding why this is the best they can do, and what would give them better options, leads to a very different set of prescriptions.

 

Sidney Dekker calls this perspective the local rationality principle. It assumes that people make decisions that are reasonable given the constraints that they are working within, even though from the outside those decisions appear misguided.

I find this assumption of rationality to be a useful frame for explaining individual behavior. It’s worth putting in the effort to identify why a particular decision would have seemed rational within the context in which it was made.

Written by Lorin

June 28, 2017 at 1:55 am

Posted in systems

A conjecture on why reliable systems fail

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Even highly reliable systems go down occasionally. After having read over the details of several incidents, I’ve started to notice a pattern, which has led me to the following conjecture:

Once a system reaches a certain level of reliability, most major incidents will involve:

  • A manual intervention that was intended to mitigate a minor incident, or
  • Unexpected behavior of a subsystem whose primary purpose was to improve reliability

Here are three examples from Amazon’s post-mortem write-ups of major AWS outages:

The S3 outage on February 28, 2017 involved a manual intervention to debug an issue that was causing the S3 billing system to progress more slowly than expected.

The DynamoDB outage on September 20, 2015 (which also affected SQS, auto scaling, and CloudWatch) involved healthy storage servers taking themselves out of service by executing a distributed protocol that was (presumably) designed that way for fault tolerance.

The EBS outage on October 22, 2012 (which also affected EC2, RDS, and ELBs) involved a memory leak bug in an agent that monitors the health of EBS servers.

Written by Lorin

June 24, 2017 at 4:45 pm

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Engineering research reveals wrongdoing

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The New York Times has a story today, Inside VW’s Campaign of Trickery, about how Volkswagon conspired to hide their excessive diesel emissions from California regulators.

What was fascinating to me was that the emission violations were discovered by mechanical engineering researchers at West Virginia University, Dan Carder, Hemanth Kappanna, and Marc Besch (Kappanna and Besch were graduate students at the time).

The presence of high levels of lead in Flint, Michigan drinking water was also discovered by an engineering researcher: Marc Edwards, a civil engineering professor at Virginia Tech.

It’s a reminder that regulators alone aren’t sufficient to ensure safety, and that academic engineering research can have a real impact on society.

Written by Lorin

May 7, 2017 at 1:56 pm

Posted in research, Uncategorized

Sketching on the back end

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In his essay entitled, The Cognitive View: A Different Look at Software Design, Robert Glass made the following observation about how software engineers do design work:

What we see here is a mental process, a very rapid process, an iterative process, a process in fact of fast trial and error. The mind forms a solution to the problem, knowing that it will be inadequate because the mind is not yet able to fully grasp all the facets of the problem. That problem solution, the mind knows, must be in the form of a model, because it is going to be necessary to try sample input against the model, run a quick simulation (inside the mind) on the model using the sample input, and get sample outputs (still inside the mind) from that simulation.

The essence of design, then, is rapid modeling and simulation. And a key factor in design is the ability to propose solutions and allow them to fail!

Design work incorporates trial and error. We think up a potential solution, and then try to evaluate whether it’s a good fit for the problem.

But not all potential solutions are so easy to evaluate entirely inside one’s head. In fields where designers are working on physical designs or visual interfaces, they use sketches to help with early solution evaluation, sometimes called formative evaluation. Sketching user experiences is a great book on this topic.

For those of us working on the back-end, we don’t think of this early evaluation of solutions as involving “sketching”, but the metaphor fits. One of the things I find appealing about the Alloy modeling language is that it allows you to sketch out different data models and check their properties.

In a recent blog post, Michael Fogus describes working with clojure.spec as a form of sketching. He also makes the following observation about JSON.

While Lisp programmers have long know the utility of sketching their structs, I’m convinced that one of the primary reasons that JSON has taken over the world is that it provides JS-direct syntactic literals that can be easy typed up and manipulated with little fuss. JSON is a perfectly adequate tool for sketching for many programmers.

I believe UML failed because the designers did not understand the role that box-and-arrow diagrams played in software engineering design. They wanted to build a visual modeling language with well-defined semantics: we wanted to sketch.

Written by Lorin

February 20, 2017 at 2:54 am

Posted in software

I’d rather read code than tests

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But the benefits [of test-driven development] go beyond that. If you want to know how to call a certain API, there is a test that does it. If you want to know how to create a certain object, there is a test that does it. Anything you want to know about the existing system, there is a test that demonstrates it. The tests are like little design documents, little coding examples, that describe how the system works and how to use it.

Have you ever integrated a third party library into your project? You got a big manual full of nice documentation. At the end there was a thin appendix of examples. Which of the two did you read? The examples of course! That’s what the unit tests are! They are the most useful part of the documentation. They are the living examples of how to use the code. They are design documents that are hideously detailed, utterly unambiguous, so formal that they execute, and they cannot get out of sync with the production code.

TheThreeRulesOfTdd, “Uncle” Bob Martin

I sat down today with a co-worker today to discuss a pull request I had submitted earlier in the day.

“You read it already?” I asked.

“Yeah”, he replied, to which he amended “…well, I didn’t read your tests.”

I wasn’t really surprised at that, since (a) it’s the non-test code that I’m worried about, and (b) I often don’t read tests in pull requests either.

Still, I paused for a moment to discuss this with him, because I remembered Uncle Bob’s paean to unit tests as documentation for code. My colleague admitted that he didn’t read tests because reading the tests required more effort than reading the code.

On further reflection, I realized that I, too, never look at tests when I’m trying to understand an unfamiliar codebase. I just read the code directly.

What’s going on here? My theory is: unit tests are too noisy to be useful for understanding.

For APIs, yes, examples are wonderful: if I need to write code that will call into somebody else’s API, I can never get enough examples. But for most of the code that I read, I’m not trying to understand how to call into it like a library, I’m trying to understand its role in the context of a larger system.

For the code I typically work with, the unit tests require complex setup and assertion sections. This increases the cognitive load on the reader: why expend the extra effort to understand the test when I can read the source directly?

There are techniques for making tests more readable: factor your tests well, use a single assertion per test, use tools to improve readability like Rspec and Hamcrest. Still,  it always feels like it isn’t worth the effort to try and understand the tests. After all, I can always just read the code.

 

Written by Lorin

January 26, 2017 at 1:51 am

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How open source beat agile

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How does code land in your master branch? Do your team members commit directly to master, or do you merge in pull requests?

In 2008, the repository hosting company known as GitHub was founded. GitHub quickly became the dominant player in the world of open source project hosting. GitHub’s pull request model of contributing to a project became so popular that pull request is now a generic expression.

Two years after GitHub was founded, Jez Humble and David Farley published Continuous Delivery, a book about automated deployment. One of the central ideas of continuous delivery is that all team members should commit directly and frequently to the mainline branch in the version control repository (master in Git, “trunk” in Subversion, Git’s predecessor). According to proponents of continuous delivery, working in long-lived feature branches that are occasionally merged to mainline should be avoided.

Continuous Delivery was an enormously influential book. As an example of its influence, Netflix’s deployment system, Spinnaker, bills itself as a “continuous delivery platform”. And yet, I’ve never worked on a project where the developers committed directly to mainline. The “review and merge pull request” model has dominated my professional career. The problem is that it isn’t clear how to integrate the continuous delivery approach with code review[1]. I’d wager that the majority of developers who support the idea of continuous delivery are also merging feature branches via pull request rather than committing directly to mainline.

While we pay lip service to doing continuous delivery, we’ve made the choice to go the pull request route. What’s going on here?

I think this illustrates a larger tension between the open source way of doing software development and the agile way of doing software development, where we end up speaking the language of agile but adopting the processes of open source. To understand why agile and open source would be in tension with one another, it’s instructive to examine where the agile movement came from.

Agile was born out of the world of software consulting; the substring “consult” has twelve matches on the Authors page of the Agile Manifesto. The kinds of software projects that consultants work on tend to have three properties: they are co-located, synchronous and have a well-defined customer. A good illustration of this is Extreme Programming (XP), the ur-agile process, which requires co-located synchronous development for pair programming, and having a well-defined customer or doing release planning.

By contrast, open source software projects are distributed, asynchronous, and don’t have well-defined customers. It’s natural, then, that open source processes would be different than agile ones. The continuous delivery approach of committing to mainline doesn’t make sense in an open source project, since most contributors don’t have permission to commit to mainline.

Most of the projects I’ve worked on have looked more like the agile context than the open source project, but we still do things the open source way.

When we talk about agile concepts such as continuous delivery, pair programming, and test-driven development, we refer to them as “processes”, but they’re really skills. You can’t just pick up a skill and be proficient immediately.  One way to improve a skill is the apprentice model: to observe how an expert applies that skill in context.

In open source, the entire process is visible, by definition. We can observe how projects do code reviews, integration tests, new feature planning, and so on. In the world where agile came from, we can’t. We have access to books and talks by consultants, but we don’t get to see how they applied their skills to solve specific difficulties they encountered with agile.

Consider Gherkin-based specifications. Gherkin is popular in agile, but it isn’t used in open source projects, because open source projects don’t use specifications. Without being able to observe how experts write Gherkin-based specs, it’s difficult for a novice to assess whether the approach is worth pursuing[2]. The only time I’ve ever seen Gherkin on projects is when I’ve written it myself.

Because open source succeeds in making work visible where agile fails, developers have more opportunities to become better open source developers than agile developers.


  1. Yes, yes, if you do pair programming, then you are doing a kind of continuous code review that is compatible with continuous delivery. But not all teams want to do pair programming, and in that case you still only have a single reviewer.  ↩
  2. The biggest disappointment in the otherwise great book Specification by Example by Gojko Adzic is the lack of examples!  ↩

Written by Lorin

January 21, 2017 at 7:42 pm

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Changing a complex system is hard

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I’ve been reading Drift into Failure by Sidney Dekker, and it’s a fantastic book about applying systems thinking to understand how complex systems fail. One of the things that a systems thinking perspective teaches you is that we don’t know how a complex system will respond to a change in inputs. In particular, when you try an intervention that is intended to improve the system, it might make things worse.

There’s a great example of this phenomenon in a recent paper by Alpert et al. entitled Supply-Side Drug Policy in the Presence of Substitutes: Evidence from the Introduction of Abuse-Deterrent Opioids, that was mentioned on a recent episode of Vox’s The Weeds podcast.

The paper examined the impact of introducing an abuse-deterrent version of OxyContin on drug abuse. The FDA approved OxyContin pills that were more difficult to crush or dissolve. These pills were expected to act as a deterrent to abuse since addicts tend to consume the drug by chewing, snorting, or injecting it to increase its impact. The authors examined the rates of OxyContin abuse before and after these new abuse-deterrent drugs were introduced in different states.

What the authors found was that this intervention did have the effect of decreasing OxyContin abuse. Unfortunately, it also increased heroin-related deaths. The unexpected effect was that addicts  substituted one form of opiate for a more dangerous one.

The only way for us to know that our interventions have the desired effect on a complex system is to try and measure that effect.

Written by Lorin

January 18, 2017 at 1:21 am

Posted in systems