An experiment about communication through tests

How effectively communicates our test code? We wanted to know if we were able to recreate a software from its tests alone. The experiment gained us some worthwile insights.

lrg-668-wuerfelRecently, we conducted a little experiment to determine our ability to communicate effectively by only using automatic tests. We wanted to know if the tests we write are sufficient to recreate the entire production code from them and understand the original requirements. We were inspired by a similar experiment performed by the Softwerkskammer Karlsruhe in July 2012.

The rules

We chose a “game master” and two teams of two developers each, named “Team A” and “Team B”. The game master secretly picked two coding exercises with comparable skill and effort and briefed every team to one of them. The other team shouldn’t know the original assignment beforehands, so the briefings were held in isolation. Then, the implementation phase began. The teams were instructed to write extensive tests, be it unit or integration tests, before or after the production code. The teams knew about the further utilization of the tests. After about two hours of implementation time, we stopped development and held a little recreation break. Then, the complete test code of each implementation was transferred to the other team, but all production code was kept back for comparison. So, Team A started with all tests of Team B and had to recreate the complete missing production code to fulfill the assignment of Team B without knowing exactly what it was. Team B had to do the same with the production code and assignment of Team A, using only their test code, too. After the “reengineering phase”, as we called it, we compared the solutions and discussed problems and impressions, essentially performing a retrospective on the experiment.

The assignments

The two coding exercises were taken from the Kata Catalogue and adapted to exhibit slightly different rules:

  • Compare Poker Hands: Given two hands of five poker cards, determine which hand has a higher rank and wins the round.
  • Automatic Yahtzee Player: Given five dice and our local Yahtzee rules, determine a strategy which dice should be rerolled.

There was no obligation to complete the exercise, only to develop from a reasonable starting point in a comprehensible direction. The code should be correct and compileable virtually all the time. The test coverage should be near to 100%, even if test driven development or test first wasn’t explicitely required. The emphasis of effort should be on the test code, not on the production code.

The implementation

Both teams understood the assignment immediately and had their “natural” way to develop the code. Programming language of choice was Java for both teams. The game master oscillated between the teams to answer minor questions and gather impressions. After about two hours, we decided to end the phase and stop coding with the next passing test. No team completed their assignment, but the resulting code was very similar in size and other key figures:

  • Team A: 217 lines production code, 198 lines test code. 5 production classes, 17 tests. Test coverage of 94,1%
  • Team B: 199 lines production code, 166 lines test code. 7 production classes, 17 tests. Test coverage of 94,1%

In summary, each team produced half a dozen production classes with a total of ~200 lines of code. 17 tests with a total of ~180 lines of code covered more than 90% of the production code.

The reengineering

After a short break, the teams started with all the test code of the other team, but no production code. The first step was to let the IDE create the missing classes and methods to get the tests to compile. Then, the teams chose basic unit tests to build up the initial production code base. This succeeded very quickly and turned a lot of tests to green. Both teams struggled later on when the tests (and production code) increased in complexity. Both teams introduced new classes to the codebase even when the tests didn’t suggest to do so. Both teams justified their decision with a “better code design” and “ease of implementation”. After about 90 minutes (and nearly simultaneous), both teams had implemented enough production code to turn all tests to green. Both teams were confident to understand the initial assignment and to have implemented a solution equal to the original production code base.

The examination

We gathered for the examination and found that both teams met their requirements: The recreated code bases were correct in terms of the original solution and the assignment. We have shown that communication through only test code is possible for us. But that wasn’t the deepest insight we got from the experiment. Here are a few insights we gathered during the retrospective:

  • Both teams had trouble to effectively distinguish between requirements from the assignment and implementation decisions made by the other team. The tests didn’t transport this aspect good enough. See an example below.
  • The recreated production code turned out to be slightly more precise and concise than the original code. This surprised us a bit and is a huge hint that test driven development, if applied with the “right state of mind”, might improve code quality (at least for this problem domain and these developers).
  • The classes that were introduced during the reengineering phase were present in the original code, too. They just didn’t explicitely show up in the test code.
  • The test code alone wasn’t really helpful in several cases, like:
    • Deciding if a class was/should be an Enum or a normal class
    • Figuring out the meaning of arguments with primitive values. A language with named parameter support would alleviate this problem. In Java, you might consider to use Code Squiggles if you want to prepare for this scenario.
  • The original team would greatly benefit from watching the reengineering team during their coding. The reengineering team would not benefit from interference by the original team. For a solution to this problem, see below.

The revelation

One revelation we can directly apply to our test code was how to help with the distinction between a requirement (“has to be this way”) and implementator’s choice (“incidentally is this way”). Let’s look at an example:

In the poker hands coding exercise, every card is represented by two characters, like “2D” for a two of diamonds or “AS” for an ace of spades. The encoding is straight-forward, except for the 10, it is represented by a “T” and not a “10”: “TH” is a ten of hearts. This is a requirement, the implementator cannot choose another encoding. The test for the encoding looks like this:


@Test
public void parseValueForSymbol() {
  assertEquals(Value._2, Value.forSymbol("2"));
  [...]
  assertEquals(Value._10, Value.forSymbol("T"));
  [...]
  assertEquals(Value.ACE, Value.forSymbol("A"));
}

If you write the test like this, there is a clear definition of the encoding, but not of the underlying decision for it. Let’s rewrite the test to communicate that the “T” for ten isn’t an arbitrary choice:


@Test
public void parseValueForSymbol() {
  assertEquals(Value._2, Value.forSymbol("2"));
  [...]
  assertEquals(Value.ACE, Value.forSymbol("A"));
}

@Test
public void tenIsRequiredToBeRepresentedByT() {
  assertEquals(Value._10, Value.forSymbol("T"));
}

Just by extracting this encoding to a special test case, you emphasize that you are aware of the “inconsistency”. By the test name, you state that it wasn’t your choice to encode it this way.

The improvement

We definitely want to repeat this experiment again in the future, but with some improvements. One would be that the reengineering phases should be recorded with a screencast software to be able to watch the steps in detail and listen to the discussions without the possibility to interact or influence. Both original teams had great interest in the details of the recreation process and the problems with their tests. The other improvement might be an easing on the time axis, as with the recorded implementation phases, there would be no need for a direct observation by a game master or even a concurrent performance. The tasks could be bigger and a bit more relaxed.

In short: It was fun, challenging, informative and reaffirming. A great experience!

Summary of the Schneide Dev Brunch at 2012-05-27

If you couldn’t attend the Schneide Dev Brunch in May 2012, here are the main topics we discussed neatly summarized.

Yesterday, we held another Schneide Dev Brunch on our roofgarden. The Dev Brunch is a regular brunch on a sunday, only that all attendees want to talk about software development and various other topics. If you bring a software-related topic along with your food, everyone has something to share.

We had to do another introductory round because there were new participants with new and very interesting topics. This brunch was very well attended and rich in information. Let’s have a look at the main topics we discussed:

Agile wording (especially SCRUM)

This was just a quick overview over the common agile vocabulary and what ordinary people associate with them. A few examples are “scrum“, “sprint” and “master”. We agreed that some terms are flawed without deeper knowledge about the context in agile.

Book: “Please Understand Me”

if you are interested in the Myers-Briggs classification of personality types (keywords: are you INTJ, ESTP or INFP?), this is the book to go. It uses a variation of the personality test to classify and explain yourself, your motives and personal traits. And if you happen to know about the personality type of somebody else, it might open your eyes to the miscommunication that will likely occur sooner or later. Just don’t go overboard with it, it’s just a model about the most apparent personality characteristics. The german translation of the book is called “Versteh mich bitte” and has some flaws with typing and layouting errors. If you can overlook them, it might be the missing piece of insight (or empathy) you need to get through to somebody you know.

TV series: “Dollhouse”

As most of us are science fiction buffs and hold a special place in our heart for the series “Firefly”, the TV series “Dollhouse” by Joss Whedon should be a no-brainer to be interested in. This time, it lasted two seasons and brings up numerous important questions about programmability every software developer should have a personal answer for. Just a recommendation if you want to adopt another series with limited episode count.

Wolfpack Programming

A new concept of collaborative programming is “wolfpack programming” (refer to pages 21-26). It depends on a shared (web-based) editor that several developers use at once to develop code for the same tasks. The idea is that the team organizes itself like a pack of wolves hunting deer. Some alpha wolves lead groups of developers to a specific task and the hunt begins. Some wolves/developers are running/programming while the others supervise the situation and get involved when convenient. The whole code is “huntable”, so it sounds like a very chaotic experience. There are some tools and reports of experiments with wolfpack programming in Smalltalk. An interesting idea and maybe the next step beyond pair programming. Some more information about the editor can be found on their homepage and in this paper.

Book: “Durchstarten mit Scala”

Sorry for the german title, but the book in this review is a german introductory book about Scala. It’s not very big (around 200 pages) but covers a lot of topics in short, with a list of links and reading recommendations for deeper coverage. If you are a german developer and used to a modern object-oriented language, this book will keep its promise to kickstart you with Scala. Everything can be read and understood easily, with only a few topics that are more challenging than there are pages for them in the book. The topics range from build to test and other additional frameworks and tools, not just core Scala. This book got a recommendation for being concise, profound and understandable (as long as you can understand german).

Free Worktime Rule

This was a short report about employers that pay their developers a fixed salary, but don’t define the workload that should happen in return. Neither the work time nor the work content is specified or bounded. While this sounds great in the first place (two hours of work a week with full pay, anybody?), we came to the conclusion that peer pressure and intrinsic motivation will likely create a dangerous environment for eager developers. Most of us developers really want to work and need boundaries to not burn out in a short time. But an interesting thought nevertheless.

Experimental Eclipse Plugin: “Code_Readability”

This was the highlight of the Dev Brunch. One attendee presented his (early stage) plugin for Eclipse to reformat source code in a naturally readable manner. The effect is intriguing and very promising. We voted vehemently for early publication of the source code on github (or whatever hosting platform seems suitable). If the plugin is available, we will provide you with a link. The plugin has a tradition in the “Three refactorings to grace” article of the last Dev Brunch.

Light Table IDE

A short description of the new IDE concept named “Light Table”. While the idea itself isn’t new at all, the implementation is very inspirational. In short, Lighttable lets you program code and evaluates it on the fly, creating a full feedback loop in milliseconds. The effects on your programming habits are… well, see and try it for yourself, it’s definitely worth a look.

Inventing on Principles

Light Table and other cool projects are closely linked to Bret Victor, the speaker in the mind-blowing talk “Inventing on Principles”. While the talk is nearly an hour of playtime, you won’t regret listening. The first half of the talk is devoted to several demo projects Bret made to illustrate his way of solving problems and building things. They are worth a talk alone. But in the second half of the talk, Bret explains the philosophy behind his motivation and approach. He provides several examples of people who had a mission and kept implementing it. This is very valuable and inspiring stuff, it kept most of us on the edge of our seats in awe. Don’t miss this talk!

Albatros book page reminder (and Leselotte)

If you didn’t upgrade your reading experience to e-book readers yet, you might want to look at these little feature upgrades for conventional books. The Albatros bookmark is a page remembering indexer that updates itself without your intervention. We could test it on a book and it works. You might want to consider it especially for your travelling literature. This brought us to another feature that classic dead wood books are lacking: the self-sustained positioning. And there’s a solution, too: The “Leselotte” is a german implementation of the bean bag concept for a flexible book stand. It got a recommendation by an attendee, too.

Bullshit-Meter

If you ever wondered what you just read: It might have been bullshit. To test a text on its content of empty phrases, filler and hot air, you can use the blabla-meter for german or english text. Just don’t make the mistake to examine the last apidoc comments you hopefully have written. It might crush your already little motivation to write another one.

Review on Soplets

In one of the last talks on the Java User Group Karlsruhe, there was a presentation of “Soplets”, a new concept to program in Java. One of our attendees summarized the talk and the concept for us. You might want to check out Soplets on your own, but we weren’t convinced of the approach. There are many practical problems with the solution that aren’t addressed yet.

Review on TDD code camp

One of our attendees lead a code camp with students, targeting Test Driven Development as the basic ruleset for programming. The camp rules closely resembled the rules of Code Retreats by Corey Haines and had Conway’s Game of Life as the programming task, too. With only rudimentary knowledge about TDD and Test First, the students only needed four iterations to come up with really surprising and successful approaches. It was a great experience, but showed clearly how traditional approaches like “structured object-oriented analysis” stands in the way of TDD. Example: Before any test was written to help guide the way, most students decided on the complete type structure of the software and didn’t aberrate from this decision even when the tests told them to.

Report of Grails meetup

Earlier last week, the first informal Grails User Group Karlsruhe meeting was held. It started on a hot late evening some distance out of town in a nice restaurant. The founding members got to know each other and exchanged basic information about their settings. The next meeting is planned with presentations. We are looking forward to what this promising user group will become.

Epilogue

This Dev Brunch was a lot of fun and new information and inspiration. As always, it had a lot more content than listed here, this summary is just a best-of. We are looking forward to the next Dev Brunch at the Softwareschneiderei. And as always, we are open for guests and future regulars. Just drop us a notice and we’ll invite you over next time.

Clean Code Developer at your fingertips

You’ve probably already heard about the Clean Code Developer initiative. We’re donating a full spectrum of mousepad designs for your educational support.

We are participants in the Clean Code Developer (CCD) movement. This initiative provides a way to perpetually learn, train, reflect and act on the most important topics of today’s software development by formulating a value system and a learning path. The learning path is subdivided in different grades, associated with colors. Every Clean Code Developer progresses continually through the grades, focussing on the principles and practices of the current grade.

If you want a tongue-in-cheek explanation of what the Clean Code Developer is in one sentence: It’s a sight-seeing tour to the most prominent topics every professional software developer should know. But other than your usual tourist rip-off, you can just stay seated and enjoy another round without ever paying anything except attention.

Visualize it

An important aspect of learning and deliberate practice is proper visualization. We invest a lot of work at our workplace, our software and the interaction with our customers to make things visible. When we reflected on our Clean Code Developer practice, we knew that it lacked visualization.

The proposed equipment for a Clean Code Developer is a desktop background picture, a mousepad with an image of all grades at once and some rubber wristbands in the colors of the grades. The wristbands serve as a reminder and a self-assessment tool. The desktop background picture is nice, but only visible if we don’t perform actual work. This let us concentrate on the mousepad.

Duplicate if necessary

The mousepad is the most prominent “advertising” space on the typical work desktop. We want to advertise the content of our current Clean Code Developer grade to ourself. The combination of these two thoughts is not one mousepad, but one for every grade. Imagine six mousepads in the colors of the grades, displaying your currently most important topics right under your fingertips.

We liked the idea so much that we worked on it. The result is a collection of mousepads for every Clean Code Developer to enjoy.

Iterative design

It took us several full cycles of planning, design, layout and proof-reading to have the first version of mousepads produced. It took only a few hours of real-world testing to start the second iteration to further improve the design. Right now, we are on the third iteration. The first iteration had the five colored CCD grades printed on real mousepads. The second iteration added the mousepad for the white grade and a little stand-up display for the initial black grade. The third iteration incorporated the official Clean Code Developer logo, the website URL and improved some details.

Here are some promotional photos of the five first-iteration mousepads:

This slideshow requires JavaScript.

As you can see, we chose to print ultra-slim mousepads to test if it’s feasible to use them stacked all at once (it isn’t, your mileage may vary) or use them even if you aren’t used to mousepads at all (it depends, really). You might want to print the images onto the mousepad you prefer best.

Do it yourself

Yes, you’ve read it right. We are donating the mousepad images back to the community. You can download everything right here:

All documents are bare of any company logo or other advertising and free for your constructive usage. There is only one catch really: the documents are in german language. This might not be apparent at first because we really like the original english technical terms, but some content might need translation for non-german speakers. If you are interested to produce an all-english version, drop us a line.

Acknowledgements

These mousepads wouldn’t exist without the help and inspiration of many co-workers. First of all, the founders of the Clean Code Developer movement, Ralf Westphal and Stefan Lieser, provided all the content of the mousepads. Without their groundbreaking work, we probably wouldn’t have thought of this. The design and production is owed to Hannegret Lindner from the Hannafaktur, a small graphic design agency. We admire her endurance with our iterative approach. And finally, the initial inspiration sparked in a creative discussion with Eric Wolf and Benjamin Trautwein from ABAS Software AG.

It’s your turn now

We are very curious about your story, photo or action still with the mousepads (or the little stand-up display). You can also just share your thoughts about the whole idea or submit an improvement. We’d love to hear from you.

Depth-first programmers

Depth-first programmers are always busy creating horribly complicated solutions that are somehow off the mark. Here’s why and what to do against it.

Just as there are at least two fundamentally different approaches for searching, namely depth-first and breadth-first search, there are also different types of programmers. The depth-first programmer is a dangerous type, as he is prone to yak shaving and reinvention of the wheel.

The depth-first programmer

Let me try to define the term of a “depth-first programmer” by a little (true) story. A novice java programmer should make some changes to an existing code. To secure his work, he should and wanted to write unit tests in JUnit. He started the work and soon enough, first results could be seen. But when he started to write his tests, the progress notifications stopped. The programmer worked frantically for hours and then days to write what appeared to be some simple data-driven tests.

Finally, the novice java programmer reported success and showed his results. He wrote his tests and “had to extend JUnit a bit to do it right”. Wait, what? Well, in JUnit, the test methods cannot have parameters, but the programmer’s tests needed to be parametrized. So he replaced the part of JUnit that calls the test methods by reflection with an “improved” algorithm that could also inject parameters. His implementation relied on obscure data structures that provided the actual parameter values and only really worked for his needs. The whole mess was nearly intangible, a big bloat and needed most of the development time for the unit tests.

And it was totally unnecessary once you learn about “Parameterized” JUnit4 tests or build light-weight data drivers instead of changing the signature of the test method itself. But this programmer dove deep into JUnit to adjust the framework itself to his needs. When asked about it, he stated that “he needed to pass the parameters somehow”. That’s right, but he choose the most expensive way to do so.

He exhibited the general behaviour of a depth-first programmer: whenever you face a problem, take the first possible solution to a problem you can come up with and work on it without evaluation against other possibilities. And continue on the path without looking back, no matter how long it takes.

Stuck in activism

The problem with this approach should be common sense. The obvious option isn’t always the best or even a good one. You have to evaluate the different possible solutions by their advantages and drawbacks. A less obvious solution might be far better in every aspect but obviousness. Another problem with this approach is the absence of internal warning signs.

Getting stuck is an internal warning sign every programmer understands. You’ve worked your way in a certain direction and suddenly, you cannot advance further. Something blocks your anticipated way of solving the problem and you cannot think of an acceptable way past it. A depth-first programmer never gets stuck this way. No matter how expensive, he will pursue the first thing that brings him closer to the target. A depth-first programmer always churns out code at full speed. Most of it isn’t needed on second thought and can be plain harmful when left in the project. The depth-first programmer will always report progress even when he needs days for a task of minutes. He is stuck in activism.

Progress without guidance

This isn’t a rant about incompetent programmers. Every good programmer knows the situation when you suddenly realize that you’re shaving a yak when all you wanted to do is to add a feature to the code base. This is your self-guidance system regaining consciousness after a period of auto-piloting in depth-first mode. Every programmer behaves depth-first sometimes.

This can be explained with the Dreyfus Model of Skill Acquisition. On the first stage, called “Beginner”, you are simply not capable of proper self-evaluation. You cannot distinguish between good and not so good approaches beforehands or even afterwards. Your expertise in the narrow field of the problem at hand isn’t broad enough to recognize an error even when you are working on the error yourself for prolonged times.

In the Dreyfus Model, a beginner needs external guidance. Somebody with more experience has to point out errors for you and formulate alternatives as clearly and specific as possible. Without external guidance, a beginner will become a depth-first programmer. We’ve all been there.

 Be a guide

The real failure in the story above was done by me. Instead of interacting with the novice java programmer after a few hours when I thought he should be done by now, I let him “advance”. I could have avoided the resulting mess by providing guidance and a few alternate solutions for the immediate problem. I would give an overview of the problem’s context and some hints about the general direction this task should be solved.

Every depth-first programmer works in a suboptimal environment. The programmer tries his best, it’s really the environment that could do better.

So, the next time you see somebody working frantically on a problem that should be rather easy to solve, lend him a hand. Be gentle and empathic about his attempt and work with proposals, not with instructions. Perhaps you’ve spared yourself a mess like an unnecessarily extended JUnit library and the depth-first programmer the frustration when his hard work of several days is silently discarded.

How I met my coding style

One of my students recently asked me where I got my coding style from. This blog post tries to answer that really good question.

One of my students recently asked a question that really stuck in my brain: “Where did you get your coding style from?”. The best part of the question was that I didn’t have an answer, until now. I care about coding style a lot and try to talk about it, too. Here are some coding style related blog posts to give you some examples:

Code squiggles were a crazy idea that really provided readability value even after the initial excitement was gone.

Readable code means that you can read the code out loud and it makes sense to (nearly) everyone.

The student wanted to know how to gather the experience to write readable, elegant or just plain crazy code. The answers he anticipated were books or “trial and error”. I’ve come to the conclusion that, while both sources provided enormous amounts of inspiration and knowledge, there’s another single vital ingredient that rounds up the mix: care.

The simple answer to the question is that I cared enough about coding style to gather some knowledge in this field. The more adequate answer is that a lot of factors helped me to improve my coding style.

Books with code examples

Early in my career, I was always looking for programming books with lots of code examples. This is a typical pattern for the novice and advanced beginner stages in the Dreyfus model of skill acquisition. I felt confident when I understood a piece of code and knew that I could produce something similar, given the proper amount of time.

It took a few years of exposure to actual real-life programming to discover that most code examples in books are rubbish. The code exists only as a “textbook example” for the given problem, it isn’t meant to be actually used outside the (often narrow) context. If you stick to copy&paste programming, your code will have the appeal of a patchwork clothing made out of rags. It might fulfill the requirement, but nothing more. You can’t learn style from programming books alone. This isn’t the fault of the books, by the way. Most programming books aren’t about style, but about programming or solving programmer’s problems.

There are a few precious exceptions from the general trend of bad code snippets. For example, Robert C. Martin’s “Clean Code” is a recent book with mostly well-done code listings. The best way to separate ugly code from nice one in books is to re-read them a few years later and try to improve the printed source code.

I encourage you to read as many books about programming as you can possibly handle. Even the bad ones will have an impact and inspire you in some way or the other. Books are like mentors, whereas good books are good mentors. Reading a programming book again after some time will show you how much knowledge you gained in the meantime and how much there’s still to be discovered.

Other people’s source code

The large amount of open source software available to be read in the original version is a gift to every programmer. You can dissect the source code of rock star programmers, scroll over the vast textual deserts of “enterprise code” or digest little gems of pure genius inside otherwise rather dull projects, all without additional cost except the time and attention you’re willing to invest.

When you reach a level of code reading when other people’s code “opens up” to you and you start to see the “deeper motives” or the “bigger picture”, whatever you call it, that’s a magical moment. Suddenly, you don’t read other people’s code, but other people’s minds as they lay out the fundamentals of their software. You’ll begin to read and understand code in larger and larger blocks and see structures that were right there before, but unbeknownst to you.

I doubt you can gain this ability by working with textbook examples, as they are restricted in scope by the very nature of limited print space and a specific topic. The one book that accomplishes something equivalent is “Growing Object-Oriented Software, Guided by Tests” by Steve Freeman and Nat Pryce. It’s a rare gem of truly holistic code examples mixed with the essence of many years of experience.

Other people

As beneficial as reading other people’s source code is, talking with them about it raises the whole experience to another level. If you can get in synch well enough to get past the initial buzzword bombing to show off your leetness, you can exchange coding experience worth many weeks in just a few minutes. And while you are talking with them, why not grab a notebook and type away your ideas?

Most programmers I’ve met, regardless of their skill level, could teach me something. And I’ve had several enlightening moments of novice programmers pointing out something or asking a question in a way that really inspired me. You’ll have to listen in order to learn, as painful or overwhelming as it may be sometimes.

Most people are very shy and insecure about their abilities. Encourage them to tell you more and show your interest in their work. Several noteworthy elements of my coding style were adopted late at night at a bar, when a fellow programmer finally had enough beer to brag about his code.

Own achievements

This one will hurt. Remember the times when you shouted out loud “what the f**k?” over some idiot’s source code? Let this idiot be yourself some months or a year ago. Read your own code. Refactor your own code. Rewrite your own code. It’s the same proceeding our brain performs when we dream at night: It rehashes old memories and experiences and lives through it again, in time lapse mode. As long as you don’t dream about programming (I’ve started to code in my dreams very early and it keeps getting more abstract over the time), the only way to rehash your old code it to live through it again by working with it again.

You’ll put many past achievements into perspective, remembering how proud you were and being embarrassed now. That’s part of the learning process and really shows your progress. Just be aware that today’s triumph will undergo the same transformation sooner or later.

Practice is a big part of gaining experience. And practicing means playing around, making errors, trying crazy new things and generally questioning everything you’ve learnt so far. Try to allocate as much practicing time as you can get (besides reading those books!) and really hone your skills. This works exceptionally well with other people, at a code camp, a code retreat, a hackathon, whatever you call it.

The right mindset

This is the secret ingredient to all the things listed above. Without the right mindset, everything in addition to your day-to-day work will appear like a chore. This doesn’t mean that you’ll have to sacrifice all your spare time to improve your coding style. It just means that you won’t mind reading a good book about programming at the weekend if you’re really enjoying it. You cannot learn this attitude from books or even blog posts, it’s a passion that you’ll have to develop on your own.

I can try to describe a big part of my passion: Nothing is good enough. There is never a “good enough”. I’m not falling into despair over it, it’s just an endless challenge for me. Tomorrow, I will be a little bit better than today. But even tomorrow, I’m not “good enough” and can still improve. Sometimes, my improvement rate is neglectable for a long time when suddenly an inspiration completely rearranges my (programming) world.

I made my last giant leaps in coding style when I deliberately avoided parts of my usual habits during programming and tried to focus on what I really wanted to do right now and how to express it in the best way possible (for me). The result was astonishing and humbling at the same time: there’s so much knowledge to gain, there’s so little I know. But yet, I keep getting better and that really makes me complete.

Old code: The StringChunker

This is a little story about a single piece of (java) code: Why it got written, how it got used, what happened after the initial usage and where it is today. At the end, you’ll get the full source code and a brainteaser.

This will be a little story about a single piece of code: Why it got written, how it got used, what happened after the initial usage and where it is today. At the end, you’ll get the full source code and a brainteaser.

Prelude

In the year 2004, a long-term customer asked us to develop a little data charting software for the web. The task wasn’t very complicated, but there were two hidden challenges. The first challenge was the data source itself that could have outages for various reasons that each needed to be addressed differently. The second, more subtle challenge was a “message from the operator” that should be displayed, but without the comments. Failing to meet any of these challenges would put the project at risk of usability.

On a side note, when the project was finished, the greatest risk to its usability wasn’t these challenges, but some assumptions made by the developers that turned out wrong, without proper test coverage or documentation. But that’s fodder for another blog post.

Why it got written

When addressing the functionality of the “message from the operator”, we developed it in a test-first manner, as the specification was quite clear: Everything after the first comment sign (“#”) must never be displayed on the web. Soon, we discovered a serious flaw (let’s call it a bug) in the java.util.StringTokenizer class we used to break down the string. Whenever the comment sign was the first character of the string, it just got ignored. This behaviour is still present with today’s JDK and will not be fixed, as StringTokenizer is a legacy class now:

public class LeadingDelimiterBug {
@Test
public void ignoresLeadingDelimiter() throws Exception {
StringTokenizer tokenizer = new StringTokenizer("#thisShouldn'tBeShown", "#");
assertEquals("", tokenizer.nextToken());
assertEquals("thisShouldn'tBeShown", tokenizer.nextToken());
}

String.split() wasn’t available in 2004, so we had to develop our own string partitioning functionality. It was my task and I named the class StringChunker. The class was born on a monday, 21.06.2004, coincidentally also the longest day of the year. I remember coding it until late in the night.

How it got used

The StringChunker class was developed test-first and suffered from feature creep early on. As it was planned as an utility class, I didn’t focus on the requirements at hand, but thought of “possibly needed functionality” and implemented those, too. The class soon had 9 member variables and over 250 lines of code. You could toggle between four different tokenizing modes like “ignore leading/trailing delimiters”, which ironically is exactly what the StringTokenizer does. The code was secured with tests that covered assumed use cases.

Despite the swiss army knife of string tokenizing that I created, the class only served to pick the comment apart from the payload of the operator’s message. If the special case of a leading comment sign would have been declared impossible (or ruled out beforehands), the StringTokenizer would have done the job just as good. Today, there is String.split() that handles the job decently:

public class LeadingDelimiterBug {
@Test
public void ignoresLeadingDelimiterWithSplit() throws Exception {
String[] tokens = "#thisShouldn'tBeShown".split("\\#");
assertEquals("", tokens[0]);
assertEquals("thisShouldn'tBeShown", tokens[1]);
}
 

But the StringChunker in summer 2004 was the shiny new utility class for the job. It got included in the project and known to the developers.

What happened afterwards

The StringChunker was a success in the project and soon was adopted to virtually every other project in our company. Several bugs and quirks were found (despite the unit tests, there were edge cases) and fixed. This lead to a multitude of slightly different implementations over the years. If you want to know what version of the class you’re using, you need to look at the test that covers all bugfixes (or lacks them).

Whenever one of our developers had to chop a string, he instantly imported the StringChunker to the project. Not long after, the class got promoted to be part of our base library of classes that serves as the foundation for every new project. Now the StringChunker was available like every class of java.lang or java.util and got used like a commodity.

Where it is today

When you compare the initial implementation with today’s code, there really isn’t much difference. Some methods got rewritten to conform to our recent taste of style, but the core of the class still is a hopeless mess of 25-lines-methods and a mind-boggling amount of member variables and conditional statements. I’m still a little bit ashamed to be the creator of such a beast, even if it’s not the worst code I’ve ever written (or will write).

The test coverage of the class never reached 100%, it’s at 95% with some lines lacking a test. This will be the topic of the challenge at the end of this blog post. The test code never got enough love to be readable. It’s only a wall of text in its current state. We can do better than that now.

The class is so ubiquitous in our code base that more than a dozen other foundation classes rely on it. If you would delete the class in a project of ours, it would definitely fall apart somewhere crucial. This will be the most important point in the conclusion.

The source

If you want to have a look at the complete source of the StringChunker, you can download the zip archive containing the compileable sources from our download server. Please bear in mind that we give out the code for educational purpose only. You are free to adapt the work to suit your needs, though.

An open question

When you look at the test coverage, you’ll notice that some lines aren’t tested. We have an internal challenge for several years now if somebody is able to come up with a test that covers these lines. It might be possible that these lines aren’t logically reachable and should be deleted. Or our test harness still has holes. The really annoying aspect about this is that we cannot just delete the lines and see what happens. Most of our ancient projects lack extensive test coverages, and even if they are tested, there could be a critical test missing, allowing the project to pass the tests but fail in production. It’s just too dangerous a risk to take.

So the challenge to you is: Can you provide test cases that cover the remaining lines, thus pushing the test coverage to 100%? I’m very eager to see your solution.

Conclusion

The StringChunker class is a very important class in our toolset. It’s versatile and well tried. But it suffered from feature creep from the very first implementation. There are too many different operation modes combined in one class, violating the Single Responsibility Principle and agglomerating complexity. The test coverage isn’t perfect, leaving little but enough room for speculative functionality (behaviour you might employ, presumably unaware of the fact that it isn’t guaranteed by tests). And while the StringChunker code got micro-refactored (and improved) several times over the years, the test code has a bad case of code rot, leaving it in a state of paralysis. Before the production code is changed in any manner, the test code needs to be overhauled to be readable again.

If I should weight the advantages provided by this class to the disadvantages and risks, I would consider the StringChunker a legacy risk. It might even be a technical debt, now that String.split() is available. The major pain point is that this class is used way too often given its poor code quality. With every new usage, the direct or assumed cost of code change rises. And the code has to change to comply to our current quality standards.

Finale

This was my confession about “old code” in a blog post series that was started by Volker with his blog post “Old Code”. As a personal statement: I’m embarrassed. I can vividly remember the feeling of satisfaction when this beast was completed. I’m guilty of promoting the code as a solution to every use case that could easily be implemented with a StringTokenizer or a String.split(), just because it is available, too and it contains my genius. After reviewing the code, I hope the bigger genius lies within avoiding the class in the future.

Bear up against static code analysis

If you ever had the urge to switch off a rule in your static code analysis tool, this article tries to convince you not to do it. By accepting challenges presented by your tools, you become a better developer and clean up your code on the run.

One of the first things we do when we join a team on a new (or existing) project is to set up a whole barrage of static code analysis tools, like Findbugs, Checkstyle or PMD for java (or any other for virtually every language around). Most of these tools spit out tremendous amounts of numbers and violated rules, totally overwhelming the team. But the amount of violations, (nearly) regardless how high it might be, is not the problem. It’s the trend of the violation curve that shows the problem and its solution. If 2000 findbugs violations didn’t kill your project yet, they most likely won’t do it in the future, too. But if for every week of development there are another 50 violations added to the codebase, it will become a major problem, sooner or later.

Visibility is key

So the first step is always to gain visibility, no matter how painful the numbers are. After the initial shock, most teams accept the challenge and begin to resolve issues in their codebase as soon as they appear and slowly decrease the violation count by spending extra minutes with fixing old code. This is the most valuable phase of static code analysis tools: It enables developers to learn from their mistakes (or goofs) without being embarrassed by a colleague. The analysis tool acts like a very strict and nit-picking code review partner, revealing every flaw in the code. A developer that embraces the changes implied by static analysis tools will greatly accelerate his learning.

But then, after the euphoric initial challenges that improve the code without much hassle, there are some violations that seem hard, if not impossible to solve. The developer already sought out his journey to master the tool, he cannot turn around and just leave these violations in the code. Surely, the tool has flaws itself! The analysis brought up a false positive here! This isn’t faulty code at all, it’s just an overly pedantic algorithm without taste for style that doesn’t see the whole picture! Come to think about it, we have to turn off this rule!

Leave your comfort zone

When this stage is reached, the developers have a deep look into the tool’s configuration and adjust every nut and bolt to their immediate skill level. There’s nothing wrong with this approach if you want to stay on your skill level. But you’ll miss a chance to greatly improve your coding skills by allowing the ruleset to be harder than you can cope with now. Over time, you will come up with solutions you now thought are impossible. It’s like fitness training for your coding skills, you should raise the bar every now and then. Unlike fitness training, nobody gets hurt if the numbers of your code analysis show more violations than you can fix up right now. The violations are in the code, if you let them count or not.

Once, a fellow developer complained really loud about a specific rule in a code analysis tool. He was convinced that the rule was pointless and should be switched off. I asked about a specific example where this rule was violated in his code. When reviewing the code, I thought that applying the rule would improve the code’s internal structure (it was a rule dealing with collapsible conditional statements). In the discussion on how to implement the code block without violating the rule, the real problem showed up – my colleague couldn’t think about a solution to the challenge. So we proceeded to implement the code block in a dozen variations, each without breaking the rule. After the initial few attempts that I had to lead program for him, he suddenly came up with even more solutions. It was as if a switch snapped in his head, from “I’m unable to resolve this stupid rule” to “Hey, if we do it this way, we even can get rid of this local variable”.

Embrace challenges

Don’t trick yourself into thinking that just because your analysis tool doesn’t bring up these esoteric violations anymore after you switched off the rules, they are gone. They are still in your code, just hidden and without your awareness. Bear up against your analysis tool and fix every violation it brings you, one after the other. The tools aren’t there to annoy you, they want to help you stay clear of trouble by pointing out the flaws in a clear and precise manner. Once you meet the challenges the tool presents you with, your skill level will increase automatically. And as a side effect, your code becomes cleaner.

Beyond clean code

Even if every analysis tool approves your code as being clean, it can still be improved. You might have a look at Object Calisthenics or similar code training rulesets. They work the same way as the analysis tools, but without the automatic enforcement (yet). The goal is always cleaner code and higher skilled developers.

Fluent code – challenge your compiler

Learn how to leverage the abilities of your compiler to achieve highly readable code in Java (and probably other similar languages).

Making code more readable, that is, easier to read and therefor easier to grasp, has always been an important secondary goal for me when writing code. The primary goal is correctly working software, but immediately after the code works, it enters maintainance mode. Refactoring is a great tool to improve the structure and accessibility of existing code, but it doesn’t necessarily lead to code that is more readable. I’ve even found that there are multiple levels of “easily accessible” code, depending on your experience with different code structures. But that’s another topic for another blog post.

Readable code

Before I can talk about how to create readable code, I have to define what “readable” means to me: I see readable code as code everybody can read (out loud) and directly understand without further reference.

Here’s an example of a little code snippet in Java that follows my definition:

ForeachFile.in(directory).checkIf(IsOlder.than(5).days());

If you replace the parentheses and dots with whitespace, you can read the line fluently and gain a proper idea of what it is doing.

I’ve always found it much easier to write code similar to this example in dynamic languages. In Groovy, Scala or Perl, you are used to invent your own domain specific language dialect that’s much more readable and concise than using the underlying API directly with all the tedious details. But with a bit of practice, Java (and other statically typed languages) are nearly as flexible to reach (or get near) the highest level of readability: code in natural language.

Start with a sentence

The easy way to accomplish the really challenging task of matching computer programming language and naturally spoken language is to pass it on to the compiler. Start with the desired behaviour of a line of code written as a sentence. The compiler will raise all kinds of objections against this form of programming, and all you have to do is to follow the compiler’s complaints, add some special characters and camel casing and then fill out the classes and methods you just planned ahead.

In reality, it will not be as easy as outlined above, but the process stays the same:

  1. Write your desired code, neglecting all compiler errors
  2. Identify method calls, method parameters, class names and other language features as it fits best
  3. Outline the next code you’ll have to write by silencing the compiler with code stubs (use the code generation features of your IDE)
  4. Fill out the (empty) spots you just created, starting with point 1.

Your first attempts might not be as successful as hoped, so you have to backtrack and adjust for perfectly fluent code to a slightly less perfect form, but that’s just reasonable. You still came up with the possibly most readable code you were able to write.

Know when to stop

Although the process seems to be indefinitely repeatable as you descend deeper and deeper in your code (assuming you started with rather high-level code), there will be a fine line when you have to stop the process because the technical aspects of your code will overwhelm every attempt to wrap natural language around it. You probably still have a good amount of perfectly readable code that even non-programmers can grasp at first sight. Just if you dig deeper into its details, the readability will fade.

Your code will be partitioned into two regions: One region is meant to be read, understood and adjusted if requirements change. The other part of the code isn’t as readable and exists mostly to support the first type of code. This is where you still have to be a programmer to make a change. I assume that your partitioning will meander on the border between business requirements and technical implementation.

Observations along the way

My experimentation phase with this kind of programming revealed some insights that mostly other developers made intuitively when exposed to this style in pair programming sessions.

The most interesting revelation was that the names of my classes change: Instead of using nouns, I tend to use verbs in combination with prepositions (like CheckThat, CreateSome or WaitUntil). This is unfamiliar when reading the class name in isolation, but won’t bother you if you read it in the context of the use case.

Which brings me to the next revelation: The resulting code from the abovementioned process seems to be highly focussed on the current use case. It’s not that it isn’t modifiable or inflexible, but it will serve the task at hand in the best way and fall somewhat short for other use cases. It’s in the ability of the developer to refactor the code once additional use cases appear.

Due to the structuring the natural language imposes on the code, refactorings seem to have a “scope” that can verify if the change at hand is really suitable to bring the code forward. It will be very obvious if a refactoring breaks the ruling structure of the code – the readability of your code will degrade.

Another example

Here is another example of readable code written by the process described above, this time copied from an acceptance test:

station.currentPackage().withTypicalContent().send();
WaitUntilPackage.from(stationName).isProcessedWithin(
    Wait.LONGER).asShownOn(center().statusbar());
Wait.SHORT.perform();
assertThatFilesAreStoredInArchive();
assertThatFilesAreStoredOn(ftpSpace, with(exportName));

You can see that it aren’t always the classnames that drive the code, method names are just as important. And you can see the fitting usage of a code squiggle in the last line, a technique I often use to squeeze in the last missing pieces of fluency.

Summary

Writing readable code that can be read and understood by virtually everyone is a tough task. The programming cycle presented in this article uses the compiler’s ability to complain and the feature of modern IDE to create code stubs (named “quick fixes” or alike) to outline naturally readable code and then fill out the gaps in the best attempt. The result will be code that looks like plain english for the most important parts of the code, the translation of the business requirements. The downside is slightly unusual naming and structure in the other parts of your code.

If you have experiences with this approach to readable code, let us know about it.

Podcasts

Podcasts are a very good means to shorten your commute, to keep you entertained during otherwise boring house-keeping activities, or, if you’re into sports, during your training sessions. Here is a list of some of my favourite shows.

This Developer’s Life

Rob Conery and Scott Hanselman interview developers and other IT professionals who share their stories. Very interesting, very well edited and flavoured with some nice pieces of music.

TechZing

Basically, TechZing are two guys, Jason Roberts and Justin Vincent, who discuss different topics concerning their lives as freelance web developers and startup bootstrappers. They enjoy themselves very much just talking to each other which is very entertaining already. The occasional interview and panel shows are then the icing on the cake.

It’s impossible to give a clear range of  topics since they consist of technical stuff like ‘how to store images in web applications’, SEO, NoSQL, JavaScript and iPhone development, but also non IT stuff like Pioneer One, geological challenges, and the Luck-Surface-Area. Edutainment at its best! Highly recommended!

Software Engineering Radio

This is purely an interview show which addresses all sorts of topics of interest for professional software developers: languages, platforms, technologies, methodologies, etc. Very informative, high profile guests and very competent hosts. Unfortunately, the output rate has gone down a lot in the last year.

Software ArchitekTOUR Podcast

This german (with little bits of swabian) speaking podcast is mostly concerned with topics around software architecture (as the name already suggests). DSLs, NoSQL databases and REST have been some of the latest topics.

FLOSS Weekly

Randal Schwartz (mostly) and other hosts are talking about Free Libre Open Source Software projects, ranging from whole OSes like CentOS to smaller niche projects like Ledger. Great show if you want to know what’s going on in the Open Source world.

Security Now

Steve Gibson and Leo Laporte talk about everything related to IT security. This will keep you informed about the latest browser vulnerabilities, Adobe Flash updates and Windows patches. But you will also learn e.g. how SSL works, the details of Stuxnet and everything about BitCoins. Don’t miss the all-time favourite episode 248: The Portable Dog Killer.

What are your favourite shows?

A tale of scrap metal code – Part III

The third and last part of a series about the analysis of a software product. This part tries to give some rules of thumb on how to avoid failure like in this project.

In the first part of this tale about an examined software project, I described the initial situation and high-level observations about the project. The second part dove into the actual source code and pointed out what’s wrong on this level. This part will summarize everything and give some hints on how to avoid creating scrap metal code.

About the project

If you want to know more about the project, read the first part of this tale. In short, the project looked like a normal Java software, but unfolded into a nightmare, lacking basic requirements like tests, dependency management or continuity.

A summary of what went wrong

In short, the project failed in every respect except being reasonable functional and delivering business value to the customers. I will repeat this sentence soon, but let’s recall the worst parts again. The project had no tests. The project modularization was made redundant by circular dependencies and hardwired paths. No dependency management was in place, neither through the means of a build tool nor by manual means (like jar versions). The code was bloated and overly complex. The application’s data model was a widely distributed network of arbitrary collections with implicit connections via lookup keys. No effort was spent to grasp exception handling or multithreading. The cleverness was rather invested into wildcard usage of java’s reflection API capabilities. And when the cleverness of the developer was challenged, he resorted to code comments instead of making the code more accessible.

How can this be avoided?

First, you need to know exactly what it is you want to avoid. Let me repeat that the project was sold to happily paying customers who gained profit using it. Many software projects fail to deliver this utmost vital aspect of virtually every project. The problem with this project isn’t apparent yet, because it has a presence (and a past). It’s just that it has no future. I want to give some hints how to develop software projects with a future while still delivering business value to the customer.

Avoid the no-future trap

http://www.istockphoto.com/stock-photo-5407438-percent-blocks.phpThe most important thing to make a project future-proof is to restrain yourself from taking shortcuts that pay off now and need to be paid back later. You might want to believe that you don’t need to pay back your technical debts (the official term for these shortcuts) or that they will magically disappear sometimes, but both scenarios are quite unlikely. If your project has any chance to keep being alive over a prolonged amount of time, the technical debts will charge interest.

Of course you can take shortcuts to meet tight deadlines or fit into a small budget. This is called prototyping and it pays off in terms of availability (“time to market”) and scope (“trial version”). Just remember that a prototype isn’t meant for production. You definitely need the extra time and/or budget to fix the intentional shortcomings in the code. You won’t feel the difference right now (hey, it works, what else should it do?), but it will return with compound interest in a few years. The project in this tale was dead after three years. The technical debt had added up beyond being repairable.

Analyzing technical debts

It’s always easy to say that you should “do it right” in the first place. What could the developer for project at hands have done differently to be better off now?

1. Invest in automated tests

When I asked why the project has no tests at all, the developer replied that “it surely would be better to have tests, yet there was no time to write them“. This statement implies that tests take more time to write than they save acting as a guideline and a safety net. And it is probably true for every developer just starting to write tests. You will feel uncomfortable, your tests will be cumbersome and everything will slow down. Until you gain knowledge and experience in writing tests. It is an investment. It will pay off in the future, not right now. If you don’t start now, there will be no future payout. And even better: now your investment, not your debt, will accumulate interest. You might get used to writing tests and start being guided by them. They will mercilessly tell you when your anticipated solution is overly complex. And they will stay around and guard your code long after you forgot about it. Tests are a precaution, not an afterthought.

2. Review and refactor your code

The project has a line count of 80,000 lines of ugly code. I’m fairly confident that it can be reduced to 20,000 lines of code without losing any functionality. The code is written with the lowest possible granularity, with higher concepts lurking everywhere, waiting to be found and exposed. Of course, you cannot write correct, concise and considerate code on your first attempt. This is why you should revisit old code in a recurring manner. If you followed advice number one and brought your tests in place, you can apply every refactoring of the book’s catalog and still be sure that you rather fixed this part instead of breaking it. Constantly reviewing and refactoring your code has the additional advantage of a code base that gets more proficient alongside yourself. There are no “dark regions” (the code to never be read or touched again, because it hurts) if you light them up every now and then. This will additionally slow you down when you start out, but put you on afterburner when you realize that you can rescue any code from rotting by applying the refactoring super-powers that you gained through pratice. It’s an investment again, aiming at midterm return of investment.

3. Refrain from clever solutions

The project of this tale had several aspects that the developer thought were “clever”. The only thing with “clever” is that it’s a swearword in software programming. Remember the clever introduction of wildcard runtime classloading to provide a “plugin mechanism”? Pure poison if you ever wanted your API to be stable and documented, just like a plugin interface should be. Magic numbers throughout your code? Of course you are smart enough to handle this little extra obfuscation. Except when you aren’t. You aren’t sure how exception handling works? Be clever and just “empty catch Exception” everywhere the compiler points you to. In this project, the developer knew this couldn’t be the right solution. Yet, he never reviewed the code when he one day knew how to handle exceptions in a meaningful manner. Let me rest my case by stating that if you write your code as clever as you can handle it, you won’t be able to read it soon, as reading code is harder than writing it.

Summary

Over the course of this tale, you learned a lot about a failed project. In this article, I tried to give you some advice (in the form of three basic rules) on how this failure could probably have been avoided. Of course, the advice isn’t complete. There is much more you could do to improve yourself and your project. Perhaps the best self-training program for developer skills is the Clean Code Developer Initiative (it’s mostly german text yet, so here is an english blog post about it), based upon the book “Clean Code” by Robert C. Martin (Uncle Bob).

Invest in the future of your project and stay clean.