The Law of Conservation of Testing Effort

The Law of Conservation of Testing Effort:

  1. Like energy, the total testing burden for a piece of software is constant. It can’t be added to nor reduced, only transformed.
  2. However, the cost to your business (in terms of time, money and trust) of these different forms is variable.

Here’s an example.

Let’s say I’ve been tasked with adding a new screen to my company’s mobile app. Let’s also say I’m not in the mood for following TDD right now. I just want to see the new screen in the app as soon as possible.

So I go ahead and write the feature without any tests.

Since I’m still somewhat of a responsible developer, I need to test the scenarios for the new screen before shipping it. It’s just that now I have to test them manually.

Luckily I mean uh, because I’m an awesome developer, everything works the first time. Nice.

I just cleverly avoided spending an extra hour of tedious development time writing tests.

Or did I?

Notice that I didn’t actually dodge any testing responsibilities. I simply exchanged up-front, deterministic, slow-to-write, fast-to-run tests for after-the-fact, non-deterministic, free-to-write, slow-to-run tests. This seems like a good deal if I only need to run the tests once. And it would be, if this were ever true.

In reality, software that never changes is almost immediately obsolete. Changes to this new screen will inevitably be needed. And with every change, the tests need to be run again. This shifts the cost-benefit equation in favour of the up-front, automated tests. The prospect of running through all of the scenarios manually each time is becoming less and less attractive and clever-sounding.

Now, on to a more extreme example.

Let’s say I’m a less responsible yet even more confident developer (not a good combo). After coding the new screen, I run the app and give it a quick smoke test. The screen shows up with the expected content. Nice. Ship it. I go and get a coffee, smugly satisfied with my speed and skill as a developer, having cleverly dodged both automated testing and manual testing.

What about the burden of testing the different edge cases for the new feature? Did it evaporate? No, it was merely shifted onto the users – unbeknownst to them. The testing effort remains constant but the cost is different. The immediate, measurable cost of developer time is exchanged for cost to your product and company’s reputation – i.e. lost trust with your users as they discover bugs that could have easily been found prior to shipping. In not hard to see how this will ultimately impact your company’s bottom line, too.

Now, am I making the case that you should work to predict all possible errors for all conceivable scenarios before shipping anything? Not at all. Due to the ultimately unpredictable complexity of a real production environment with real users, there is a class of errors which you can only find out about by shipping. Some of the testing burden forever rests on your users.

Also, too much time spent trying to predict errors comes with its own costs. As Charity Majors points out in her excellent article I test in prod:

80 percent of the bugs are caught with 20 percent of the effort, and after that you get sharply diminishing returns.

Failing to ship fast enough deprives your users of valuable-but-imperfect software. And your team is deprived of valuable learning from real-world testing, absolutely crucial in adapting the product quickly enough for the business to survive.

The implication of the Law of Conservation of Testing Effort is not that all testing should be done before production. It’s that attempting to dodge the kinds of testing more appropriate to pre-production merely shifts the burden into post-prod, where it costs more.

The phraseI don’t always test, but when I do, I test in production” seems to insinuate that you can only do one or the other: test before production or test in production. But that’s a false dichotomy. All responsible teams perform both kinds of tests. [Emphasis mine]

Publishing your node service with DNS-SD/mDNS from an Alpine Linux docker container

Multicast DNS service discovery, aka. Zeroconf or Bonjour, is a useful means of making your node app (e.g. multiplayer game or IoT project) easily discoverable to clients on the same local network.

The node_mdns module worked out-of-the-box on my Mac. Unfortunately things weren’t as straightforward on a node-alpine docker container running on Raspberry Pi Zero, evidenced by this error at runtime:

Error: dns service error: unknown
    at new Browser (/home/app/node_modules/mdns/lib/browser.js:86:10)
    at Object.create [as createBrowser] (/home/app/node_modules/mdns/lib/browser.js:114:10)

Here’s how I managed to solve this. The following was pieced together from a number of sources (linked at the end).

I’ll assume you have a node app using node_mdns to publish your service, and a Dockerfile based on alpine-linux to build your app into an image for running on the Pi.

Firstly, you’ll need to have the alpine packages to run the avahi daemon, along with its development headers and compat support for bonjour. I.e. in your Dockerfile:

FROM arm32v6/node:10-alpine3.9

# Avahi is for DNS-SD broadcasting on the local network; DBUS is how Avahi communicates with clients
RUN apk add python make gcc libc-dev g++ linux-headers dbus avahi avahi-dev avahi-compat-libdns_sd

You’ll need to make sure the DBus and Avahi daemons are started in your container before starting your node app. Since you can only execute a single startup command from your Dockerfile, we’ll need to bundle the commands into a startup script, and run that. In your Dockerfile:

ENTRYPOINT ["./startup.sh"]

And startup.sh:

#!/usr/bin/env sh

dbus-daemon --system
avahi-daemon --no-chroot &
node index.js    # your app script here

Note: --no-chroot is added to avoid this runtime error:

alpine linux netlink.c: send(): Not supported

Build your Docker image (since this is for a Pi Zero in my case, I’m using DockerX to build for the ARMv6 architecture on my Mac. I recommend this over waiting days or weeks for it to build on the Pi Zero):

docker buildx build -t myapp --platform linux/arm/v6 -o type=docker .

Now push then pull your Docker image onto your Raspberry Pi. If you don’t want to use a cloud-hosted registry, I’d recommend taking a look into setting up a local registry to push it directly to the Pi on your local network.

To run your docker image on the Pi, you’ll first need to disable the host OS’s avahi-daemon (if any) to prevent conflicts with the avahi-daemon that will be running inside your alpine-linux container. On Raspbian, you can disable avahi with:

# SSH into your Pi
sudo systemctl disable avahi-daemon

Then to run your docker image:

docker run -d --net=host localhost:5000/myapp

(localhost:5000 here refers to a local docker registry.) Using the host’s network (--net=host) seems to be necessary for mDNS advertisements to function. In theory you should be able to just map port 5353/udp from the container, but this didn’t work. (If you happen to know why, please drop a comment below).

That’s it. If all goes well you should be able to see your service advertised on the local network. E.g. from a Mac on the same network (the last line is our node app’s http service):

$ dns-sd -B _services._dns-sd._udp
Browsing for _services._dns-sd._udp
DATE: ---Fri 29 May 2020---
22:05:29.580  ...STARTING...
Timestamp     A/R    Flags  if Domain               Service Type         Instance Name
22:05:29.582  Add        3  10 .                    _tcp.local.          _hue
22:05:29.582  Add        3  10 .                    _tcp.local.          _hap
22:05:29.582  Add        3  10 .                    _tcp.local.          _workstation
22:05:29.582  Add        3  10 .                    _tcp.local.          _ssh
22:05:29.582  Add        3  10 .                    _tcp.local.          _sftp-ssh
22:05:29.582  Add        2  10 .                    _tcp.local.          _http

Full sample source code on github: https://github.com/kiwiandroiddev/node-alpine-docker-mdns

Credits/References

https://hub.docker.com/r/stanback/alpine-avahi

https://github.com/homebridge/homebridge/issues/613

https://github.com/joyent/smartos-live/issues/669

https://github.com/home-assistant/docker/issues/23

https://stackoverflow.com/questions/30646943/how-to-avahi-browse-from-a-docker-container

“Business needs vs. Customer needs” is a False Dichotomy

“We have to balance the customer’s needs with the business needs”.

How many times have you heard this while working in a software development team?

I’ve worked as a mobile developer at a number of large companies. In enterprise environments like these, typically the mobile app is “the storefront of the business”, and brings together a number of features paid for by other departments.

Often the initial requirements from the other department will come with a suggestion to make their feature more prominent in the app. For example, “add it to the top of the dashboard”, “just add a new tab for it” or “send a push notification to our users about it”.

This is understandable. The job of the people from the other department is firstly to improve the area of the business they are responsible for. Their job is not to work out how to nicely integrate their feature into the app so it plays nicely with every other feature. That’s the app team’s job.

When members of the app team point out that adding a new top-level tab or push-notification for every new feature requested by every department isn’t a sustainable long-term strategy, and will lead to a poor user experience, the protest that often comes back is something like:

Well, we have to remember to balance the customer’s needs with the business needs.

I was never comfortable with this statement. It’s taken me a while to think through exactly why this is. What I eventually concluded is that while it seems reasonable on the surface, buried in it is a wrong assumption.

It’s not that you should always prioritize the customer’s needs over business needs, or vice versa. Rather, the assumption underlying the statement – that these two things are at odds – is wrong. It’s a false dichotomy.

To believe that “balancing the user’s needs with business needs” makes sense, you need to be engaged in short-term thinking of one kind or another.

If you want your business to survive in the long term, there can be no distinction between the interests of your customer and those of your business.

Your business exists to serve a customer, in a sustainable way. In the final analysis (assuming a free market where your customers can leave), business needs and customer needs must be aligned. Promoting one at the expense of the other actually harms both.

In the long term, building a system that helps the business at the expense of your customers is actually harming both the business and your customers. (Spamming them with notifications in an attempt to boost engagement, for example).

Likewise, building a system that helps your customers at the expense of the business is actually harming both your customers and the business.

How does this second point make sense? I.e. how is that helping your customers at the expense of the business actually harms them?

Here’s how: presumably, your customers would rather your business continues to exist than not. For example, bribing customers with giveaways and subsidized prices isn’t sustainable. If you “spend 1 dollar to make 80 cents”, you will eventually go out of business.

When this happens, you will (at the very least) inconvenience your customers, leaving them bereft or forced against their wishes to switch to a competitor. Or if you offer something unique, you deprive them of that unique offering altogether.

Is it idealistic or wishful thinking to see the success of your customer and business as inextricably linked? Jeff Bezos, the CEO of Amazon, doesn’t seem to think so. The top 3 of his 4 pillars of Amazon’s success are:

  1. Customer Obsession
  2. Eagerness to Invent to Please the Customer
  3. Long-term Orientation

So next time you hear that the “needs of customer need to be balanced with the needs of the business” remember that to successful businesses, there is really no distinction.


Thanks to Xiao and Arun for their feedback and suggestions. Liked this article? Please consider sharing it with your friends and colleagues with one of the buttons below.

Beyond DRY – Why Redundancy Makes Your Code More Robust and Less Fragile

Anti-Fragile by Nassim Nicholas Taleb is a goldmine of practical ideas for software developers, despite it not being a software development book.

Redundancy is one example of such an idea that is explored. Taleb explains how having some redundancy reduces fragility, and means we don’t need to predict the future so well. Think of food stored in your basement, or cash under your mattress.

Taleb notes how nature’s designs frequently employ redundancy (“Nature likes to overinsure itself”):

“Layers of redundancy are the central risk management property of natural systems. We humans have two kidneys […] extra spare parts, and extra capacity in many, many things (say, lungs, neural system, arterial apparatus), while human design tends to be spare and inversely redundant, so to speak – we have a historical track record of engaging in debt, which is the opposite of redundancy”

Software source code is a good example of human design that tends to be “spare” (having no excess fat) and “inversely redundant”. Redundancy in code is traditionally avoided at all costs. In fact, one of the first principles that junior developers are often taught is the DRY principle – Don’t Repeat Yourself. As far as DRY is concerned, redundant code is a blight that should be eliminated wherever it shows up.

There are good reasons for the DRY principle. Duplicate code adds noise to the project, making it harder to understand without adding any obvious value. It makes the project harder to modify because the same code must be maintained separately at each place it is duplicated. Each of these locations is also another opportunity to introduce bugs. Duplicate code feels like waste.

However, as Taleb states:

“Redundancy is ambiguous because it seems like a waste if nothing unusual happens. Except that something unusual happens – usually.” [emphasis added]

What are these “unusual things that usually happen” in software development? And how could duplicate code possibly help protect us against them?

The Wrong Abstraction

Firstly, remember that duplication is eliminated by introducing abstractions, such as a function or class. The problem with abstractions is that it is difficult to know ahead of time whether a chosen abstraction is actually a good fit for your project. And the cost of getting this wrong is high. Poorly-chosen abstractions add friction to making the kinds of changes that are actually needed for the project, while still exacting an ongoing cost in terms of complexity. There’s also the risk that by the time poor abstractions have been recognised as such, they have already spread throughout the project. Rooting them out at this point will likewise impact code all throughout the project, potentially with unintended consequences.

The “unusual things that usually happen” in software development are unexpected, unpredictable (and unavoidable) changes in business requirements. These have the annoying effect of revealing the shortcomings of your abstractions, abstractions that you perhaps added while faithfully following the DRY principle.

Too-eager abstraction and a lack of redundancy mirrors the problems of centralisation, another idea explored in Anti-Fragile. Centralisation, while efficient in the short-term (read: less code), makes systems fragile. When blow-ups happen, they can take down (or at least damage) the entire system. NNT outlines in Anti-Fragile how such fragility and lack of redundancy was the cause of the banking system collapse of 2008.

Redundancy in the form of duplicated code, on the other hand, makes code more robust. It does this by avoiding the worse evil of introducing the wrong abstraction. In this way, it limits the impact of unexpected changes in business requirements. As Sandi Metz states: “Duplication is far cheaper than the wrong abstraction”

The Rule of Three

As it turns out, there is another software development principle (or rule of thumb) which does recognise the risks of poor abstractions, and seeks to mitigate them through some redundancy. It’s called the “Rule of Three”. It states that you should wait until a piece of code appears three times before abstracting it out. (Note that this appears to contradict the DRY principle). This minimises the chances that the abstraction is premature, and increases the chances that it addresses a real, recurring feature of the problem domain that is worth the cost of abstraction.

Introducing an abstraction is in some sense a prediction of the future. Abstractions make a certain class of future changes easier, at the cost of some extra complexity and fragility. They are worth this cost if and only if the types of changes they make easier actually turn out to be reasonably common. Following The Rule of Three means deliberately holding off on making a prediction until more evidence has come in. The assumption built into the Rule of Three is that past changes are the best predictor of future changes.

Back to Nature

Now to return to Taleb’s observation of widespread redundancy in nature’s designs. An interesting implication of this is that despite all of the apparent “waste” involved, evolutionary processes have nonetheless converged onto it as the best strategy for dealing with unpredictability – a permanent feature of the real world (or at least, a better strategy than no redundancy – having one kidney, for instance).

At a high level, our software projects and teams are similar in the sense that they exist in a challenging, competitive environment punctuated by unpredictable changes. If meaningful parallels can be made between complex systems, it’s worth considering the possibility that despite the apparent “waste” involved, some redundancy is likewise the best strategy for dealing with the unpredictability in our environment too.

This is all to say: go forth and fearlessly copy-paste more code 🙂

References and Further Reading

The Wrong Abstraction

Write code that is easy to delete, not easy to extend

Antifragile: Things That Gain from Disorder (Incerto)

10 Tips for Exploring Foreign Cities

 

Ruins of St. Pauls, Macau

Last month I was fortunate enough to spend two weeks traveling around southern China including Hainan, Guangzhou, Macau and Hong Kong. It was an awesome trip; I would particularly recommend stopping by Hong Kong for a few days to check it out if you get the chance. It’s an amazing, vibrant city.

At some point during the trip I started noting down the things I was learning (about travel in general, and travel around cities in particular) into Evernote. Over time the list kept growing. What follows is an edited version of the original list, compiled into a top 10 (in typical web article fashion…)

1. Get the phone number of contacts in foreign country

If you’re meeting friends at the destination airport, make sure you have their mobile number. Just having them on Google Hangouts, WeChat, Facebook messenger or <insert online service here> won’t cut it as you can’t rely on WiFi access at airports. In Shanghai for example, you’ll still need a local mobile number to access the “Free” airport wifi.

Old fashioned and low-tech is sometimes best.

2. Double check that airports of connecting flights match

Cities can have more than one airport, and they may not be close together at all. As a New Zealander, this was surprising to learn…

3. Bring plenty of cash in the local currency

Unlike credit card and bank cards, cash is guaranteed to be accepted everywhere and is a lifesaver in emergencies.

Even if you’re going to a first-world country, don’t assume your card will be widely accepted, even at popular tourist attractions. For instance, you’ll need cash to buy a ticket for the Victoria Peak Tram in Hong Kong.

Another tip: divide your cash up and distribute it amongst your bags. That way if one goes missing, you still have backups. I had three stashes: one in my checked-in luggage, one in my backpack and a small amount in my wallet.

Again, low-tech = good.

4. Pack the night before

It’s easy to be unrealistic about how easy and fast it will be to “throw everything into your bag in the morning”. If checking out of your hotel room in the morning, do all possible packing the night before.

5. Invest in good walking shoes

When you’re out exploring all day every day, decent shoes will really pay dividends. Conversely, bad shoes and feet that are killing you each day can put a damper on your travel experience!

6. Sort out mobile data for your smartphone

Having internet access on your smartphone is absolutely essential when travelling, if only for Maps/GPS, Google Translate and being able to research other places to see while you’re already out.

With that in mind, set up global roaming with your mobile provider before you leave, or check if SIM cards are freely available at destination country. Some countries require you to be a local resident and/or have identification to get a SIM card (Hong Kong isn’t like this; China is).

Remember to pack the SIM card removal tool for your phone, if applicable.

If going to a country with restricted internet access, you may want to sort out VPN access beforehand so you can still access the online services you’re used to (Facebook, YouTube, etc). Record multiple fallback IP addresses for your VPN provider as it’s hard to know which will be blocked.

7. Always have snacks and water with you

Bring water and lots of snack foods such as energy bars and nuts in your day pack to keep up your energy levels throughout the day. You never know where you might end up while exploring; it might be a long time between proper meals.

8. Find out the off-peak hours of the tourist attractions you want to visit…

…and go then to avoid crowds. Crowds are pretty much guaranteed no matter the time of day at remotely popular attractions but you can avoid the worst of it with careful planning. Again, this was a bit of surprise to someone from a country as small as N.Z. where things are pretty much guaranteed to be quiet on weekdays and mornings.

9. Get a Metro map

This is a must if you’re checking out any city with a decent metro (e.g. Guangzhou, Shanghai and Hong Kong) due to the sheer amount of time you’ll spend using it. A paper map is better (no worries about dead batteries) or download a PDF online onto your phone or tablet.

10. Invest in or borrow a decent camera

As good as phone cameras are these days, there’s still no substitute for a standalone camera.

And finally (bonus tip 11), if I’ve learned one thing about travel so far it’s this: the big-name tourist attractions at any given destination can be pretty overrated. They’re often geared towards foreigners so much so that they shield you from the actual local culture. Some of the most enjoyable experiences I’ve had travelling have been while wandering around exploring, taking it all in and spontaneously discovering things. So don’t just tick all the boxes, get out there and experience the authentic whatever-place-it-is.

MySQL + ODBC + Python

How to connect Python programs to a MySQL database using ODBC on Ubuntu 10.04 LTS (Lucid)

This guide assumes you already have a MySQL server set up somewhere

  1. Install needed packages:

    sudo apt-get install unixodbc unixodbc-dev python-dev libmyodbc

    (libmyodbc is the MySQL driver for ODBC)

  2. Get current version of pyodbc:
    If you have python-setuptools installed:

    sudo easy_install pyodbc

    Or with pip (from python-pip):

    sudo pip install pyodbc

    Or if all else fails, download the latest source archive from https://code.google.com/p/pyodbc/downloads/list
    (I used v2.1.8) extract it somewhere on disk, cd into the directory, and run

    sudo python setup.py install

  3. Add a reference to MySQL driver to ODBC config file /etc/odbcinst.ini:

    [MySQL]
    Description = ODBC for MySQL
    Driver = /usr/lib/odbc/libmyodbc.so
    FileUsage = 1

  4. Test it:

    python
    import pyodbc
    cn = pyodbc.connect('DRIVER={MySQL};SERVER=localhost;DATABASE=test;UID=root;PWD=abc;')

For more examples of pyodbc usage the official documentation is very good: https://code.google.com/p/pyodbc/wiki/GettingStarted

This was pieced together from a number of sources which I’ll credit when I find the links again…