Today’s keynote was given by Alan Noble, Engineering Director for Google Australia and long-term adjunct at the University of Adelaide, who was mildly delayed by Sydney traffic but this is hardly surprising. (Sorry, Sydney!) Whn asked to talk about Google’s Software Engineering (SE) processes, Alan thought “Wow, where do I began?” Alan describes Google’s processes as “organic” and “changing over time” but no one label can describe an organisation that has over 30,000 employees.
So what does Alan mean by “organic”? Each team in Google is empowered to use the tools and processes that work best for them – there is no one true way (with some caveats). The process encouraged is “launch and iterate” and “release early, release often”, which many of us have seen in practice! You launch a bit, you iterate a bit, so you’re growing it piece by piece. As Alan noted, you might think that sounds random, so how does it work? There are some very important underlying commonalities. In the context of SE, you have an underlying platform and underlying common principles.
Everything is built on Google Three (Edit: actually it’s google3, from Alan’s comment below so I’ll change that from here on) – Google’s third iteration of their production codebase, which also enforces certain approaches to the codebase. At the heart of google3 is something called a package, which encapsulates a group of source files, and this is associated with a build file. Not exciting, but standard. Open Source projects are often outside: Chrome and Android are not in google3. Coming to grips with google3 takes months, and can be frustrating for new hires, who can spend weeks doing code labs to get a feeling for the codebase. It can take months before an engineer can navigate google3 easily. There are common tools that operate on this, but not that many of them and for a loose definition of “common”. There’s more than one source code control system, for example. (As a note, any third party packages used inside Google have the heck audited out of them for security purposes, unsurprisingly.) The source code system used to be Perforce by itself but it’s a highly centralised server architecture that hasn’t scaled for how Google is now. Google has a lot of employees spread around the world and this presents problems. (As a note, Sydney is the 10th largest engineering centre for Google outside of Mountain View.) In response to this scaling problem, Google have tried working with the vendor (which didn’t pan out) and have now started to produce their own source control system. Currently, the two source control systems co-exist while migration takes place – but there’s no mandated move. Teams will move based on their needs.
Another tool is a tracking tool called Buganizer which does more than track bugs. What’s interesting is that there are tools that Google use internally that we will never see, to go along with their tools that are developed for public release.
There’s a really strong emphasis on making sure that the tools have well-defined, well-documented and robust APIs. They want to support customisation, which means documentation is really important if sound extensions and new front ends can be built. By providing a strong API, engineering teams can build a sensible front end for their team – although complete reinvention of the wheel is frowned upon and controlled. Some of the front ends get adopted by other teams, such as the Mondrian UI front-end for Buganizer. Another front end for Google Spreadsheets is Maestro. The API philosophy is carried from the internal tools to the external products.
Google makes heavy use of their own external products that they produce, such as Docs, Spreadsheets and Analytics. (See, dog food, the eating thereof.) This also allows the internal testing of pre-release and just-released products. Google Engineers are slightly allergic to GANTT charts but you can support them by writing an extension to Spreadsheets. There is a spreadsheet called Smartsheet that has been approved for internal use but is not widely used. Scripting over existing tools is far more common.
And now we move onto programming languages. Or should I say that we Go onto programming languages. There are four major languages in use at Google: Java, C++, Python, and Go (the Google language). Alan’s a big fan of Go and recommends it for distributed and concurrent systems. (I’ve used it a bit and it’s quite interesting but I haven’t written enough in it to make much comment.) There are some custom languages as well, including scripting languages for production tasks. Teams can use their own language of choice, although it’s unlikely to be Ruby on Rails anytime soon.
Is letting engineers pick their language the key to Google’s success? Is it the common platform? The common tools? No. The platforms, tools and languages won’t matter if your organisational culture isn’t right. If the soil is toxic, the tree won’t grow. Google is in a highly competitive space and have to be continually innovating and improving or users will go elsewhere. The drive for innovation is the need to keep the users insanely happy. Getting the organisational settings right is essential: how do you foster innovation?
Well, how do they do it? First and foremost, it’s about producing a culture of innovation. The wrong culture and you won’t get interesting or exciting software. Hiring matters a LOT. Try to hire people that are smarter than you, are passionate, are quick learners – look for this when you’re interviewing. Senior people at Google need to have technical skills, yes, but they have to be a cultural fit. Will this person be a great addition to the team? (Culture Fit is actually something they assess for – it’s on the form.) Passion is essential: not just for software but for other things as well. If people are passionate about one thing, something, then you’d expect that this passion would flow over into other things in their lives.
Second ingredient: instead of managing, you’re unmanaging. This is why Alan is able to talk today – he’s hired great people and can leave the office without things falling apart. You need to hire technical managers as well, people who have forgotten their technical skills won’t work at Google if they’re to provide a sounding board and be able to mentor members of the team.
The third aspect is being open to sharing information: share, share, share. The free exchange of information is essential in a collaborative environments, based on trust.
“Info sharing is power, info hoarding is impotence.” (Alan Noble)
The fourth thing is to recognise merit. It’s cool to do geeky things. Success is celebrated generously.
Finally, it’s important to empower teams to be agile and to break big projects into smaller, more manageable things. The unit of work at Google is about 3-4 engineers. Have 8 engineers? That’s two 4 person teams. What about meetings? Is face-to-face still important? Yes, despite all the tech. (I spoke about this recently.) Having a rich conversation is very high bandwidth and when you’re in the same room, body language will tell you if things aren’t going across. The 15 minute “stand up” meeting is a common form of meeting: stand up in the workplace and have a quick discussion, then break. There’s also often a more regular weekly meeting which is held in a “fun” space. Google wants you to be within 150m of coffee, food and fuel at all times to allow you to get what you need to keep going, so weekly meetings will be there. There’s also the project kick-off meeting, where the whole team of 20-30 will come together in order to break it down to autonomous smaller units.
People matter and people drive innovation. Googlers are supposed to adapt to fast-paced change and are encouraged to pursue their passions: taking their interests and applying them in new ways to get products that may excite other people. Another thing that happens is TGIF – which is now on Thursday, rather than Friday, where there is an open Q and A session with the senior people at Google. But you also need strong principles underlying all of this people power.
The common guiding principles that bring it all together need to be well understood and communicated. Here’s Alan’s list of guiding principles (the number varies by speaker, apparently.)
- Focus on the user. This keeps you honest and provides you with a source of innovation. Users may not be articulate what they want but this, of course, is one of our jobs: working out what the user actually wants and working out how many users want a particular feature.
- Start with problems. Problems are a fantastic source of innovation. We want to be solving real, important and big problems. There are problems everywhere!
- Experiment Often. Try things, try a lot of things, work out what works, detect your failures and don’t expose your users to any more failures than you have to.
- Fail Fast. You need to be able to tolerate failure: it’s the flip side of failure. (A brief mention of Google Wave, *sniff*)
- Paying Attention to the Data. Listen to the data to find out what is and what is not working. Don’t survey, don’t hire marketing people, look at the data to find out what people are actually doing!
- Passion. Let engineers find their passion – people are always more productive when they can follow their passion. Google engineers can self-initiate a transfer to encourage them to follow their passion, and there is always the famous Google 20% time.
- Dogfood. Eat your own dogfood! Testing your own product in house and making sure that you want to use it is an essential step.
The Google approach to failure has benefited from the Silicon Valley origins of the company, with the approach to entrepreneurship and failure tolerance. Being associated with a failed start-up is not a bad thing: failure doesn’t have to be permanent. As long as you didn’t lie, cheat or steal, then you’ve gained experience. It’s not making the mistake, it’s how you recover from it and how you carry yourself through that process (hence being ethical even as the company is winding down).
To wind it all up, Google doesn’t have standard SE processes across the company: they focus on getting their organisation culture right with common principles that foster innovation. People want to do exciting things and follow new ideas so every team is empowered to make their own choices, select their own tools and processes. Launch, iterate, get it out, and don’t hold it back. Grow your software like a tree rather than dropping a monolith. Did it work? No? Wind it back. Yes? Build on it! Take the big bets sometimes because some big problems need big leaps forward: the moon shot is a part of the Google culture.
Embrace failure, learn from your mistakes and then move on.
Regrettably, despite best efforts, I was a bit late getting back from the lunch and I missed the opening session, so my apologies to Andres Neyem, Jose Benedetto and Andres Chacon, the authors of the first paper. From the discussion I heard, their course sounds interesting so I have to read their paper!
The next paper was “Selecting Open Source Software Projects to Teach Software Engineering” presented by Robert McCartney from University of Connecticut. The overview is why would we do this, the characteristics of the students, the projects and the course, finding good protects, what we found, how well it worked and what the conclusions were.
In terms of motivation, most of their SE course is in project work. The current project approach emphasises generative aspects. However, in most of SE, the effort involves maintenance and evolution. (Industry SE’s regularly tweak and tune, rather than build from the bottom.) The authors wanted to change focus to software maintenance and evolution, have the students working on an existing system, understanding it, adding enhancements, implementing, testing and documenting their changes. But if you’re going to do this, where do you get code from?
There are a lot of open source projects, available on0line, in a variety of domains and languages and at different stages of development. There should* be a project that fits every group. (*should not necessarily valid in this Universe.) The students are not actually being embedded in the open source community, the team is forking the code and not planning to reintegrate it. The students themselves are in 2nd and 3rd year, with courses in OO and DS in Java, some experience with UML diagrams and Eclipse.
For each team of students, they get to pick a project from a set, try to understand the code, propose enhancements, describe and document all o their plans, build their enhancements and present the results back. This happens over about 14 weeks. The language is Java and the code size has to be challenging but not impossible (so about 10K lines). The build time had to fit into a day or two of reasonable effort (which seems a little low to me – NF). Ideally, it should be a team-based project, where multiple developed could work in parallel. An initial look at the open source repositories on these criteria revealed a lot of issues: not many Java programs around 10K but Sourceforge showed promise. Interestingly, there were very few multi-developer projects around 10K lines. Choosing candidate projects located about 1000 candidates, where 200 actually met the initial size criterion. Having selected some, they added more criteria: had to be cool, recent, well documented, modular and have capacity to be built (no missing jar files, which turned out to be a big problem). Final number of projects: 19, size range 5.2-11 k lines.
That’s not a great figure. The takeaway? If you’re going to try and find projects for students, it’s going to take a while and the final yield is about 2%. Woo. The class ended up picking 16 projects and were able to comprehend the code (with staff help). Most of the enhancements, interestingly, involved GUIs. (Thats not so great, in my opinion, I’d always prefer to see functional additions first and shiny second.)
In concluding, Robert said that it’s possible to find OSS projects but it’s a lot of work. A search capability for OSS repositories would be really nice. Oh – now he’s talking about something else. Here it comes!
Small projects are not built and set up to the same standard as larger projects. They are harder to build, less-structured and lower quality documentation, most likely because it’s one person building it and they don’t notice the omissions. Thes second observation is that running more projects is harder for the staff. The lab supervisor ends up getting hammered. The response in later offerings was to offer fewer but larger projects (better design and well documented) and the lab supervisor can get away with learning fewer projects. On the next offering, they increased the project size (40-100K lines), gave the students the build information that was required (it’s frustrating without being amazingly educational). Overall, even with the same projects, teams produced different enhancements but with a lot less stress on the lab instructor.
Rather unfortunately, I had to duck out so I didn’t see Claudia’s final talk! I’ll write it up as a separate post later. (Claudia, you should probably re-present it at home. 🙂 )