Mix the ingredients. Season to taste. Witness how fixing a 1000e computer can cost over 1300e. Be thankful for a good home insurance plan.
I’ve been delving into Scala quite a bit the last couple of weeks. Coderspiel has a piece on one of the coolest features in Scala: implicit type conversions.
In theory, these conversions allow you to use an object of type X in an expression where an object of type Y is expected. To achieve this, you define an implicit conversion function that takes an X and returns a Y (f: X -> Y for the mathematically inclined), and tag it with the language keyword implicit. Now, Scala can implicitly (and transparently to you, the coder) convert X’s to Y’s, if needed. Consider an example:
class Apple
class Orange {
def hi = Console.println("Hi, orange!")
}
object ApplesToOranges extends Application {
// This is our type conversion function.
// Note that its name can be ANYTHING:
// only the type signature matters!
implicit def applesToOranges(a: Apple) : Orange = new Orange
val a = new Apple()
// the implicit conversion occurs here, as Apple
// doesn't have a method called "hi", but Orange
// does, and we have defined the implicit conversion
// from Apples to Oranges
a.hi
}
This example doesn’t make much sense, but shows you the mechanics of implicit conversions. In practice, implicit conversions allow you to do some seriously cool things, such as extending existing Java class libraries (check out the Coderspiel entry for the JDBC and Wicket examples) elegantly.
There’s more to implicit type conversions than merely forcing legacy code to behave, though. For example, now that we can convert objects between different types pretty much arbitrarily and transparently, some DSL-like constructs become easier. Consider the RSpec -style assertions found in many BDD frameworks:
product.price.should == 5 character.name.should == "Mudkip"
Now, these clearly boil down to the format of
(some expression) should (expectation)
Perhaps we could represent these expectations using a class called Expectation, and have it evaluate any conditions thrown at it. Since we want to be able to say should for any object whatsoever, we need an implicit conversion from any type to an Expectation. Drum roll, please:
case class Thing(name: String)
object Expectations extends Application with Specification {
val mudkip = new Thing("mudkip")
mudkip.name should equal("mudkip")
(2 * 3) should equal(6)
// BOOM: failure!
false should equal(true)
}
// quick and dirty implementation
// just to illustrate the mechanics
class Expectation[T](value: T) {
def should(eq: Equality[T]) = {
if(value != eq.value) {
throw new AssertionError(
“Expected ” + eq.value + ” but was ” + value
)
}
}
}
case class Equality[T](value: T)
trait Specification {
implicit def anythingToExpectation[T](value: T)
= new Expectation[T](value)
def equal[T](t: T) = new Equality[T](t)
}
Specs, a BDD framework for Scala, seems to utilize implicit conversions in this manner to support a pretty concise way of writing specifications.
So, in inaccurate yet nerd-appealing terms, implicit type conversions are sort of like having the power of Ruby’s open classes in a statically typed language - monkey patching, wicked! Don’t over-use ‘em, though: you might hit issues such as ambiguity between different conversions (though the conversion functions are scoped.) As open classes, they seem like a prime candidate for the “I don’t know what I’m doing but if I stick this here, it seems to work” -type of coding.
Btw, the PDF version of the forthcoming book on Scala, aptly named Programming in Scala, is available through Artima’s early access program.
Courtesy of Confreaks. Good stuff. Currently watching the presentation on Treetop, a DSL-based parser framework for Ruby.
Joel Spolsky’s recent talk at Yale has some interesting bits on software quality.
He claims that über-geeks tend to focus on defining quality strictly as conforming to requirements (à la Crosby), and thus try to write automated specs for the whole program. Now, trying to write complete specs becomes the “software engineering equivalent of a perpetual motion machine”, since if your specs are complete, they describe the whole program accurately, and could be thus used to generate the actual program somehow - after all, your specs will end up being as detailed as the program itself. (btw, I love any blog entry with the terms “perpetual motion machine” and “crackpot” in it)
Naturally, the concept of “complete specs” is unrealistic and is there just for the argument’s sake (let’s just strive for “enough specs” instead.) Also, any sane definition of software quality goes beyond “literally adhering to specs.” Different stakeholders have different perspectives on quality. General product quality concepts that originate from the manufacturing industry tend to view specifications as the producer’s “path to victory.” Adhering to them should be enough to produce a high-quality product - from the manufacturer’s point of view. The value perceived by customer can be different, and consists of different attributes such as price, performance and reliability. The ultimate validation of quality is always customer satisfaction.
Although all generic product quality concepts don’t necessarily translate to software quality, there are recurrent themes on both sides: customer is king!
Stephan Schmidt has a blog entry on how explicit static type declarations are for the benefit of the developer, and not the compiler. That is, when you have something like this:
Person person = mysteriousCall()
you know at a glance that the object reference person is in fact of type Person. To a compiler, this might be obvious through type inference - in this case, by looking at the return type of the mysteriousCall function.
In Java, you always declare the member types explicitly, but for a statically typed language that does rely on type inference (I’m thinking Scala), the type declaration Person is clearly redundant. And if there’s one thing any programmer hates, it’s redundancy. So, one could just go with
val person = mysteriousCall()
and be done with it: the compiler will infer the type. Now, Schmidt makes a good point in that now the type of person is not immediately obvious to the naked eye, since there’s no explicit declaration in sight. This gave me an idea, which I posted as a comment to the blog entry:
What if a smart IDE could consult the compiler and decorate / annotate the above code line visually by showing the type of the person field? Now, the type would be explicitly shown to the user, but information that’s available through type inference wouldn’t have to be duplicated in the actual code.
Something like
val person : Person = mysteriousCall()
where : Person is the visual aid shown by the IDE, in standard Scala type declaration notation. Now, the type of the value is clearly visible, but there are no redundant type declarations in the code.
I wonder if anyone’s thought of this before (likely), and whether some tool already implements this for some language with type inference (less likely)?
I changed the visual theme of this blog (feed reader users ahoy) and “officially” discontinued my old blog, the contents of which will be migrated here at some point. Also, the RSS feed for this blog is now available via FeedBurner.
So, things got a bit busy a while back, and my blogging activity took on a steep decline. The thesis is progressing just fine, I’ve been delving deep into a bunch of interesting history behind the theory and practice of the concept of continuous quality. Craig Larman’s talk on Lean Thinking and the Toyota Production System at the recent Agile Finland conference got me interested in how some of the quality control measures Toyota engineers implemented some 50 years ago have influenced the QA activities we see in (agile) software projects today. Fascinating stuff.
Two books worth recommending: The Toyota Way outlines and explains the principles and practices behind the Toyota Production System, and lessons Toyota learned from prior work by the renown US quality consultant W. Edwards Deming. The Poppendiecks’ book on Lean Software Development ties the Lean principles all together with software development.
Having done the necessary paperwork earlier this week, my thesis is now officially underway. I’m currently sketching the table of contents to get a feel of how to organize things. Apart from a brief 2-page introduction, I haven’t done any actual writing yet, but I’m hoping to put in a couple of pages on general software QA theory and practice during this week — yeah, the easy stuff first. Also, I have a preliminary schedule in the works. The deadline I thought of at first seems a little unrealistic at the moment, but I’m trying to keep things agile and re-evaluate my progress as often as possible.
InfoQ has a piece on how the Adium (a kick-ass IM application for OS X) team does code reviews by going through SVN commit mails and looking for anything suspicious. That’s all good, and certainly a practice that’s been widely adopted where I work as well. But the thing that caught my eye was this quote by Peter Hosey:
Some of them also made me think “man, I wish we had some tests, since those would have caught this”.
Manual code reviews are certainly beneficial, but I couldn’t imagine doing them without having good, automated tests for my code first. Just think of the regressions issues…
Just got the “OK” on my thesis subject from the professor, yay! Need to get some paperwork done next, and after that my thesis will be underway officially. Bureaucracy…
I’m writing this entry from a bus, hooray HKL (Helsinki transport authority) for free WLAN!
