Coding Is Like Cooking

Note: This post first appeared on Pagero’s blog

One of the questions that Kent Beck asked when he was developing the eXtreme Programming development methodology, was what happens if we turn the dials up all the way to 10? Take a practice we know is good, and do more of it? Practices like Test-Driven Development and Pair Programming are what he came up with, starting from manual testing and code review.

In the same way, Continuous Delivery is what you get if you turn the dials to 10 on your annual release cycle. You get to the point that you are pushing out new code to users, many times a day.

“Shortening the release cycle like this has a lot of advantages, especially around risk and quality.”

LOWER RISK AND HIGHER QUALITY WITH SHORTER RELEASE CYCLES

Shortening the release cycle like this has a lot of advantages, especially around risk and quality. Basically, you’re decreasing the batch size, a well-known tenet of lean manufacturing. If each new release contains fewer changes, then you have fewer places to look when things go wrong, so finding bugs is easier. You also lower the risk that any individual batch has a defect in the first place. By having an engineering setup that allows you to make code changes at the drop of a hat and push them out to production easily, you facilitate getting fixes out quickly.

So the upshot is quality problems surface sporadically instead of all at once, and are more easily dealt with. It’s an attractive prospect for us, especially with the growth in traffic we’re experiencing. Every time we have a defect in production, it affects a proportion of our customers, and the number of customers is increasing all the time. If we had a small bug a year ago that affected one or two customers, today the same bug might affect tens or even hundreds.

FROM MONOLITH TO MICROSERVICES FOR GREATER FLEXIBILITY

At Pagero, historically we’ve been pushing out a new version of our product “Pagero Online”, about once a month. We’ve been able to sustain that since about 2007. So when we began looking at Continuous Delivery, about three years ago, we were starting from a fairly good position. We’ve experienced steady growth in transactions through our cloud platform since the start, and it was in early 2014 we started switching over our architecture from a clustered monolithic JEE instance, to distributed microservices (see my previous article).

We needed to do this, in order to scale out our system horizontally, and handle the increasing traffic. One of the other benefits of microservices though, is you can deploy services independently of one another, and if you do it right, you can deploy new code without stopping traffic to the site.

“One of the other benefits of microservices, is you can deploy services independently of one another.”

FROM MONTHLY SERVICE WINDOWS ON SUNDAYS…

Our old monthly release cycle was based on having a ‘service window’, usually on a Sunday morning, where we could stop all the traffic, take a backup of the database, roll out the new version of the monolith, then bring everything back up again. You’ve got the database backup to fall back on, if something goes wrong with the update. You can easily roll everything back to the state it had before the service window.

…TO SEVERAL ROLLOUTS A WEEK

So of course, initially the microservices we had were fairly peripheral to the main function of our platform, and it wasn’t a huge risk to roll out new code without the safety of a service window. So we built deployment tools that allowed us to do that. All our microservices run with at least two instances, so an update consisted of taking each instance down in turn, replacing it with the new version. If something goes wrong, it’s not hard to roll back to a previous version. It’s a little more problematic to restore previous state, but generally we have good mechanisms to re-submit failed transactions once the service is working again.

So these days we roll out new versions of our microservices several times a week, when new features are ready, and rarely have any difficulties with this. The need to roll back does occur occasionally, but more often we can ‘roll-forward’ and deploy a newer version with a fix.

“These days we roll out new versions of our microservices several times a week, when new features are ready.”

MANY REASONS TO CONTINUE ON THIS PATH

With our former monolith, the situation is a little different though. Any changes that touch the database are deemed too risky to deploy without first taking a backup, and that currently requires a service window. We’ve got so used to frequently pushing out new versions of the microservices, and seen the benefits of that, that we’d like to do the same with the former monolith.

We also have good business reasons for wanting to release without having a service window – for a start our traffic is growing at such a rate, we can ill afford any downtime. Perhaps more importantly, as we get customers in more parts of the world, a Sunday morning is no longer a ‘quiet’ time of the week when it’s relatively ok to suspend our service. In some Arab countries where we do business, Sunday is the first day of the working week.

THE SHIFT TO CONTINUOUS DELIVERY HAS STARTED

Now we’ve gained some experience with Continuous Delivery of our microservices, it’s time to do the same with the whole Pagero Online platform, including our old monolith. So I look forward to being able to soon report that we’ve got the dials going all the way up to 10 and we are deploying any part of our system at any time.

Please note – As of March 2013, I have rewritten this post in the light of further experience and discussions. The updated post is available here.

I feel like I’ve spent most of my career learning how to write good automated tests in an agile environment. When I downloaded JUnit in the year 2000 it didn’t take long before I was hooked – unit tests for everything in sight. That gratifying green bar is near-instant feedback that everthing is as expected, my code does what I intended, and I can continue developing from a firm foundation.

Later, starting in about 2002, I began writing larger granularity tests, for whole subsystems; functional tests if you like. The feedback that my code does what I intended, and that it has working functionality has given me confidence time and again to release updated versions to end-users.

Often, I’ve written functional tests as regression tests, after the functionality is supposed to work. In other situations, I’ve been able to write these kinds of tests in advance, as part of an ATDD, or BDD process. In either case, I’ve found the regression tests you end up with need to have certain properties if they’re going to be useful in an agile environment moving forward. I think the same properties are needed for good agile functional tests as for good unit tests, but it’s much harder. Your mistakes are amplified as the scope of the test increases.

I’d like to outline four principles of agile test automation that I’ve derived from my experience.

Coverage

If you have a test for a feature, and there is a bug in that feature, the test should fail. Note I’m talking about coverage of functionality, not code coverage, although these concepts are related. If your code coverage is poor, your functionality coverage is likely also to be poor.

If your tests have poor coverage, they will continue to pass even when your system is broken and functionality unusable. This can happen if you have missed out needed test cases, or when your test cases don’t check properly what the system actually did. The consequences of poor coverage is that you can’t refactor with confidence, and need to do additional (manual) testing before release.

The aim for automated regression tests is good Coverage: If you break something important and no tests fail, your test coverage is not good enough. All the other principles are in tension with this one – improving Coverage will often impair the others.

Readability

When you look at the test case, you can read it through and understand what the test is for. You can see what the expected behaviour is, and what aspects of it are covered by the test. When the test fails, you can quickly see what is broken.

If your test case is not readable, it will not be useful. When it fails you will have to dig though other sources outside of the test case to find out what is wrong. Quite likely you will not understand what is wrong and you will rewrite the test to check for something else, or simply delete it.

As you improve Coverage, you will likely add more and more test cases. Each one may be fairly readable on its own, but taken all together it can become hard to navigate and get an overview.

Robustness

When a test fails, it means the functionality it tests is broken, or at least is behaving significantly differently from before. You need to take action to correct the system or update the test to account for the new behaviour. Fragile tests are the opposite of Robust: they fail often for no good reason.

Aspects of Robustness you often run into are tests that are not isolated from one another, duplication between test cases, and flickering tests. If you run a test by itself and it passes, but fails in a suite together with other tests, then you have an isolation problem. If you have one broken feature and it causes a large number of test failures, you have duplication between test cases. If you have a test that fails in one test run, then passes in the next when nothing changed, you have a flickering test.

If your tests often fail for no good reason, you will start to ignore them. Quite likely there will be real failures hiding amongst all the false ones, and the danger is you will not see them.

As you improve Coverage you’ll want to add more checks for details of your system. This will give your tests more and more reasons to fail.

Speed

As an agile developer you run the tests frequently. Both (a) every time you build the system, and (b) before you check in changes. I recommend time limits of 2 minutes for (a) and 10 minutes for (b). This fast feedback gives you the best chance of actually being willing to run the tests, and to find defects when they’re cheapest to fix.

If your test suite is slow, it will not be used. When you’re feeling stressed, you’ll skip running them, and problem code will enter the system. In the worst case the test suite will never become green. You’ll fix the one or two problems in a given run and kick off a new test run, but in the meantime someone else has checked in other changes, and the new run is not green either. You’re developing all the while the tests are running, and they never quite catch up. This can become pretty demoralizing.

As you improve Coverage, you add more test cases, and this will naturally increase the execution time for the whole test suite.

How are these principles useful?

I find it useful to remember these principles when designing test cases. I may need to make tradeoffs between them, and it helps just to step back and assess how I’m doing on each principle from time to time as I develop.

I also find these principles useful when I’m trying to diagnose why a test suite is not being useful to a development team, especially if things have got so bad they have stopped maintaining it. I can often identify which principle(s) the team has missed, and advise how to refactor the test suite to compensate.

For example, if the problem is lack of Speed you have some options and tradeoffs to make:

• Invest in hardware and run tests in parallel (costs $)
• Use a profiler to optimize the tests for speed the same as you would production code (may affect Readability)
• push down tests to a lower level of granularity where they can execute faster. (may reduce Coverage and/or increase Readability)
• Identify key test cases for essential functionality and remove the other test cases. (sacrifice Coverage to get Speed)

Explaining these principles can promote useful discussions with people new to agile, particularly testers. The test suite is a resource used by many agile teamembers – developers, analysts, managers etc, in its role as “Living Documentation” for the system, (See Gojko Adzic‘s writings on this). This emphasizes the need for both Readability and Coverage. Automated tests in agile are quite different from in a traditional process, since they are run continually throughout the process, not just at the end. I’ve found many traditional automation approaches don’t lead to enough Speed and Robustness to support agile development.

I hope you will find these principles will help you to reason about the automated tests in your suite.

Programmers have a vested interest in making sure the software they create does what they think it does. When I’m coding I prefer to work in the context of feedback from automated tests, that help me to keep track of what works and how far I’ve got. I’ve written before about Test Driven Development, (TDD). In this article I’d like to explain some of the main features of Text-Based Testing. It’s a variant on TDD, perhaps more suited to the functional level than unit tests, and which I’ve found powerful and productive to use.


The basic idea
You get your program to produce a plain text file that documents all the important things that it does. A log, if you will. You run the program and store this text as a “golden copy” of the output. You create from this a Text-Based Test with a descriptive name, any inputs you gave to the program, and the golden copy of the textual output.

You make some changes to your program, and you run it again, gathering the new text produced. You compare the text with the golden copy, and if they are identical, the test passes. If the there is a difference, the test fails. If you look at the diff, and you like the new text better than the old text, you update your golden copy, and the test is passing once again.

Tool Support
Text-Based Testing is a simple idea, and in fact many people do it already in their unit tests. AssertEquals(String expected, String actual) is actually a form of it. You often create the “expected” string based on the actual output of the program, (although purists will write the whole assert before they execute the test).

Most unit test tools these days give you a nice diff even on multi-line strings. For example:

Which is a failing text-based test using JUnit.

Once your strings get very long, to the scale of whole log files, even multi-line diffs aren’t really enough. You get datestamps, process ids and other stuff that changes every run, hashmaps with indeterminate order, etc. It gets tedious to deal with all this on a test-by-test basis.

My husband, Geoff Bache, has created a tool called “TextTest” to support Text-Based testing. Amongst other things, it helps you organize and run your text-based tests, and filter the text before you compare it. It’s free, open source, and of course used to test itself. (Eats own dog food!) TextTest is used extensively within Jeppesen Systems, (Geoff works for them, and they support development), and I’ve used it too on various projects in other organizations.

In the rest of this article I’ll look at some of the main implications of using a Text-Based Testing approach, and some of my experiences.

Little code per test
The biggest advantage of the approach, is that you tend to write very little unique code for each test. You generally access the application through a public interface as a user would, often a command line interface or (web)service call. You then create many tests by for example varying the command line options or request contents. This reduces test maintenance work, since you have less test code to worry about, and the public API of your program should change relatively infrequently.

Legacy code
Text-Based Testing is obviously a regression testing technique. You’re checking the code still does what it did before, by checking the log is the same. So these tests are perfect for refactoring. As you move around the code, the log statements move too, and your tests stay green, (so long as you don’t make any mistakes!) In most systems, it’s cheap and risk-free to add log statements, no matter how horribly gnarly the design is. So text-based testing is an easy way to get some initial tests in place to lean on while refactoring. I’ve used it this way fairly successfully to get legacy code under control, particularly if the code already produces a meaningful log or textual output.


No help with your design
I just told you how good Text-Based Testing is with Legacy code. But actually these tests give you very little help with the internal design of your program. With normal TDD, the activity of creating unit tests at least forces you to decompose your design into units, and if you do it well, you’ll find these tests giving you all sorts of feedback about your design. Text-Based tests don’t. Log statements don’t care if they’re in the middle of a long horrible method or if they’re spread around several smaller ones. So you have to get feedback on your design some other way.

I usually work with TDD at the unit level in combination with Text-Based tests at the functional level. I think it gives me the best of both worlds.

Log statements and readability
Some people complain that log statements reduce the readability of their code and don’t like to add any at all. They seem to be out of fashion, just like comments. The idea is that all the important ideas should be expressed in the class and method names, and logs and comments just clutter up the important stuff. I agree to an extent, you can definitely over-use logs and comments. I think a few well placed ones can make all the difference though. For Text-Based Testing purposes, you don’t want a log that is megabytes and megabytes of junk, listing every time you enter and leave every method, and the values of every variable. That’s going to seriously hinder your refactoring, apart from being a nightmare to store and update.

What we’re talking about here is targeted log statements at the points when something important happens, that we want to make sure should continue happening. You can think about it like the asserts you make in unit tests. You don’t assert everything, just what’s important. In my experience less than two percent of the lines of code end up being log statements, and if anything, they increase readability.

Text-Based tests are completed after the code
In normal TDD you write the test first, and thereby set up a mini pull system for the functionality you need. It’s lean, it forces you to focus on the problem you’re trying to solve before you solve it, and starts giving you feedback before you commit to an implementation. With Text-Based Testing, you often find it’s too much work the specify the log up front. It’s much easier to wait until you’ve implemented the feature, run the test, and save the log afterwards.

So your tests usually aren’t completed until after the code they test, unlike in normal TDD. Having said that, I would argue that you can still do a form of TDD with Text-Based Tests. I’d normally create the half the test before the code. I name the test, and find suitable inputs that should provoke the behaviour I need to implement in the system. The test will fail the first time I run it. In this way I think I get many of the benefits of TDD, but only actually pin down the exact assertion once the functionality is working.

“Expert Reads Output” Antipattern
If you’re relying on a diff in the logs to tell you when your program is broken, you had better have good logs! But who decides what to log? Who checks the “golden copy”? Usually it is the person creating the test, who should look through the log and check everything is in order the first time. Of course, after a test is created, every time it fails you have to make a decision whether to update the golden copy of the log. You might make a mistake. There’s a well known antipattern called “Expert Reads Output” which basically says that you shouldn’t rely on having someone check the results of your tests by eye.

This is actually a problem with any automated testing approach – someone has to make a judgement about what to do when a test fails – whether the test is wrong or there’s a bug in the application. With Text-Based Testing you might have a larger quantity of text to read through compared with other approaches, or maybe not. If you have human-readable, concise, targeted log statements and good tools for working with them, it goes a long way. You need a good diff tool, version control, and some way of grouping similar changes. It’s also useful to have some sanity checks. For example TextTest can easily search for regular expressions in the log and warn you if you try to save a golden copy containing a stack trace for example.

In my experience, you do need to update the golden copy quite often. I think this is one of the key skills with a Text-Based Testing approach. You have to learn to write good logs, and to be disciplined about either doing refactoring or adding functionality, not both at the same time. If you’re refactoring and the logs change, you need to be able to quickly recognize if it’s ok, or if you made a mistake. Similarly, if you add new functionality and no logs change, that could be a problem.

Agile Tests Manage Behaviour
When you create a unit test, you end with an Assert statement. This is supposed to be some kind of universal truth that should always be valid, or else there is a big problem. Particularly for functional level tests, it can be hard to find these kinds of invariants. What is correct today might be updated next week when the market moves or the product owner changes their mind. With Text-Based Testing you have an opportunity to quickly and easily update the golden copy every time the test “fails”. This makes your tests much more about keeping control of what your app does over time, and less about rewriting assert statements.

Text-Based Testing grew up in the domain of optimizing logistics planning. In this domain there is no “correct” answer you can predict in advance and assert. Planning problems that are interesting to solve are far too complex for a complete mathematical analysis, and the code relies on heuristics and fancy algorithms to come up with better and better solutions. So Text-Based Testing makes it easy to spot when the test produces a different plan from before, and use it as the new baseline if it’s an improvement.

I think generally it leads to more “agile” tests. They can easily respond to changes in the business requirements.

Conclusions
There is undoubtedly a lot more to be said about Text-Based Testing. I havn’t mentioned text-based mocking, data-driven vs workflow testing, or how to handle databases and GUIs – all relevant topics. I hope this article has given you a flavour of how it’s different from ordinary TDD, though. I’ve found that good tool support is pretty essential to making Text-Based Testing work well, and that it’s a particularly good technique for handling legacy code, although not exclusively. I like the approach because it minimizes the amount of code per test, and makes it easy to keep the tests in sync with the current behaviour of the system.

I’m speaking next week at ScanDev on Tour in Stockholm on the subject of “Software Development Craftsmanship”, and as part of my research I read both “The Clean Coder” by Robert C. Martin and “Apprenticeship Patterns” by Dave Hoover & Adewale Oshineye. These are very different books, but both aimed at less experienced software developers who want to learn about what it means to be a professional in the field. In this article I’d like to review them side by side. First some text from each preface on what the authors think the books are about:

Apprenticeship Patterns

“This book should help you through the tough decisions you face as a newcomer to the field of professional software development. “ (preface xi)

The Clean Coder

“This book is about software professionalism. It contains a lot of pragmatic advice” (preface xxii)

The Content
Both books contain a lot of personal stories and anecdotes from the authors’ careers, and begin with a short autobiography. Some of the advice is also similar. Both advise you to practice with “Kata” exercises, to read widely and to find suitable mentors. I think that’s mostly where the similarities end though.

Dave and Ade don’t say much about how to handle unreasonable managers imposing impossible deadlines. Bob Martin devotes a several chapters to this kind of issue, handling pressure, time management, estimation, making committments etc.

Dave and Ade talk more about how to get yourself into situations optimized for learning and progress in your career. They advise you to “Be the worst”, “Find mentors”, seek “Kindred Spirits”. In other words, join a team where you’re the least skilled but you’ll be taught, look for mentors in many places, and get involved in the community.

Bob talks about a lot of specific practices and has detailed advice. He mentions “… pairing is the most efficient way to solve a problem” (p164) Later in the chapter he suggests the optimal composition of job roles in a gelled team. (p169) He also has some advice about how to successfully argue with your boss and go over their head when necessary (p35).

The Advice
Those few example perhaps illustrate that these two books are miles apart when it comes to writing style, approach and world view. Dave&Ade have clearly spent a lot of time talking with other professionals about their material, acting on feedback and testing their ideas for validity in real situations. The book is highly collaborative and while full of advice, is not prescriptive.

Bob Martin on the other hand loves to be specific, provocative and extreme in his advice. “QA should find nothing.”(p114) “You should plan on working 60 hours per week.” (p16) “Avoid the Zone.” (p62) “The jury is in! … TDD works” (p79) These are some of his more suprising pieces of advice, which I think are actually fairly doubtful propositions when taken to extremes like this. Mixed in are more reasonable statements. “You do not have to attend every meeting to which you are invited” (p123) “The professional developer is calm and decisive under pressure”. (p150)

The way everything is presented as black-and-white, do-or-do-not-there-is-no-try is actually pretty wearing after a while. He does it to try to make you think, as a rhetorical device, to promote healthy discussion. I think it all too easily leads the reader to throw the baby out with the bathwater. I can’t accept one of his recommendations, so I throw them all out.

Some of Dave&Ade’s advice is actually just as hard to put into practice. Each of their patterns is followed by a call to action. Things like re-implementing a program you’ve written in an imperative language in a functional language (p21). Join or start a user group (p65). Solve the same coding exercise once a week for the next four weeks (p79). None of these things is particularly easy to do, but they seem to me to be interesting and useful challenges.


Collaboration
Bob has also clearly not collaborated very widely when preparing his material. One part that particularly sticks out for me is a footnote on page 75:

“I had a wonderful conversation with @desi (Desi McAdam, founder of DevChix) about what motivates women programmers. I told her that when I got a program working, it was like slaying the great beast. She told me that for her and other women she had spoken to, the act of writing code was an act of nurturing creation.” (footnote, p75)

Has he ever actually run his “programming is slaying a great beast” thing past any other male programmers? Let me qualify that – non-fantasy-role-playing male programmers? Thought not. This is in enormous contrast to Dave&Ade, whose book is full of stories from other people backing up their claims.

Stories
Bob’s book is full of stories from his own career, and he is very honest and open about his failures. This is a very brave thing to do, and I have a great deal of respect for him for doing so. It’s also really interesting to hear about the history of what life was like when computers filled a room and people used punch cards to program them. Dave&Ades stories are less compelling and not always as well written.

Bob’s book is not just about his professional life, he shares his likes and dislikes. He reccommends cycling or walking to recharge your energy, or “focus-manna” as he calls it, (p127). Reading science fiction as a cure for writer’s block. (p66) Listening to “The Wall” while coding could be bad for your design. (p63) When describing “Master” programmers he likens them to Scotty from Star Trek. (p182)

All this is very cute and gives you a more rounded picture of what software professionalism is about. Maybe. Actually it really puts me off the idea. I know a lot of software developers like science fiction and fantasy role playing, but it really isn’t mandatory. He usually says that you may have other preferences, and you don’t have to do like he does, but I just don’t think it helps all that much. The rest of the book is highly dogmatic about what you should and shouldn’t do, and it kind of rubs off.

Conclusions
The bottom line is, I wouldn’t reccommend “The Clean Coder” to any young inexperienced software developer, particularly not if she were a woman. There is too much of it written from a foreign culture, in a demanding tone, propounding overly extreme behaviour. The interesting stories and good pieces of advice are drowned out.

On the other hand, I would recommend “Apprenticeship Patterns”. I think it is humbly written and anchored in real experience from a range of people. I agree with them when they say you need to read it twice to understand it. The first time to get an overview, the second time to understand how the patterns connect. It’s not as easy to read as it might be. But still, I think the content is interesting, and it gives a good introduction to what being a professional software craftsman is about, and how to get there.

This is the second time I’ve attended Nordic Ruby, you can read about what I thought last year here. This year I enjoyed the conference more, for several reasons. There were some small changes in the way it was organized, (on a Friday and Saturday instead of taking up a whole weekend), a better choice of speakers and topics, (less technical, more inspirational), and I knew more of the people there.

One of the themes of the conference was diversity, which I was very, very happy to see. There was an inspiring talk by Joshua Wehner about this topic, taking up some depressing statistics about the IT industry in general and open source software in particular. What struck me most was that he said the statistics for women involvement are improving in many formerly male-dominated disciplines, like maths, physics and law, but in computing, the situation was actually better 20 years ago than it is now. The curves are pointing the wrong way in our industry.

Having said that, there were slightly more women at the conference this year than last, I think I counted 4 of 150, compared with 2 of 90 last year. There were also far fewer references to science fiction movies from the speakers this year 😉

Joshua did take up several things that we could do practically to reduce bias and positively encourage diversity. He’s written about some of them in this blog post. Another one he mentioned that I liked was the “no asshole rule”. If people engage in arrogant one-upmanship, talk down to others, and emphasize their superior programming abilities, they should be regarded as not just annoying, but actually incompetent. Developing software is a multi-faceted skill, and it takes a lot more than just writing good code to be a good software developer.

Joe O’Brien continued the diversity theme in his talk “Taking back education” by basically arguing that having a degree in computer science correlates very badly with being a good software developer, and that we should be finding ways to bring people into our industry who have non-traditional backgrounds. He advocated companies to start apprenticeship programmes, while conceding that this model of education doesn’t scale very well. He talked about getting a group of companies together to set up a “code school”. He said “forget universities when it comes to education [of software developers]. We’re better at it”

I applaud his efforts to bring a more diverse range of people into the industry, and I think my recent experiences teaching a group like this are relevant. I think I’ll write a separate blog post about that experience, but basically I think the idea of a “code school” is a good one, and similar institutions probably already exist, and could add a course in software development to their programme of courses in practical skills. For this to happen it’s up to companies to put in time and energy setting them up, rather than just complaining that when they put out a job advert, all they get are white male applicants between the ages of 25-35, so it’s not their fault.

Another talk that deserves a mention is the one by Joseph Wilk. He spoke about “The Limited Red Society” which is an idea that Joshua Kerievsky came up with. I heard Joshua speak about it at XP2009, and I thought Joseph did a very good job of explaining what it is, and why it’s important.

Basically the idea is that although you need your tests to go red during TDD, if they stay red for any length of time, it can get you into trouble. While they are red, you can’t check in, ship your code, or change to working on a different task. This is one motivation for trying to measure, and limit, how much of the time your tests are red. It’s also about more generally improving the feedback we get for ourselves while we work. Professional sports stars spend time analysing and visualizing their performances (where balls land on a tennis court, footballers rates of passing etc). We programmers could benefit from that kind of thing too.

Joseph has invented a tool that helps him to track his state when doing TDD. It’s a simple monitoring program that makes a note every time he runs his tests. It’s not as elaborate as the commercial tool offered by Joshua Kerievsky’s company, but it does work with Ruby and Cucumber. Joseph also has his tool connected to his CI server so that it runs tests that have failed recently in his and others’ checkouts first in the CI test run. He also gathers statistics about individual tests, how often they fail, and whether they are fixed without the production code needing to be changed – a way of spotting fragile tests.

I think this kind of statistics gathering is really interesting and I think Joseph will just have more insights to share as he gathers more data and does more analysis. I’ve been experimenting with the tool provided by codersdojo.org for measuring my performance at code katas, but Joseph seems to be taking this all to the next level.

Overall I thoroughly enjoyed Nordic Ruby. (I still think it would be improved by some actual open space sessions though). I talked to loads of really interesting people, enjoyed good food and drink in comfortable surroundings, and listened to some people give excellent talks. Thanks for organizing a great conference, Elabs.