Thinking Inside a Bigger Box

“Design” is a verb, not a noun. If I want to create a good program, studying the process of getting there is much more important than the resulting software. This is why I use coding katas as a form of study. I find an interesting problem problem and then solve the same problem over and over again. In this blog post, I will focus on six principles of software design. I will illustrate each with a screencast from a kata.

One of my favorite problems is that of creating a Java EE application from scratch. I call this kata “The Java EE Spike Kata”. In order to understand the role of frameworks, I use no web frameworks in the process of creating this application. I’ve completed this particular exercise about forty times on my own and more than ten times with various pair-programming partners. The whole exercise takes me about 90 minutes, and I still learn new things.

The total time of the screencasts is around 40 minutes, so you may want to pick and choose. Each section provides a link to the starting point for the source code if you want to follow along.

Please notice: The videos are accompanied by loud, pounding music. Keep you headphones on or your volume down if you share offices with someone else. Or mute the videos if you dislike the music.

Idea 1: Build your software from the outside-in

(10 minutes, github starting point)

I start building the application by writing tests that access the application over HTTP and looks at the resulting HTML. As you might have gathered, when I start this test, there is no web application. Only when the tests require a web application to continue do I start creating it. In this example, I had created a basic sketch of the interaction between the three web pages in the application before I started coding. No further design was necessary.

This particular approach uses WebDriver and Jetty to run. The cute assertion library that you may have noticed at the end of the video is FEST-assert.

Idea 2: Specify behavior rather than implementation

(6 minutes, github starting point)

I don’t make much of a distinction between different types of tests. All good tests try to describe what the software should do at some level, rather than how the software does it. But the how at some level may be the what at another level. My first test specified the interaction between the web browser and the server. In this test, one step may be to fill in the form element of a web page. This second video shows how this form works in terms of actual HTML. But the details of what framework (if any) is used, is not visible in the test.

The second thing you’ll notice in the video is that I run the tests more frequently. And each test run is much quicker. As our tests move close to the code, the rate of feedback improves.

This particular test uses Mockito to mock out the Servlet API. The assertions use FEST-assert.

Idea 3: Increase the rate of feedback

(5 minutes, github starting point)

This video illustrates the frequency of feedback. The example test-drives creating an equals-method. This task is often not worth test-driving. The resulting method is usually simple and/or generated by your IDE. But it is a good example to of how quick the cycle between test and production code can be when you’re writing tests that are close to the problem at hand. When I pair program this part of the kata, we usually use a technique called ping-pong programming: One programmer writes a failing test (or failing assertion) and hands the keyboard to his partner, the other programmer makes the test pass and writes another failing test before passing the keyboard back. On a good run, we will switch who’s got the keyboard more often than once per minute.

Notice that I also focus on the behavior of the equals-method in this test.

Idea 4: Grow the API rather than designing it up front

(8 minutes, github starting point)

As the web application grows under my fingers, I discover the need for a Data Access Object (DAO). However, as this represents a major internal interface in my application, I use Mockito to mock the implementation until I’m done with the behavior of my servlet. When this is done, I test-drive the implementation of the DAO in a separate test class.

The video also illustrates another important lesson: The code is getting ripe for a refactoring. But it’s important to resist the urge to refactor until the tests are green. If you refactor on red tests, you have much higher chances of running down a dead-end road and you’ll have to throw away your progress, wondering what went wrong.

The example uses Mockito to mock out the DAO API.

If you want to see how I implement the DAO with Hibernate, you can see the video on blip.tv.

Idea 5: Grow the design rather than speculating

(3 minutes, github starting point)

The video is only a partial example of this principle. Throughout the whole application, I’ve been refactoring, gradually pulling out structure to more well-structured methods and classes. The video illustrates some of the power of IDE’s when it comes to refactoring. Using the IDE to massage your code into a better design makes evolutionary design much easier to do in practice. Make sure to learn your IDE’s most useful refactorings!

The kata may seem like a non-realistic example at this time, but I’ve actually grown a very successful architecture on my current project using much the same approach. If you want to explore where to go next, the next step needed for this application is to factor out the views into separate classes and then use either a View Template language (like Velocity) or a View Transformer (using, for example dom4j) to generate the HTML. (Let me know if you’d like to see the screencast of this as well).

Idea 6: It’s supposed to work the first time around

(5 minutes, github starting point)

In this video, I return to the first test, PersonWebTest, to finish the configuration of the application. I discover a few mistakes I made in the web test as I complete the exercise. Then I try out the code in the browser. And all the scenarios I had planned for work out of the box.

When you try out your code for the first time, it should work. When you master test-driven development, you will probably forget how you programs didn’t use to work the first time. Only when you occasionally run into an unexpected error during manual testing it becomes clear how test-driven development changes you life.

If you want to see the whole, uninterrupted 75 minute code kata, the video is available at blip.tv. You can also take a look at the finished source code at github.

Happy programming!

A big thanks to Trond, Thomas, Ram and Christian who helped improve this post. Thank you to Finn-Robert, Øistein, Mats, Anders, Siv, Peyman, Ivar, Øystein, Cecilia, Nicolay, and Karianne who have pair-programmed this exercise with me. I especially appreciate how Ivar and Karianne both helped influence the way the application is wired together and showed me that even after 30 iterations, I still had things to learn; and how Øistein showed me how two trained developers could complete the exercise faster with pair-programming than alone. And thank you to Nicolay who graciously brought food to our pair-programming exercise.

The videos were made with the excellent (and free!) BB FlashBack Express. The keyboard echo is courtesy of KeyPosé by Magnus Jungsbluth.

My colleague Thomas sent me a very interesting link about attempts to solve Sudoku using test-driven development. The article, somewhat unfairly, pits Ron Jeffries’ explorations of Sudoku using test-driven development against Peter Norvig’s “design driven” approach.

I found both attempts lacking. However, while Ron Jeffries freely admitted that he didn’t even know the rules of Sudoku when he started, both Norvig himself and his readers fawn over his solution. I didn’t find it very understandable.

So I took it upon myself to examine the problem myself. I did some up-front thinking in the shower and on the subway, then attacked the problem with TDD. I ended up with a solution that works in all cases (unlike Norvig). My implementation has readable code, readable tests, and solves the problem reasonably fast.

Observations and conjectures

Here are a few things I learned from the exercise:

• When you’re using TDD to solve a tricky algorithm, you have to think about both the algorithm and the test approach.
• Solving a problem with a known algorithm using TDD gives more readable code than I otherwise would expect.
• When I solved the problem with TDD, running the solution on real problems worked the very first time I tried it.
• The trick to making TDD work is to work from the outside in.
• When creating a Sudoku solver, don’t think like a human! Think like a machine! The human algorithm is difficult to understand and likely to not work on all problems. This was the biggest problem with Norvig’s code

The journey

I decided on the following approach:

1. I had decided upon an initial design with a solver class and a board class. The solver should use a recursive depth first search. The solver asks the board what options exists per cell, but it has no knowledge of the rules of Sudoku (such as no duplicate numbers on the same row).
2. The first step was to get the solver (“the outside”) correct. For this step, I mocked out the board
3. The second step was to implement the interface that the solver needed for the board. Mainly, this is a matter of specifying the rules for what numbers can occur in which cell on a Sudoku board.
4. Finally, I wrote some code to read and write the Sudoku board. When trying the solver on real problems, it worked the first time, and solved 95 hard problems correct. It was somewhat slow, though.

After solving the problem the first time, I practices a few times and recorded a screen cast of the solution:

The solver

Testing the solver is a matter of creating a mock board and ensuring that the solver does the correct things. This is the most complex test case:

@Test
public void shouldBacktrackWhenNoMoreOptions() throws Exception {
    SudokuSolver solver = new SudokuSolver();
    SudokuBoard board = mock(SudokuBoard.class);
    when(board.getOptionsForCell(anyInt(), anyInt()))
            .thenReturn(singleOption());
 
    when(board.getOptionsForCell(8, 7))
            .thenReturn(moreOptions(1, 2));
    when(board.getOptionsForCell(8, 8))
            .thenReturn(noOptions())
            .thenReturn(singleOption());
 
    assertThat(solver.findSolution(board)).isTrue();
    InOrder order = inOrder(board);
    order.verify(board).setValueInCell(1, 8,7);
    order.verify(board).setValueInCell(2, 8,7);
}

It specifies that all cells, except (8,7) and (8,8) return exactly one option. (8,7) returns two options. (8,8) returns no options the first time it is called, and one option the second time. The test verifies that a solution is found, and the solver tries to set both options for (8,7).

This drives a rather simple algorithm. Here’s basically the whole algorithm:

public boolean findSolution(Board board, int cell) {
    if (cell == SIZE*SIZE) return true;
 
    boolean wasEmpty = board.isEmpty(row(cell), col(cell));
    for (Integer value : board.getCellOptions(row(cell), col(cell))) {
        board.setValueInCell(value, row(cell), col(cell));
        if (findSolution(board, cell+1)) return true;
    }
    if (wasEmpty) board.clearValueInCell(row(cell), col(cell));
 
    return false;
}

The algorithm tries all available options for a cell in order. If no solution works for the rest of the board, the algorithm returns false (for “no solution”).

The algorithm is not how a human would solve Sudoku. But then again, we’re not writing a tutorial on how to solve Sudoku, we’re writing a program that solves Sudoku.
The board

As I implemented the solver, the interface for the board started to emerge. At that point in time, I had to create tests for the Sudoku board itself. A typical test verifies that the board doesn’t allow duplicate values in a row:

@Test
public void shouldDisallowOptionsInSameRow() throws Exception {
    int row = 4;
    board.setValueInCell(1, row, 5);
    board.setValueInCell(2, row, 8);
    board.setValueInCell(3, row+1, 5);
    assertThat(board.getOptionsForCell(row, 0))
            .excludes(1,2).contains(3);
}

The essence of SudokuBoard is finding out what values are legal in an open cell:

public List getOptionsForCell(int row, int col) {
    if (!isEmpty(row,col)) return Arrays.asList(cells[row][col]);
    List result = allOptions();
    removeAllInRow(result, row);
    removeAllInCol(result, col);
    removeAllInBox(result, row, col);
    return result;
}

TDD as a design guide

I invite you to compare Peter Norvig’s solution to mine (you can find the full source code for my solution in my github repository).

It would probably have been possible for me to code the solution faster without tests, but it probably would not have worked the first time I tried it. I also would have much less confidence in the code. Finally, I think the design imposed by the tests made my code easier to understand.

This article is a summary of a seminar I had on the topic. If it seems like it’s a continuation of an existing discussion that’s because, to some extent, it is. If you haven’t been discussing exchanging your app server, this article probably isn’t very interesting to you.

By putting the application server inside my application instead of the other way around, I was able to leap tall buildings in a single bound.

The embedded application server

This is how I build and deploy my sample application to a new test environment (or to production):

1. mvn install
2. scp someapp-server/target/someapp-1.0.onejar.jar appuser@appserver:/home/appuser/test-env1/
3. ssh appuser@appserver "cd /home/appuser/test-env1/ && java -jar someapp-1.0.onejar.jar&"

This require no prior installed software on the appserver (with the exception of the JVM). It requires no prior configuration. Rolling back is a matter of replacing one jar-file with another. Clustering is a matter of deploying the same application several times.

In order to make this work in a real environment, there are a many details you as a developer need to take care of. As a matter of fact, you will have to take responsibility for your operational environment. The good news is that creating a good operational environment is not more time-consuming than trying to cope with the feed and care of a big-a Application Server.

In this scheme every application comes with its own application server in the form of jetty’s jar-files embedded in the deployed jar-file.

The advantages

Why would you want to do something like this?

• Independent application: If you’ve ever been told that you can’t use Java 1.5 because that would require an upgrade of the application server. And if we upgrade the application server, that could affect someone else adversely. So we need to start a huge undertaking to find out who could possibly be affected.
• Developer managed libraries: Similar problems can occur with libraries. Especially those that come with the application server. For example: Oracle OC4J helpfully places a preview version of JPA 1.0 first in your classpath. If you want to use Hibernate with JPA 1.0-FINAL, it will mostly work. Until you try to use a annotation that was changed after the preview version (@Discriminator, for example). The general rule is: If an API comes with your app server, you’re better served by staying away from it. A rather bizarre state of affairs.
• Deployment, configuration and upgrades: Each version of the application, including all its dependencies is packaged into a single jar-file that can be deployed on several application server, or several times on the same application server (with different ports). The configuration is read from a properties-file in the current working directory. On the minus side, there’s no fancy web UI where you can step through a wizard to deploy the application or change the configuration. On the plus side, there is no fancy web UI …. If you’ve used one such web UI, you know what I mean.
• Continuous deployment: As your maven-repository will contain stand alone applications, creating a continuous deployment scheme is very easy. In my previous environment, a cron job running wget periodically was all that was needed to connect the dots. Having each server environment PULL the latest version gives a bit more flexibility if you want many test environments. (However, if you’re doing automated PUSH deployment, it’s probably just as practical for you).
• Same code in test and production: The fact that you can start Jetty inside a plain old JUnit test means that it is ideal for taking your automated tests one step further. However, if you test with Jetty and deploy on a different Application Server, the difference will occasionally trip you. It’s not a big deal. You have to test in the server environment anyway. But why not eliminate the extra source of pain if you can?
• Licenses: Sure, you can afford to pay a few million $ for an application server. You probably don’t have any better use for that money, anyway, right? However, if you have to pay licenses for each test-server in addition, it will probably mean that you will test less. We don’t want that.
• Operations: In my experience, operations people don’t like to mess around with the internals of an Application Server. An executable jar file plus a script that can be run with [start|status|stop] may be a much better match.

The missing bits

Taking control of the application server takes away a lot of complex technology. This simplifies and makes a lot of stuff cheaper. It also puts you back in control of the environment. However, it forces you to think about some things that might’ve been solved for you before:

• Monitoring: The first step of monitoring is simple: Just make sure you write to a log file that is being monitored by your operations department. The second step requires some work: Create a servlet (or a Jetty Handler) that a monitoring tool can ping to check that everything is okay. Taking control of this means that you can improve it: Check if your data sources can connect, if your file share is visible, if that service answers. Maybe add application-calibrated load reporting. Beyond that, Jetty has good JMX support, but I’ve never needed it myself.
• Load balancing: My setup supports no load balancing or failover out of the box. However, this is normally something that the web server or routers in front of the application server anyway. You might want to look into Jetty’s options for session affinity, if you need that.
• Security: Jetty supports JAAS, of course. Also: In all the environments I’ve been working with (CA SiteMinder, Sun OpenSSO, Oracle SSO), the SSO server sends the user name of the currently logged in user as an HTTP header. You can get far by just using that.
• Consistency: If you deploy more than one application as an embedded application server, the file structure used by an application (if any) should be standardized. As should the commands to start and stop the application. And the location of logs. Beyond that, reuse what you like, recreate what you don’t.

Taking control of your destiny

Using an embedded application server means using the application server as a library instead of a framework. It means taking control of your “main” method. There’s a surprisingly small number of things you need to work out yourself. In exchange, you get the control to do many things that are impossible with a big-A Application Server.

Thanks to Dicksen, Eivind, Terje, Kristian and Kristian for a fun discussion on Jetty as a production app server

When picking iteration length for an agile project, there are mainly two forces that you have to balance: The rate of learning is proportional with the number of iterations, rather than the length of the project. This means that shorter iterations help you get better faster. But each iteration has some overhead with sprint reviews, retrospectives and planning. You don’t want this overhead to dominate the effort spent on the project.

For some reason, most projects I’ve seen with little experience in iterative development prefer three week iterations. Personally, I prefer two week iterations. Here is the breakdown:

• Three week iterations: After three months, you’ve spent about 7% of your time on iteration meetings. You’ve had 4 opportunities to improve.
• Two week iterations: After three months, you’ve spent about 10% of your time on iteration meetings. You’ve had 6 opportunities to improve.
• One week iterations: After three months, you’ve spent about 20% of your time on iteration meetings. You’ve had 12 opportunities to improve.

Going from 93% to 90% efficiency for a 50% increase in learning seems like a good deal. Going from 90% to 80% efficiency for a 100% increase in learning, not so much.

These numbers are of course greatly simplified. You might also consider:

• With shorter iterations, the planning time may go down. But this takes practice – it doesn’t happen automatically.
• With very short iterations, you may not have experienced enough to learn much from the retrospective. However, if you find that you do a timeline, and most of the things people remember happened the last week, it may not be because that’s the only time something significant happened.
• You may consider different frequencies for different ceremonies. For example, on my current project we want to have demos with our power users. But they have to travel far to visit us. So we only have a full demo every other four weeks. We plan every two weeks and have an internal review and retrospective every two weeks.

What’s the right iteration length for your project?

If you wonder why this blog has been so quiet lately, it not (just) that I’m getting lazier. Together with several of my colleagues at Steria Norway, I’ve started up a blog at http://sterkblanding.no. “Sterk blanding” is Norwegian for “potent mix”, and we hope that as representatives for several disciplines, we will be able to give a broad perspective on IT and management issues.

I’ve not yet decided what posts to publish here and what posts to publish on Sterk Blanding. My present idea is that I’ll publish most of my English articles here at Thinking inside a Bigger Box and Norwegian language articles at Sterk Blanding. But I can be persuaded to change my mind.

For my Norwegian readers, enjoy my articles on Sterk Blanding:

• Hvordan komme i gang med blogging: Some tips for people who’re considering to write on a blog
• Hemmligheten bak gode spesifikasjoner: How to change your tests from specifying behavior to specify intentions
• “Det var dyrt”-øyeblikket: The sobering moment in my project when both I and my customer realized that the transparency of Scrum meant that we could see how much the project was really costing.
• Hvordan endre en statisk klasse til en dynamisk singleton: A step by step guide to an important strategic level refactoring in legacy systems: Changing static calls to use a singleton that can be mocked.
• Med Ram Yoga: Smidig brukervennlighet: Some practical tips on how to use agile software development and user centric design to complement each other

I’ve pair programmed the Java EE spike kata in Eclipse with a number of people, I’ve found that a number of keyboard short cuts and preference settings recur as useful new information. I’ve compiled the most popular ones in this article. The article is subject to change, but I won’t change the number of shortcuts.

Top five shortcuts

There are some keyboard short cuts that everyone who uses Eclipse should know:

• ctrl-1 (quick fix): You hopefully use this shortcut to get quick fix support for compiler errors and warnings. Did you know that you can also use it to assign parameters to new fields, rename variables, and invert if-statements and equals-checks? Learn to think of ctrl-1 as asking Eclipse “what can you do make the code better (or just different)?”
• ctrl-space (complete): Again, you hopefully know that you can use ctrl-space to complete the name of variables and method names. But did you know that you can type “equa<ctrl-space>” in the class body and have Eclipse override the equals-method for you? Or that you can type “getNam<ctrl-space>” and have Eclipse create the whole implementation of a getter for name (if there’s a name-field in the class). Or that you can type “Test<ctrl-space>” and have Eclipse fill in the Test code template. Think of ctrl-space as asking Eclipse “guess what I’m about to write”
• ctrl-f6 (next editor): Use this to cycle between open files. It really should’ve been bound to ctrl-tab, but you can do this yourself.
• f3 (go to definition): Place the cursor on a method call or variable usage and press f3 to go to it’s definition.
• ctrl-shift-t (open type): A nifty dialog to go to any class in your project. Did you know that typing PerCoT will take you to PersonControllerTest?

Top ten runner ups

Here are some eye-openers that people enjoy learning:

• alt-ctrl-down (copy current line): Creates a new copy of the line under the cursor on the next line. Without wiping the clipboard! Try it while selecting several lines, too
• alt-down (move current line): Moves the line under the cursor down one line. Works with alt-up, too. And with a number of lines selected. A quick way to move code around with the keyboard.
• alt-shift-left (extend selection): Progressively selects a larger syntactic element in the editor. Hard to explain. Try it out!
• ctrl-shift-m (static import): Replace a call to Assert.assertEquals with a static import of the org.junit.Assert.assertEquals and a call to assertEquals.
• ctrl-F11 (rerun latest command): To run for example the same test again, you can usually press ctrl-f11. Sadly, a few years back the Eclipse team tried to improve this and failed. Fix it under Windows->Preferences, Run/Debug -> Launching. Change “Launch operation” to “Always launch previous”.
• f12 (activate editor): When you perform an operation where some other pane got the focus, use f12 to return to the editor again.
• ctrl-N (new <something>): Create a new class, XML file or whatever. Be sure to use the filter
• alt-shift-l (extract local variable): My favorite refactoring. Select an expression and press alt-shift-l to assign it to a local variable and replace all uses of the expression with that variable.
• alt-shift-m (extract method): Your bread and butter refactoring to split up complex logic in understandable units.
• alt-shift-i (inline method/inline variable): The inverse of both alt-shift-l and alt-shift-m. Together, these three refactorings let you resculpt your code while being certain that the behavior is unaltered.

Top three properties to change

When I sit down with new programmers, I almost always help them make the following changes in the preferences. Find the preferences under Window->Preferences:

• Use ctrl-tab (and ctrl-shift-tab) to switch between open editors: Go to General->Keys, type in “next editor” in the filter. Select “Copy command” and type “ctrl-tab” in the Binding field. Do the same for “previous editor” and ctrl-shift-tab.
• Type filter: Do you wonder why Eclipse can’t understand that when you say List, you mean java.util.List, not org.hibernate.mapping.List or (ye gods!) java.awt.List. Well, you can make Eclipse understand. Put classes and packages you don’t like under Java->Appearance->Type filters. If your project is like mine, putting org.hibernate.mapping.*, antlr.*, java.awt.List, and com.sun.* in the list makes List unique to java.util.List. Then “organize imports” and completion works as you want.
• Static import favorites: Do you find yourself using static imports with the same few classes again and again? The preference Java->Editor->Content Assist->Favorites lets you list up classes which will have their static methods checked when you press ctrl-space to complete a method call. org.junit.Assert.* is a good first candidate.

Learning your tool

All IDEs are rich and powerful tools. Spending some time to learn a few new tricks is well worth the effort.

As it turns out, one of the least used practices of agile development is also one of the most powerful.

Up into the start of last year, I only worked sporadically with pair programming. Last year, I was lucky enough to be part of a team that used pair programming all the time. Since I’ve experienced real pair programming, I never want to give it up.

Pair programming offers benefits to many stakeholders:

• As a developer, you will have more fun at work. You will get to know your colleagues better and experience flow practically the whole day. You will be tired by the end of the day, but you will also feel like you’ve accomplished good work.
• The team will have a higher quality code base that everyone is comfortable with.
• As an architect or team lead, you will have a good way to contribute even if you only have a little time before a meeting. You will also have a better chance to influence the rest of the team, instead of just issuing edicts that nobody follows.
• As the project manager, you will have a more flexible team. If someone gets sick, goes on vacation or moves to another project, there won’t be a big problem.
• As the customer, you will get better quality code faster.

With these benefits in mind, why doesn’t everybody pair program? Well, it is unfamiliar, a little scary, and exhausting when you start out. Most developers are not used to having other watch them code. Or to focus on the task at hand the whole day.

Here are some techniques I’ve seen have effect for teams transitioning to pair programming:

• Code dojos: Everyone on the team gets together and programs a sample program or a spike together. Two people sit at the keyboard, while the rest watch on a projector. Rotate pairs frequently. This lets everyone get comfortable with coding as a social activity.
• Pair programming should be the norm, but allow for exceptions. If people only pair program occasionally, they end up not pair programming at all. If people are forced to pair program when they just need some time by themselves to think, they will not be happy pair programming.
• The pair programming star: Write the names of the team members in a circle. Every time two people pair program, draw a line between their names. Keep the pair programming star in a visible location.
• Facilities: The furniture can make it harder to get started pair programming. Consider using two mice, two keyboards and perhaps two monitors per PC to make it easier. Or use VNC for desktop sharing.
• Give it time: Pair programming is exhausting when you first start doing it. It will take a while before people are comfortable with the new pace. But once they switch, they will never want to go back.

Resources

For more inspiration, see these presentations from the Smidig 2009 conference (in Norwegian):

• Jøran Lillesand: Derre e itj smidi!: On why pair programming is the very foundation of successful agile projects
• Ørjan Lillevik og Kari Røssland: Parprogrammering gir driv: On what it feels like to be on a team that adopts pair programming. From reluctance to joy.

After seeing some of the great examples of coders working on practiced problems on KataCasts, I decided to try make my own. I am not happy with the pacing of the video. I’m about a minute too early relative to the music.

But I thought I’d post the video here, to see what you all think. Comments are welcome!

I hope the video will demonstrate how to use refactoring effectively to drive the design of a program.

I chose the FizzBuzz kata – that is, to generate a sequence of numbers where every number divisible by three is replaced by “fizz” and every number divisible by five is replaced by “five”. The music changes to be more aggressive just as I induce a new requirement into the kata: The FizzBuzz generator should be programmable, so, in the kata, numbers divisible by two are replaced by “coconut” and numbers divisible by seven are replaced by “banana”.

Thanks to Emily Bache for the inspiration for the kata.

Enjoy!

Fizz buzz code kata from Johannes Brodwall on Vimeo.

The video was made with IntelliJ IDEA Community Edition on Windows Vista (!) with BB FlashBack Express (free screen recorder), converted to AVI with Windows Media 1 codec and uploaded to Vimeo.

Lately, I’ve been working on two code katas, that is, programming exercises that I repeat until the motions are secure in my muscle memory. The katas I’ve chosen are:

• Java EE Spike: An application that stores People with names to a database and lets me search for them. I’ve repeated this pair programming with several different programmers.
• Programmable Fizz Buzz: Create a sequence of numbers 1,2,fizz,4,buzz,fizz,… you know the one. And the twist: Make it programmable, so that for example numbers divisible by 7 should be replaced with “coconut”.

I’ve learned a lot from repeating these exercises:

• Using test-driven development it takes me longer to get to something that “should work in principle”, but shorter to get to something that works correctly.
• When refactoring to a new data structure, add the new structure while keeping the old one, make switching between them as simple as changing a single line. Delete the old when it works.
• There’s always an automated refactoring you still want to help you out. Extract Parameter Object was my big one.
• Writing for example a method invocation and then using quickfix to have the IDE generate the method is the quickest way of writing code available to you.
• After 8 iterations, the Java EE Spike takes me 80 minutes solo. Pair programming with another programmer who had practiced: 65 minutes. I don’t know why!
• Pair programming a moderately complex kata like the Java EE Spike is fun. It’s also a good chance to discuss different roles of different tests.
• There is a huge difference between a test that takes 3 seconds to run and one that takes 0.5 seconds when you’re test driving. More surprisingly, there’s a big difference between a test that takes 0.5 seconds and one that takes 0.01 seconds
• If you think test-driven development is not for you or that it’s bunk, you probably write really slow tests.

What are your latest coding observations?

For å bli bedre må man trene. For å bli bedre med avanserte ting, må man forstå de grunnleggende tingene bra. For å vite hvorfor man bruker avanserte verktøy, må man prøve å jobbe uten dem. Derfor har jeg de siste ukene trent mange ganger på å lage en veldig enkel webapplikasjon i Java. For hele applikasjonen har jeg startet med å skrive testene før koden som implementerer funksjonaliteten.

Dersom du vil prøve deg på samme øvelse, inneholder denne artikkelen litt informasjon for å komme i gang. Start med koden under og følg feilmeldingene. Send en kommentar dersom du ikke kommer videre fra en feilmelding, så får vi en FAQ.

Oppgaven

Løs et så enkelt som mulig problem som involverer websider og database med så enkel teknologi om mulig.

Oppgaven jeg har laget går ut på å opprette personer med fullt navn og søke etter personer basert på navnet deres. For å gjøre oppgaven så lite som mulig har jeg valgt å la personer kun ha ett informasjonsfelt: Fullt navn. Denne oppgaven tar cirka 2-3 timer uten øvelse og du kan få den ned i 60-90 minutter med trening.

Du kan naturligvis velge en annen oppgave, men uansett hva du velger: Det er mer lærerikt å gjenta den samme oppgaven flere ganger enn å utføre en avansert oppgave.

Når jeg utfører oppgaven er det viktigste jeg lærer meg å forstå feilmeldingene som guider meg gjennom utviklingen. Dersom du trenger hjelp til å komme til de første feilmeldingene kan du se resten av artikkelen.

Steg for steg: Startpunktet

Selv om jeg valgte veldig enkel teknologi for implementasjonen, har jeg valgt et større sett med biblioteker for å skrive testene. Jeg bruker følgende når jeg skriver testene:

• JUnit 4.6
• Jetty 6.1.22
• HSqlDb 1.8.0.10
• WebDriver-HtmlUnit 0.6.1039
• Mockito 1.8.0
• FEST-assert 1.2 (ikke påkrevd, men gjør testene søtere)

Den eneste teknologien jeg har valgt for implementasjonen er Servlet-API 2.5 og Hibernate-Annotations 3.4.0.GA.

For at du skal slippe å plundre så mye med avhengigheter før du kommer i gang har jeg laget en pom.xml-fil som du kan ta utgangspunkt i.

Web-tester

For å starte utviklingen, er det lurt med en test som starter på utsiden av applikasjonen. Noe slikt:

1. Start opp miljøet
2. Legg inn en person
3. Søk etter personen

Slik kommer du i gang med en test som går mot en web applikasjon:

int SERVER_PICKS_PORT = 0;
org.mortbay.jetty.Server server = 
       new org.mortbay.jetty.Server(SERVER_PICKS_PORT);
server.addHandler(
       new org.mortbay.jetty.webapp.WebAppContext("src/main/webapp", "/"));
server.start();
 
int serverPort = server.getConnectors()[0].getLocalPort();
 
org.openqa.selenium.WebDriver browser =
       new org.openqa.selenium.htmlunit.HtmlUnitDriver();
browser.get("http://localhost:" + serverPort + "/");
browser.findElement(By.linkText("Create person"));

Dette oppsettet forventer å finne web.xml-fila på src/main/webapp/WEB-INF/web.xml.

Funksjonell test

En funksjonell test definerer kravene i applikasjonen. Det er lurt å gjøre funksjonelle tester så raske som overhode mulig, samtidig som de går gjennom alle kravene. En funksjonell test trenger ikke være en ende-til-ende test, slik som eksempelet over. Dette er viktig, fordi ende-til-ende tester er ofte veldig trege. Her er noen eksempler på funksjonelle tester:

• Vis en siden for å opprette nye personer
• Opprett en ny person
• Verifiser at personens navn er oppgitt og ikke inneholder ulovlige tegn
• Vis en side for å søke etter personer
• Vis alle personer dersom søkestreng ikke er angitt
• Søk etter angitt søkestreng

En funksjonell test kan se slik ut:

PersonServlet servlet = new PersonServlet();
 
HttpServletRequest req =
    org.mockito.Mockito.mock(HttpServletRequest.class);
HttpServletResponse resp =
    org.mockito.Mockito.mock(HttpServletResponse.class);
 
PersonDao personDao =
    org.mockito.Mockito.mock(PersonDao.class);
servlet.setPersonDao(personDao);
 
org.mockito.Mockito.when(req.getMethod())
    .thenReturn("POST");
org.mockito.Mockito.when(req.getPathInfo())
    .thenReturn("/create.html");
org.mockito.Mockito.when(req.getParameter("full_name"))
    .thenReturn("Johannes Brodwall");
 
StringWriter pageSource = new StringWriter();
org.mockito.Mockito.when(resp.getWriter())
    .thenReturn(new PrintWriter(pageSource));
 
servlet.service(req, resp);
 
org.mockito.Mockito.verify(personDao)
    .create(Person.byName("Johannes Brodwall"));
 
org.fest.assertions.Assertions.assertThat(pageSource.toString())
    .contains("Personen er opprettet");

Data-aksess-test

Hibernate forenkler databasebruken mye. Men Hibernate er selv komplekst og når man bruker det på mer avanserte måter fortjener det egne tester. En typisk test med Hibernate kan være:

1. Legg i tre personer i database
2. Søk etter en del av navnet på en av dem
3. Sjekk at du får tilbake akkurat den du forventet

Når jeg starter med Hibernate, lager jeg en test som dette, og følger feilmeldingene. Pass på å både følge feilmeldinger i loggen og stack tracer.

AnnotationConfiguration conf = new AnnotationConfiguration()
    .setProperty(Environment.URL, "jdbc:hsqldb:mem:persondaotest");
PersonDao dao = new HibernatePersonDao(conf.buildSessionFactory());
 
dao.create(Person.withName("foo"));
 
org.fest.assertions.Assertions.assertThat(dao.find(null))
    .containsExactly(Person.withName("foo"));

Følg feilmeldingene herfra.

Integrasjon

En veldig vanlig måte for web serveren å overlevere spesielt ting som DataSources til applikasjonen er via JNDI. I Jetty kan du gjøre dette i Web-testen på følgende måte:

org.hsqldb.jdbc.jdbcDataSource ds = new org.hsqldb.jdbc.jdbcDataSource();
ds.setDatabase("jdbc:hsqldb:mem:personwebtest");
ds.setUser("sa");
new org.mortbay.jetty.plus.naming.Resource("jdbc/primaryDs", ds);
 
// Oppstart av Jetty som vist over

Konklusjon

Å gjøre en liten øvelse som dette er en god måte å bli bevisst hvilke vaner du har og hvor lang tid det egentlig tar for deg å gjøre oppgavene dine. Du vil oppleve at det å skrive tester før koden føles som om det går saktere enn du tror du er vant til.

Men dersom du er som meg, vil du også oppleve noe annet: Når du tester ut applikasjonen første gang (du kan gjøre dette med Jetty, naturligvis) så er sjansene gode for at den vil være nokså feilfri og at debugging i stor grad er overflødig. Jeg vet ikke med deg, men debugging er en aktivitet jeg gjerne blir kvitt.