Øredev 2013 – What you probably missed

Øredev 2013 was last week, and it was fantastic!

Sharing knowledge

Øredev is in Malmö, Sweden. It’s very close to Copenhagen, so you can fly to there and then take a 20mn train to arrive in Malmö.

It’s a fantastic conference, totally vendor-neutral (that’s very important). It’s big yet friendly, with a mix of well established topics and more experimental ideas. This year the theme was “The Arts”, and as a result it was deliberately provoking or weird in some aspects, and that is a good thing!

For me the highlights of the conference were the radical ideas brought by two guys with some experience in the business: Woody Zuill and Fred George (I’ll come to it in a minute). I also enjoyed a lot how Jessica Kerr @jessitron manages to make alternative ways of thinking more accessible and attractive for developer using mainstream languages like Java or C#. Unfortunately I missed @Bodil talks because the room was too packed to be even able to open the door…

Before the conference you can attend 1-day trainings, and I decided to attend the Value-Driven Product Developmentcourse by JB Rainsberger @jbrains. It’s a very good course, more advanced and probably not for beginners. I knew a lot about BDD and has attended other courses already, yet I still learnt a lot during this workshop. I missed other talks from JB, but I want to watch the videos since I had very good feedbacks from other attendees.

It was interesting to listen to experience reports (New Frontiers For In-House Legal Practice by Kate Sullivan, Data @ King – How we are able analyze 100M DAU by Mats-Olov Eriksson, Curiosity killed the cat, but what kills curiosity?by Ann-Marie Charrett @charrett, Less is more! – when it comes to art and software, by @JimmyNilsson) with anecdotes and honest accounts of successes, failures and evolutions of mindset.

Radical Ideas

The Øredev program committee likes to take risks and challenge the way we think about software, as demonstrated by Woody and Fred talks, but also through the talk Code as a crime scene by Adam Petersen Tornhill @AdamTornhill?. Adam tries to reuse forensic methods used for crime investigations to help on large legacy code bases. He built the tool CodeMaat to visualize likely aggressions on the code base based on these ideas.

More radically, Woody Zuill @WoodyZuill talked about the Mob Programming approach his team has been practicing for some time now. He does not claim you should do the same, and he explains that this approach is just the result of doing more of the good things as found during retrospectives. His team found that working together on one task at a time on one single machine at a time was good, so they decided to do that all the time. You must watch his talks: Mob Programming, A Whole Team Approach (Roy “Woody” Zuill). It includes a time-lapse video and is very interesting. It also challenges the way we think about work. What if what the actual “work” was actually what’s between what we usually call “work”?

Very radical too, Fred Georges @fgeorge52 talked about his approach of Programmer Anarchy, “because that’s what it is”. He’s now replicated the experiment at two different companies including a rather traditional one (the Daily Mail newspaper), and is starting again in yet another. Again he does not claim you should do the same, just that it works for them. Again using the power of retrospectives, they got rid of every role except just the customer and the programmer roles. They don’t use the usual software craftsmanship practices like testing and refactoring. However they take great care of the business domain, just like a trader and a developer working closely together can end up giving suggestions to each other, in both ways. As Fred says: “Power to the programmer!”.

This approach works thanks to the use of Micro-Services. This style of architecture in itself is also a bit radical, with a “rapid”, an ordered bus of all the events of the whole system, and a lot of very small, cohesive, disposable micro-services that listen and publish to the bus. You can copy-paste a service to create another, you can rewrite a service rather than make changes, you can plug your new service directly into production! It may sound chaotic but in my opinion this style is disciplined indeed.

Woody gave another talk No Estimates: Let’s explore the possibilities (Roy “Woody” Zuill). It’s really a beautiful talk thanks to the beautiful illustrations from his wife Andrea. Woody does a great job at making us question our need for estimates, what it really means and how it can harm. More importantly, he suggests that estimates are an obstacle against delivering something truly wonderful!

I was lucky to spend some time talking with Woody and Fred, and what they do is very exciting. It’s a paradox, but both still really follow agile values, despite taking huge liberties with respect to the usual principles and practices. Both Fred and Woody also know a lot about object oriented principles and made sure their teams was skilled in that too. However in each case the experiments are also biased because of the very presence of outstanding developers like Fred or Woody!

Testing is not just checking

Software development requires a mix of many different skills. Some of the important skills revolve around testing. At Øredev you could listen and talk to some of the most notable representatives of the testing community: Heuristics of Testability (James Bach) @jamesmarcusbach, Regression Obsession (Michael Bolton) @michaelbolton, Balancing ATDD, GUI Automation and Exploratory Testing (Michael Larsen) @mkltesthead?, (Curiosity killed the cat, but what kills curiosity? by Ann-Marie Charrett @charrett). Other talks (The Beauty of Minimizing Effort and Maximizing Creativity While Integrating Performance Throughout the Lifecycle by Scott Barber and The Psychology of Testing, by M isko Hevery) were also about testing.

I realized that testing is much much more than just checking facts. There is a whole universe of testing practices that you are probably not even aware of, and most of this universe cannot and should not be automated.

Software development is a creative job!

As part of the theme “The Arts” some talks were not about software development. I really loved the talk Shakespeare in Dev (Thomas Q Brady) and the opening keynote of the second day “The Creativity (R)Evolution” by Denise Jacobs @DeniseJacobs. Denise managed to trigger the desire to write, talk and share insights from many attendees in the room during her keynote!

My talk

I was excited to talk at Øredev on Friday after lunch: Refactor your specs! (Cyrille Martraire) The room was almost full, which may suggest that the topic is of interest for many. As a speaker I loved the professionalism of the staff doing the video, sound and organization all around so that everything runs smooth for everyone. Thanks a lot to you all! Overall my talk was well received and I had many good questions and very good feedback’s. As I said, this talk is just the beginning of a conversation that will go on, so feel free to contribute.

All the Øredev videos are available on this page: http://oredev.org/2013/videos (still not complete at the time of writing), so have fun and enjoy them all! Also have a look at the #oredev hashtag on Twitter for more quotes, and don’t forget to follow me at @cyriux on Twitter!






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A touch of functional style in plain Java with predicates – Part 2

In the first part of this article we introduced predicates, which bring some of the benefits of functional programming to object-oriented languages such as Java, through a simple interface with one single method that returns true or false. In this second and last part, we’ll cover some more advanced notions to get the best out of your predicates.


One obvious case where predicates shine is testing. Whenever you need to test a method that mixes walking a data structure and some conditional logic, by using predicates you can test each half in isolation, walking the data structure first, then the conditional logic.

In a first step, you simply pass either the always-true or always-false predicate to the method to get rid of the conditional logic and to focus just on the correct walking on the data structure:

// check with the always-true predicate
final Iterable<PurchaseOrder> all = orders.selectOrders(Predicates.<PurchaseOrder> alwaysTrue());
assertEquals(2, Iterables.size(all));

// check with the always-false predicate
assertTrue(Iterables.isEmpty(orders.selectOrders(Predicates.<PurchaseOrder> alwaysFalse())));

In a second step, you just test each possible predicate separately.

final CustomerPredicate isForCustomer1 = new CustomerPredicate(CUSTOMER_1);
assertTrue(isForCustomer1.apply(ORDER_1)); // ORDER_1 is for CUSTOMER_1
assertFalse(isForCustomer1.apply(ORDER_2)); // ORDER_2 is for CUSTOMER_2

This example is simple but you get the idea. To test more complex logic, if testing each half of the feature is not enough you may create mock predicates, for example a predicate that returns true once, then always false later on. Forcing the predicate like that may considerably simplify your test set-up, thanks to the strict separation of concerns.

Predicates work so good for testing that if you tend to do some TDD, I mean if the way you can test influences the way you design, then as soon as you know predicates they will surely find their way into your design.

Explaining to the team

In the projects I’ve worked on, the team was not familiar with predicates at first. However this concept is easy and fun enough for everyone to get it quickly. In fact I’ve been surprised by how the idea of predicates spread naturally from the code I had written to the code of my colleagues, without much evangelism from me. I guess that the benefits of predicates speak for themselves. Having mature API’s from big names like Apache or Google also helps convince that it is serious stuff. And now with the functional programming hype, it should be even easier to sell!

Simple optimizations

This engine is so big, no optimization is required (Chicago Auto Show).

The usual optimizations are to make predicates immutable and stateless as much as possible to enable their sharing with no consideration of threading.  This enables using one single instance for the whole process (as a singleton, e.g. as static final constants). Most frequently used predicates that cannot be enumerated at compilation time may be cached at runtime if required. As usual, do it only if your profiler report really calls for it.

When possible a predicate object can pre-compute some of the calculations involved in its evaluation in its constructor (naturally thread-safe) or lazily.

A predicate is expected to be side-effect-free, in other words “read-only”: its execution should not cause any observable change to the system state. Some predicates must have some internal state, like a counter-based predicate used for paging, but they still must not change any state in the system they apply on. With internal state, they also cannot be shared, however they may be reused within their thread if they support reset between each successive use.

Fine-grained interfaces: a larger audience for your predicates

In large applications you find yourself writing very similar predicates for types totally different but that share a common property like being related to a Customer. For example in the administration page, you may want to filter logs by customer; in the CRM page you want to filter complaints by customer.

For each such type X you’d need yet another CustomerXPredicate to filter it by customer. But since each X is related to a customer in some way, we can factor that out (Extract Interface in Eclipse) into an interface CustomerSpecific with one method:

public interface CustomerSpecific {
   Customer getCustomer();

This fine-grained interface reminds me of traits in some languages, except it has no reusable implementation. It could also be seen as a way to introduce a touch of dynamic typing within statically typed languages, as it enables calling indifferently any object with a getCustomer() method. Of course our class PurchaseOrder now implements this interface.

Once we have this interface CustomerSpecific, we can define predicates on it rather than on each particular type as we did before. This helps leverage just a few predicates throughout a large project. In this case, the predicate CustomerPredicate is co-located with the interface CustomerSpecific it operates on, and it has a generic type CustomerSpecific:

public final class CustomerPredicate implements Predicate<CustomerSpecific>, CustomerSpecific {
  private final Customer customer;
  // valued constructor omitted for clarity
  public Customer getCustomer() {
    return customer;
  public boolean apply(CustomerSpecific specific) {
    return specific.getCustomer().equals(customer);

Notice that the predicate can itself implement the interface CustomerSpecific, hence could even evaluate itself!

When using trait-like interfaces like that, you must take care of the generics and change a bit the method that expects a Predicate<PurchaseOrder> in the class PurchaseOrders, so that it also accepts any predicate on a supertype of PurchaseOrder:

public Iterable<PurchaseOrder> selectOrders(Predicate<? super PurchaseOrder> condition) {
    return Iterables.filter(orders, condition);

Specification in Domain-Driven Design

Eric Evans and Martin Fowler wrote together the pattern Specification, which is clearly a predicate. Actually the word “predicate” is the word used in logic programming, and the pattern Specification was written to explain how we can borrow some of the power of logic programming into our object-oriented languages.

In the book Domain-Driven Design, Eric Evans details this pattern and gives several examples of Specifications which all express parts of the domain. Just like this book describes a Policy pattern that is nothing but the Strategy pattern when applied to the domain, in some sense the Specification pattern may be considered a version of predicate dedicated to the domain aspects, with the additional intent to clearly mark and identify the business rules.

As a remark, the method name suggested in the Specification pattern is: isSatisfiedBy(T): boolean, which emphasises a focus on the domain constraints. As we’ve seen before with predicates, atoms of business logic encapsulated into Specification objects can be recombined using boolean logic (or, and, not, any, all), as in the Interpreter pattern.

The book also describes some more advanced techniques such as optimization when querying a database or a repository, and subsumption.

Optimisations when querying

The following are optimization tricks, and I’m not sure you will ever need them. But this is true that predicates are quite dumb when it comes to filtering datasets: they must be evaluated on just each element in a set, which may cause performance problems for huge sets. If storing elements in a database and given a predicate, retrieving every element just to filter them one after another through the predicate does not sound exactly a right idea for large sets…

When you hit performance issues, you start the profiler and find the bottlenecks. Now if calling a predicate very often to filter elements out of a data structure is a bottleneck, then how do you fix that?

One way is to get rid of the full predicate thing, and to go back to hard-coded, more error-prone, repetitive and less-testable code. I always resist this approach as long as I can find better alternatives to optimize the predicates, and there are many.

First, have a deeper look at how the code is being used. In the spirit of Domain-Driven Design, looking at the domain for insights should be systematic whenever a question occurs.

Very often there are clear patterns of use in a system. Though statistical, they offer great opportunities for optimisation. For example in our PurchaseOrders class, retrieving every PENDING order may be used much more frequently than every other case, because that’s how it makes sense from a business perspective, in our imaginary example.

Friend Complicity

Weird complicity (Maeght foundation)

Based on the usage pattern you may code alternate implementations that are specifically optimised for it. In our example of pending orders being frequently queried, we would code an alternate implementation FastPurchaseOrder, that makes use of some pre-computed data structure to keep the pending orders ready for quick access.

Now, in order to benefit from this alternate implementation, you may be tempted to change its interface to add a dedicated method, e.g. selectPendingOrders(). Remember that before you only had a generic selectOrders(Predicate) method. Adding the extra method may be alright in some cases, but may raise several concerns: you must implement this extra method in every other implementation too, and the extra method may be too specific for a particular use-case hence may not fit well on the interface.

A trick for using the internal optimization through the exact same method that only expects predicates is just to make the implementation recognize the predicate it is related to. I call that “Friend Complicity“, in reference to the friend keyword in C++.

/** Optimization method: pre-computed list of pending orders */
private Iterable<PurchaseOrder> selectPendingOrders() {
  // ... optimized stuff...

public Iterable<PurchaseOrder> selectOrders(Predicate<? super PurchaseOrder> condition) {
  // internal complicity here: recognize friend class to enable optimization
  if (condition instanceof PendingOrderPredicate) {
     return selectPendingOrders();// faster way
  // otherwise, back to the usual case
  return Iterables.filter(orders, condition);

It’s clear that it increases the coupling between two implementation classes that should otherwise ignore each other. Also it only helps with performance if given the “friend” predicate directly, with no decorator or composite around.

What’s really important with Friend Complicity is to make sure that the behaviour of the method is never compromised, the contract of the interface must be met at all times, with or without the optimisation, it’s just that the performance improvement may happen, or not. Also keep in mind that you may want to switch back to the unoptimized implementation one day.


If the orders are actually stored in a database, then SQL can be used to query them quickly. By the way, you’ve probably noticed that the very concept of predicate is exactly what you put after the WHERE clause in a SQL query.

Ron Arad designed a chair that encompasses another chair: this is subsumption

A first and simple way to still use predicate yet improve performance is for some predicates to implement an additional interface SqlAware, with a method asSQL(): String that returns the exact SQL query corresponding for the evaluation of the predicate itself. When the predicate is used against a database-backed repository, the repository would call this method instead of the usual evaluate(Predicate) or apply(Predicate) method, and would then query the database with the returned query.

I call that approach SQL-compromised as the predicate is now polluted with database-specific details it should ignore more often than not.

Alternatives to using SQL directly include the use of stored procedures or named queries: the predicate has to provide the name of the query and all its parameters. Double-dispatch between the repository and the predicate passed to it is also an alternative: the repository calls the predicate on its additional method selectElements(this) that itself calls back the right pre-selection method findByState(state): Collection on the repository; the predicate then applies its own filtering on the returned set and returns the final filtered set.


Subsumption is a logic concept to express a relation of one concept that encompasses another, such as “red, green, and yellow are subsumed under the term color” (Merriam-Webster). Subsumption between predicates can be a very powerful concept to implement in your code.

Let’s take the example of an application that broadcasts stock quotes. When registering we must declare which quotes we are interested in observing. We can do that by simply passing a predicate on stocks that only evaluates true for the stocks we’re interested in:

public final class StockPredicate implements Predicate<String> {
   private final Set<String> tickers;
   // Constructors omitted for clarity

   public boolean apply(String ticker) {
     return tickers.contains(ticker);

Now we assume that the application already broadcasts standard sets of popular tickers on messaging topics, and each topic has its own predicates; if it could detect that the predicate we want to use is “included”, or subsumed in one of the standard predicates, we could just subscribe to it and save computation. In our case this subsumption is rather easy, by just adding an additional method on our predicates:

 public boolean encompasses(StockPredicate predicate) {
   return tickers.containsAll(predicate.tickers);
 }Subsumption is all about evaluating another predicate for "containment". This is easy when your predicates are based on sets, as in the example, or when they are based on intervals of numbers or dates. Otherwise You may have to resort to tricks similar to Friend Complicity, i.e. recognizing the other predicate to decide if it is subsumed or not, in a case-by-case fashion.

Overall, remember that subsumption is hard to implement in the general case, but even partial subsumption can be very valuable, so it is an important tool in your toolbox.


Predicates are fun, and can enhance both your code and the way you think about it!


The single source file for this part is available for download cyriux_predicates_part2.zip (fixed broken link)

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A touch of functional style in plain Java with predicates – Part 1

You keep hearing about functional programming that is going to take over the world, and you are still stuck to plain Java? Fear not, since you can already add a touch of functional style into your daily Java. In addition, it’s fun, saves you many lines of code and leads to fewer bugs.

What is a predicate?

I actually fell in love with predicates when I first discovered Apache Commons Collections, long ago when I was coding in Java 1.4. A predicate in this API is nothing but a Java interface with only one method:

evaluate(Object object): boolean

That’s it, it just takes some object and returns true or false. A more recent equivalent of Apache Commons Collections is Google Guava, with an Apache License 2.0. It defines a Predicate interface with one single method using a generic parameter:

apply(T input): boolean

It is that simple. To use predicates in your application you just have to implement this interface with your own logic in its single method apply(something).

A simple example

As an early example, imagine you have a list orders of PurchaseOrder objects, each with a date, a Customer and a state. The various use-cases will probably require that you find out every order for this customer, or every pending, shipped or delivered order, or every order done since last hour.  Of course you can do that with foreach loops and a if inside, in that fashion:

//List<PurchaseOrder> orders...

public List<PurchaseOrder> listOrdersByCustomer(Customer customer) {
  final List<PurchaseOrder> selection = new ArrayList<PurchaseOrder>();
  for (PurchaseOrder order : orders) {
    if (order.getCustomer().equals(customer)) {
  return selection;

And again for each case:

public List<PurchaseOrder> listRecentOrders(Date fromDate) {
  final List<PurchaseOrder> selection = new ArrayList<PurchaseOrder>();
  for (PurchaseOrder order : orders) {
    if (order.getDate().after(fromDate)) {
  return selection;

The repetition is quite obvious: each method is the same except for the condition inside the if clause, emphasized in bold here. The idea of using predicates is simply to replace the hard-coded condition inside the if clause by a call to a predicate, which then becomes a parameter. This means you can write only one method, taking a predicate as a parameter, and you can still cover all your use-cases, and even already support use-cases you do not know yet:

public List<PurchaseOrder> listOrders(Predicate<PurchaseOrder> condition) {
  final List<PurchaseOrder> selection = new ArrayList<PurchaseOrder>();
  for (PurchaseOrder order : orders) {
    if (condition.apply(order)) {
  return selection;

Each particular predicate can be defined as a standalone class, if used at several places, or as an anonymous class:

final Customer customer = new Customer("BruceWaineCorp");
final Predicate<PurchaseOrder> condition = new Predicate<PurchaseOrder>() {
  public boolean apply(PurchaseOrder order) {
    return order.getCustomer().equals(customer);

Your friends that use real functional programming languages (Scala, Clojure, Haskell etc.) will comment that the code above is awfully verbose to do something very common, and I have to agree. However we are used to that verbosity in the Java syntax and we have powerful tools (auto-completion, refactoring) to accommodate it. And our projects probably cannot switch to another syntax overnight anyway.

Predicates are collections best friends

Didn't find any related picture, so here's an unrelated picture from my library

Coming back to our example, we wrote a foreach loop only once to cover every use-case, and we were happy with that factoring out. However your friends doing functional programming “for real” can still laugh at this loop you had to write yourself. Luckily, both API from Apache or Google also provide all the goodies you may expect, in particular a class similar to java.util.Collections, hence named Collections2 (not a very original name).

This class provides a method filter() that does something similar to what we had written before, so we can now rewrite our method with no loop at all:

public Collection<PurchaseOrder> selectOrders(Predicate<PurchaseOrder> condition) {
  return Collections2.filter(orders, condition);

In fact, this method returns a filtered view:

The returned collection is a live view of unfiltered (the input collection); changes to one affect the other.

This also means that less memory is used, since there is no actual copy from the initial collection unfiltered to the actual returned collection filtered.

On a similar approach, given an iterator, you could ask for a filtered iterator on top of it (Decorator pattern) that only gives you the elements selected by your predicate:

Iterator filteredIterator = Iterators.filter(unfilteredIterator, condition);

Since Java 5 the Iterable interface comes very handy for use with the foreach loop, so we’d prefer indeed use the following expression:

public Iterable<PurchaseOrder> selectOrders(Predicate<PurchaseOrder> condition) {
  return Iterables.filter(orders, condition);

// you can directly use it in a foreach loop, and it reads well:
for (PurchaseOrder order : orders.selectOrders(condition)) {

Ready-made predicates

To use predicates, you could simply define your own interface Predicate, or one for each type parameter you need in your application. This is possible, however the good thing in using a standard Predicate interface from an API such as Guava or Commons Collections is that the API brings plenty of excellent building blocks to combine with your own predicate implementations.

First you may not even have to implement your own predicate at all. If all you need is a condition on whether an object is equal to another, or is not-null, then you can simply ask for the predicate:

// gives you a predicate that checks if an integer is zero
Predicate<Integer> isZero = Predicates.equalTo(0);
// gives a predicate that checks for non null objects
Predicate<String> isNotNull = Predicates.notNull();
// gives a predicate that checks for objects that are instanceof the given Class
Predicate<Object> isString = Predicates.instanceOf(String.class);

Given a predicate, you can inverse it (true becomes false and the other way round):


Combine several predicates using boolean operators AND, OR:

Predicates.and(predicate1, predicate2);
Predicates.or(predicate1, predicate2);
// gives you a predicate that checks for either zero or null
Predicate<Integer> isNullOrZero = Predicates.or(isZero, Predicates.isNull());

Of course you also have the special predicates that always return true or false, which are really, really useful, as we’ll see later for testing:


Where to locate your predicates

I often used to make anonymous predicates at first, however they always ended up being used more often so were often promoted to actual classes, nested or not.

By the way, where to locate these predicates? Following Robert C. Martin and his Common Closure Principle (CCP) :

Classes that change together, belong together

Because predicates manipulates objects of a certain type, I like to co-locate them close to the type they take as parameter. For example, the classes CustomerOrderPredicate, PendingOrderPredicate and RecentOrderPredicate should reside in the same package than the class PurchaseOrder that they evaluate, or in a sub-package if you have a lot of them. Another option would be to define them nested within the type itself. Obviously, the predicates are quite coupled to the objects they operate on.


Here are the source files for the examples in this article: cyriux_predicates_part1 (zip)

In the next part, we’ll have a look at how predicates simplify testing, how they relate to Specifications in Domain-Driven Design, and some additional stuff to get the best out of your predicates.

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