Older blog entries for oubiwann (starting at number 311)

27 Jul 2014 (updated 28 Jul 2014 at 06:16 UTC) »

The Future of Programming - An Overview

Art by Philip Straub
There's a new series of blog posts coming, inspired by on-going conversations with peers, continuous inspection of the development landscape, habitual navel-gazing, and participation at the catalytic OSCON 2014. As you might have inferred, these will be on the topic of "The Future of Programming."

Not to be confused with Bret Victor's excellent talk last year at DBX, these posts will be less about individual technologies or developer user experience, and more about historic trends and viewing the present (and near future) through such a lense.

In this mini-series, the aim is to present posts on following topics:

I did a similar set of posts, conceived in late 2008 and published in 2009 on the future of cloud computing entitled After the Cloud. It was a very successful series and the cloud industry seems to be heading towards some of the predictions made in it -- ZeroVM and Docker are an incremental step towards the future of distributed processes/functions outlined in To Atomic Computation and Beyond

In that post, though, are two quotes from industry greats. These provide an excellent context for this series as well, hinting at an overriding theme:
  • Alan Kay, 1998: A crucial key to growing large systems is effective communications between components.
  • Joe Armstrong, 2004: To effectively model and solve problems in a distributed manner, we need concurrency... this is made easier when we isolate processes and do not share data.

In the decade since these statements were made, we have seen individuals, projects, and companies take that vision to heart -- and succeeding as a result. But as an industry, we continue to struggle with the definition of our art; we still are tormented by change -- both from within and externally -- and do not seem to adapt to it well.

These posts will peer into such places ... in the hope that such inspection might guide us better through the tangled forest of our present into the unimagined forest of our future.

Syndicated 2014-07-27 18:02:00 (Updated 2014-07-28 05:17:59) from Duncan McGreggor

3 Jul 2014 (updated 18 Jul 2014 at 06:16 UTC) »

Uncovering Inherent Structures in Organizations

Vladimir Levenshtein
This post should have a subtitle: "Using Team Analysis and Levenshtein Distance to Reveal said Structure." It's the first part of that subtitle that is the secret, though: being able to correctly analyze and classify individual teams. Without that, using something clever like Levenshtein distance isn't going to be very much help.

But that's coming in towards the end of the story. Let's start at the beginning.

What You're Going to See

This post is a bit long. Here are the sections I've divided it into:

  • What You're Going to See
  • Premise
  • Introducing ACME
  • Categorizing Teams
  • Category Example
  • Calculating the Levenshtein Distance of Teams
  • Sorting and Interpretation
  • Conclusion

However, you don't need to read the whole thing to obtain the main benefits. You can get the Cliff Notes version by reading the Premise, Categorizing Teams, Interpretation, and the Conclusion.

Premise

Companies grow. Teams expand. If you're well-placed in your industry and providing in-demand services or products, this is happening to you. Individuals and small teams tend to deal with this sort of change pretty well. At an organizational level, however, this sort of change tends to have an impact that can bring a group down, or rocket it up to the next level.

Of the many issues faced by growing companies (or rapidly growing orgs within large companies), the structuring one can be most problematic: "Our old structures, though comfortable, won't scale well with all these new teams and all the new hires joining our existing teams. How do we reorganize? Where do we put folks? Are there natural lines along which we can provide better management (and vision!) structure?"

The answer, of course, is "yes" -- but! It requires careful analysis and a deep understanding every team in your org.

The remainder of this post will set up a scenario and then figure out how to do a re-org. I use a software engineering org as an example, but that's just because I have a long and intimate knowledge of them and understand the ways in which one can classify such teams. These same methods could be applied a Sales group, Marketing groups, etc., as long as you know the characteristics that define the teams of which these orgs are comprised.



Introducing ACME

ACME Corporation is the leading producer of some of the most innovative products of the 20th century. The CTO had previously tasked you, the VP of Software Development to bring this product line into the digital age -- and you did! Your great ideas for the updated suite are the new hottness that everyone is clamouring for. Subsequently, the growth of your teams has been fast, and dare we say, exponential.

More details on the scenario: your Software Development Group has several teams of engineers, all working on different products or services, each of which supports ACME Corporation in different ways. In the past 2 years, you've built up your org by an order of magnitude in size. You've started promoting and hiring more managers and directors to help organize these teams into sensible encapsulating structures. These larger groups, once identified, would comprise the whole Development Group.

Ideally, the new groups would represent some aspect of the company, software development, engineering, and product vision -- in other words, some sensible clustering of teams doing related work. How would you group the teams in the most natural way?

Simply dividing along language or platform lines may seem like the obvious answer, but is it the best choice? There are some questions that can help guide you in figuring this out:
  • How do these teams interact with other parts of the company? 
  • Who are the stakeholders in feature development? 
  • Which sorts of customers does each team primarily serve?
There are many more questions you could ask (some are implicit in the analysis data linked below), but this should give a taste.

ACME Software Development has grown the following teams, some of which focus on products, some on infrastructure, some on services, etc.:
  • Digital Anvil Product Team
  • Giant Rubber Band App Team
  • Digital Iron Carrot Team
  • Jet Propelled Unicycle Service Team
  • Jet Propelled Pogo Stick Service Team
  • Ultimatum Dispatcher API Team
  • Virtual Rocket Powered Roller Skates Team
  • Operations (release management, deployments, production maintenance)
  • QA (testing infrastructure, CI/CD)
  • Community Team (documentation, examples, community engagement, meetups, etc.)

Early SW Dev team hacking the ENIAC

Categorizing Teams

Each of those teams started with 2-4 devs hacking on small skunkworks projects. They've now blossomed to the extent that each team has significant sub-teams working on new features and prototyping for the product they support. These large teams now need to be characterized using a method that will allow them to be easily compared. We need the ability to see how closely related one team is to another, across many different variables. (In the scheme outlined below, we end up examining 50 bits of information for each team.)

Keep in mind that each category should be chosen such that it would make sense for teams categorized similarly to be grouped together. A counter example might be "Team Size"; you don't necessarily want all large teams together in one group, and all small teams in a different group. As such, "Team Size" is probably a poor category choice.

Here are the categories which we will use for the ACME Software Development Group:
  • Language
  • Syntax
  • Platform
  • Implementation Focus
  • Supported OS
  • Deployment Type
  • Product?
  • Service?
  • License Type
  • Industry Segment
  • Stakeholders
  • Customer Type
  • Corporate Priority
Each category may be either single-valued or multi-valued. For instance, the categories ending in question marks will be booleans. In contrast, multiple languages might be used by the same team, so the "Language" category will sometimes have several entries.

Category Example

(Things are going to get a bit more technical at this point; for those who care more about the outcomes than the methods used, feel free to skip to the section at the end: Sorting and Interpretation.)

In all cases, we will encode these values as binary digits -- this allows us to very easily compare teams using Levenshtein distance, since the total of all characteristics we are filtering on can be represented as a string value. An example should illustrate this well.

(The Levenshtein distance between two words is the minimum number of single-character edits -- such as insertions, deletions or substitutions -- required to change one word into the other. It is named after Vladimir Levenshtein, who defined this "distance" in 1965 when exploring the possibility of correcting deletions, insertions, and reversals in binary codes.)

Let's say the Software Development Group supports the following languages, with each one assigned a binary value:
  • LFE - #b0000000001
  • Erlang - #b0000000010
  • Elixir - #b0000000100
  • Ruby - #b0000001000
  • Python - #b0000010000
  • Hy - #b0000100000
  • Clojure - #b0001000000
  • Java - #b0010000000
  • JavaScript - #b0100000000
  • CoffeeScript - #b1000000000
A team that used LFE, Hy, and Clojure would obtain its "Language" category value by XOR'ing the three supported languages, and would thus be #b0001100001. In LFE, that could be done by entering the following code the REPL:

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