Gordon  Murray

Gordon Murray

1670675543

How to Reduce Boilerplate Code With Scala Macros and Quasiquotes

The concise syntax of Scala usually helps developers avoid writing boilerplate. When repetitive code is required anyway, developers can use macros and quasiquotes to keep code clean and maintainable. Here’s how.

The Scala language offers developers the opportunity to write object-oriented and functional code in a clean and concise syntax (as compared to Java, for example). Case classes, higher-order functions, and type inference are some of the features that Scala developers can leverage to write code that’s easier to maintain and less error-prone.

Unfortunately, Scala code is not immune to boilerplate, and developers may struggle to find a way to refactor and reuse such code. For example, some libraries force developers to repeat themselves by calling an API for each subclass of a sealed class.

But that’s only true until developers learn how to leverage macros and quasiquotes to generate the repeated code at compile time.

Use Case: Registering the Same Handler for All Subtypes of a Parent Class

During the development of a microservices system, I wanted to register a single handler for all events derived from a certain class. To avoid distracting us with the specifics of the framework I was using, here’s a simplified definition of its API for registering event handlers:

trait EventProcessor[Event] {
  def addHandler[E <: Event: ClassTag](
      handler: E => Unit
  ): EventProcessor[Event]

  def process(event: Event)
}

Having an event processor for any Event type, we can register handlers for subclasses of Event with the addHandler method.

Looking at the above signature, a developer might expect a handler registered for a given type to be invoked for events of its subtypes. For example, let’s consider the following class hierarchy of events involved in the User entity lifecycle:

 

Hierarchy of Scala events descending from UserEvent. There are three direct descendants: UserCreated (having a name and email, which are both Strings), UserChanged, and UserDeleted. Furthermore, UserChanged has two descendants of its own: NameChanged (having a name, which is a string) and EmailChanged (having an email, which is a string).

A Scala event class hierarchy.

 

The corresponding Scala declarations look like this:

sealed trait UserEvent
final case class UserCreated(name: String, email: String) extends UserEvent
sealed trait UserChanged                                  extends UserEvent
final case class NameChanged(name: String)                extends UserChanged
final case class EmailChanged(email: String)              extends UserChanged
case object UserDeleted                                   extends UserEvent

We can register a handler for each specific event class. But what if we want to register a handler for all the event classes? My first attempt was to register the handler for the UserEvent class. I expected it to be invoked for all the events.

val handler   = new EventHandlerImpl[UserEvent]
val processor = EventProcessor[UserEvent].addHandler[UserEvent](handler)

I noticed that the handler was never invoked during the tests. I dug into the code of Lagom, the framework I was using.

I found that the event processor implementation stored the handlers in a map with the registered class as the key. When an event is emitted, it looks for its class in that map to get the handler to call. The event processor is implemented along these lines:

type Handler[Event] = (_ <: Event) => Unit

private case class EventProcessorImpl[Event](
    handlers: Map[Class[_ <: Event], List[Handler[Event]]] =
      Map[Class[_ <: Event], List[Handler[Event]]]()
) extends EventProcessor[Event] {

  override def addHandler[E <: Event: ClassTag](
      handler: E => Unit
  ): EventProcessor[Event] = {
    val eventClass =
      implicitly[ClassTag[E]].runtimeClass.asInstanceOf[Class[_ <: Event]]
    val eventHandlers = handler
      .asInstanceOf[Handler[Event]] :: handlers.getOrElse(eventClass, List())
    copy(handlers + (eventClass -> eventHandlers))
  }

  override def process(event: Event): Unit = {
    handlers
      .get(event.getClass)
      .foreach(_.foreach(_.asInstanceOf[Event => Unit].apply(event)))
  }
}

Above, we registered a handler for the UserEvent class, but whenever a derived event like UserCreated was emitted, the processor wouldn’t find its class in the registry.

Thus Begins the Boilerplate Code

The solution is to register the same handler for each concrete event class. We can do it like this:

val handler = new EventHandlerImpl[UserEvent]  
val processor = EventProcessor[UserEvent]  
  .addHandler[UserCreated](handler)  
  .addHandler[NameChanged](handler)  
  .addHandler[EmailChanged](handler)  
  .addHandler[UserDeleted.type](handler)

Now the code works! But it’s repetitive.

It’s also difficult to maintain, as we will need to modify it every time we introduce a new event type. We might also have other places in our codebase where we are forced to list all the concrete types. We would also need to make sure to modify those places.

This is disappointing, as UserEvent is a sealed class, meaning that all its direct subclasses are known at compile time. What if we could leverage that information to avoid boilerplate?

Macros to the Rescue

Normally, Scala functions return a value based on the parameters we pass to them at run time. You can think of Scala macros as special functions that generate some code at compile time to replace their invocations with.

While the macro interface might seem to take values as parameters, its implementation will actually capture the abstract syntax tree (AST)—the internal representation of source code structure that the compiler uses—of those parameters. It then uses the AST to generate a new AST. Finally, the new AST replaces the macro call at compile time.

Let’s look at a macro declaration that will generate event handler registration for all the known subclasses of a given class:

def addHandlers[Event](
      processor: EventProcessor[Event],
      handler: Event => Unit
  ): EventProcessor[Event] = macro setEventHandlers_impl[Event]  
  
  
def setEventHandlers_impl[Event: c.WeakTypeTag](c: Context)(
      processor: c.Expr[EventProcessor[Event]],
      handler: c.Expr[Event => Unit]
  ): c.Expr[EventProcessor[Event]] = {

  // implementation here
}

Notice that for each parameter (including type parameter and return type), the implementation method has a corresponding AST expression as a parameter. For example, c.Expr[EventProcessor[Event]] matches EventProcessor[Event]. The parameter c: Context wraps the compilation context. We can use it to get all the information available at compile time.

In our case, we want to retrieve the children of our sealed class:

import c.universe._  
  
val symbol = weakTypeOf[Event].typeSymbol

def subclasses(symbol: Symbol): List[Symbol] = {  
  val children = symbol.asClass.knownDirectSubclasses.toList  
  symbol :: children.flatMap(subclasses(_))  
}  
  
val children = subclasses(symbol)

Note the recursive call to the subclasses method to ensure that indirect subclasses are also processed.

Now that we have the list of the event classes to register, we can build the AST for the code that the Scala macro will generate.

Generating Scala Code: ASTs or Quasiquotes?

To build our AST, we can either manipulate AST classes or use Scala quasiquotes. Using AST classes can produce code that is difficult to read and maintain. In contrast, quasiquotes dramatically reduce the complexity of the code by allowing us to use a syntax that is very similar to the generated code.

To illustrate the simplicity gain, let’s take the simple expression a + 2. Generating this with AST classes looks like this:

val exp = Apply(Select(Ident(TermName("a")), TermName("$plus")), List(Literal(Constant(2))))

We can achieve the same with quasiquotes with a more concise and readable syntax:

val exp = q"a + 2"

To keep our macro straightforward, we’ll use quasiquotes.

Let’s create the AST and return it as the result of the macro function:

val calls = children.foldLeft(q"$processor")((current, ref) =>
  q"$current.addHandler[$ref]($handler)"
)
c.Expr[EventProcessor[Event]](calls)

The code above starts with the processor expression received as a parameter, and for each Event subclass, it generates a call to the addHandler method with the subclass and handler function as parameters.

Now we can call the macro on the UserEvent class and it will generate the code to register the handler for all the subclasses:

val handler = new EventHandlerImpl[UserEvent]  
val processor = EventProcessorMacro.addHandlers(EventProcessor[UserEvent],handler)

That will generate this code:

com.example.event.processor.EventProcessor
.apply[com.example.event.handler.UserEvent]()
.addHandler[UserEvent](handler)
.addHandler[UserCreated](handler)
.addHandler[UserChanged](handler)
.addHandler[NameChanged](handler)
.addHandler[EmailChanged](handler)
.addHandler[UserDeleted](handler)

The code of the complete project compiles correctly and the test cases demonstrate that the handler is indeed registered for each subclass of UserEvent. Now we can be more confident in the capacity of our code to handle new event types.

Repetitive Code? Get Scala Macros to Write It

Even though Scala has a concise syntax that usually helps to avoid boilerplate, developers can still find situations where code becomes repetitive and cannot be easily refactored for reuse. Scala macros can be used with quasiquotes to overcome such issues, keeping Scala code clean and maintainable.

There are also popular libraries, like Macwire, that leverage Scala macros to help developers generate code. I strongly encourage every Scala developer to learn more about this language feature, as it can be a valuable asset in your tool set.

Original article source at: https://www.toptal.com/

#scala #macro 

What is GEEK

Buddha Community

How to Reduce Boilerplate Code With Scala Macros and Quasiquotes
Gordon  Murray

Gordon Murray

1670675543

How to Reduce Boilerplate Code With Scala Macros and Quasiquotes

The concise syntax of Scala usually helps developers avoid writing boilerplate. When repetitive code is required anyway, developers can use macros and quasiquotes to keep code clean and maintainable. Here’s how.

The Scala language offers developers the opportunity to write object-oriented and functional code in a clean and concise syntax (as compared to Java, for example). Case classes, higher-order functions, and type inference are some of the features that Scala developers can leverage to write code that’s easier to maintain and less error-prone.

Unfortunately, Scala code is not immune to boilerplate, and developers may struggle to find a way to refactor and reuse such code. For example, some libraries force developers to repeat themselves by calling an API for each subclass of a sealed class.

But that’s only true until developers learn how to leverage macros and quasiquotes to generate the repeated code at compile time.

Use Case: Registering the Same Handler for All Subtypes of a Parent Class

During the development of a microservices system, I wanted to register a single handler for all events derived from a certain class. To avoid distracting us with the specifics of the framework I was using, here’s a simplified definition of its API for registering event handlers:

trait EventProcessor[Event] {
  def addHandler[E <: Event: ClassTag](
      handler: E => Unit
  ): EventProcessor[Event]

  def process(event: Event)
}

Having an event processor for any Event type, we can register handlers for subclasses of Event with the addHandler method.

Looking at the above signature, a developer might expect a handler registered for a given type to be invoked for events of its subtypes. For example, let’s consider the following class hierarchy of events involved in the User entity lifecycle:

 

Hierarchy of Scala events descending from UserEvent. There are three direct descendants: UserCreated (having a name and email, which are both Strings), UserChanged, and UserDeleted. Furthermore, UserChanged has two descendants of its own: NameChanged (having a name, which is a string) and EmailChanged (having an email, which is a string).

A Scala event class hierarchy.

 

The corresponding Scala declarations look like this:

sealed trait UserEvent
final case class UserCreated(name: String, email: String) extends UserEvent
sealed trait UserChanged                                  extends UserEvent
final case class NameChanged(name: String)                extends UserChanged
final case class EmailChanged(email: String)              extends UserChanged
case object UserDeleted                                   extends UserEvent

We can register a handler for each specific event class. But what if we want to register a handler for all the event classes? My first attempt was to register the handler for the UserEvent class. I expected it to be invoked for all the events.

val handler   = new EventHandlerImpl[UserEvent]
val processor = EventProcessor[UserEvent].addHandler[UserEvent](handler)

I noticed that the handler was never invoked during the tests. I dug into the code of Lagom, the framework I was using.

I found that the event processor implementation stored the handlers in a map with the registered class as the key. When an event is emitted, it looks for its class in that map to get the handler to call. The event processor is implemented along these lines:

type Handler[Event] = (_ <: Event) => Unit

private case class EventProcessorImpl[Event](
    handlers: Map[Class[_ <: Event], List[Handler[Event]]] =
      Map[Class[_ <: Event], List[Handler[Event]]]()
) extends EventProcessor[Event] {

  override def addHandler[E <: Event: ClassTag](
      handler: E => Unit
  ): EventProcessor[Event] = {
    val eventClass =
      implicitly[ClassTag[E]].runtimeClass.asInstanceOf[Class[_ <: Event]]
    val eventHandlers = handler
      .asInstanceOf[Handler[Event]] :: handlers.getOrElse(eventClass, List())
    copy(handlers + (eventClass -> eventHandlers))
  }

  override def process(event: Event): Unit = {
    handlers
      .get(event.getClass)
      .foreach(_.foreach(_.asInstanceOf[Event => Unit].apply(event)))
  }
}

Above, we registered a handler for the UserEvent class, but whenever a derived event like UserCreated was emitted, the processor wouldn’t find its class in the registry.

Thus Begins the Boilerplate Code

The solution is to register the same handler for each concrete event class. We can do it like this:

val handler = new EventHandlerImpl[UserEvent]  
val processor = EventProcessor[UserEvent]  
  .addHandler[UserCreated](handler)  
  .addHandler[NameChanged](handler)  
  .addHandler[EmailChanged](handler)  
  .addHandler[UserDeleted.type](handler)

Now the code works! But it’s repetitive.

It’s also difficult to maintain, as we will need to modify it every time we introduce a new event type. We might also have other places in our codebase where we are forced to list all the concrete types. We would also need to make sure to modify those places.

This is disappointing, as UserEvent is a sealed class, meaning that all its direct subclasses are known at compile time. What if we could leverage that information to avoid boilerplate?

Macros to the Rescue

Normally, Scala functions return a value based on the parameters we pass to them at run time. You can think of Scala macros as special functions that generate some code at compile time to replace their invocations with.

While the macro interface might seem to take values as parameters, its implementation will actually capture the abstract syntax tree (AST)—the internal representation of source code structure that the compiler uses—of those parameters. It then uses the AST to generate a new AST. Finally, the new AST replaces the macro call at compile time.

Let’s look at a macro declaration that will generate event handler registration for all the known subclasses of a given class:

def addHandlers[Event](
      processor: EventProcessor[Event],
      handler: Event => Unit
  ): EventProcessor[Event] = macro setEventHandlers_impl[Event]  
  
  
def setEventHandlers_impl[Event: c.WeakTypeTag](c: Context)(
      processor: c.Expr[EventProcessor[Event]],
      handler: c.Expr[Event => Unit]
  ): c.Expr[EventProcessor[Event]] = {

  // implementation here
}

Notice that for each parameter (including type parameter and return type), the implementation method has a corresponding AST expression as a parameter. For example, c.Expr[EventProcessor[Event]] matches EventProcessor[Event]. The parameter c: Context wraps the compilation context. We can use it to get all the information available at compile time.

In our case, we want to retrieve the children of our sealed class:

import c.universe._  
  
val symbol = weakTypeOf[Event].typeSymbol

def subclasses(symbol: Symbol): List[Symbol] = {  
  val children = symbol.asClass.knownDirectSubclasses.toList  
  symbol :: children.flatMap(subclasses(_))  
}  
  
val children = subclasses(symbol)

Note the recursive call to the subclasses method to ensure that indirect subclasses are also processed.

Now that we have the list of the event classes to register, we can build the AST for the code that the Scala macro will generate.

Generating Scala Code: ASTs or Quasiquotes?

To build our AST, we can either manipulate AST classes or use Scala quasiquotes. Using AST classes can produce code that is difficult to read and maintain. In contrast, quasiquotes dramatically reduce the complexity of the code by allowing us to use a syntax that is very similar to the generated code.

To illustrate the simplicity gain, let’s take the simple expression a + 2. Generating this with AST classes looks like this:

val exp = Apply(Select(Ident(TermName("a")), TermName("$plus")), List(Literal(Constant(2))))

We can achieve the same with quasiquotes with a more concise and readable syntax:

val exp = q"a + 2"

To keep our macro straightforward, we’ll use quasiquotes.

Let’s create the AST and return it as the result of the macro function:

val calls = children.foldLeft(q"$processor")((current, ref) =>
  q"$current.addHandler[$ref]($handler)"
)
c.Expr[EventProcessor[Event]](calls)

The code above starts with the processor expression received as a parameter, and for each Event subclass, it generates a call to the addHandler method with the subclass and handler function as parameters.

Now we can call the macro on the UserEvent class and it will generate the code to register the handler for all the subclasses:

val handler = new EventHandlerImpl[UserEvent]  
val processor = EventProcessorMacro.addHandlers(EventProcessor[UserEvent],handler)

That will generate this code:

com.example.event.processor.EventProcessor
.apply[com.example.event.handler.UserEvent]()
.addHandler[UserEvent](handler)
.addHandler[UserCreated](handler)
.addHandler[UserChanged](handler)
.addHandler[NameChanged](handler)
.addHandler[EmailChanged](handler)
.addHandler[UserDeleted](handler)

The code of the complete project compiles correctly and the test cases demonstrate that the handler is indeed registered for each subclass of UserEvent. Now we can be more confident in the capacity of our code to handle new event types.

Repetitive Code? Get Scala Macros to Write It

Even though Scala has a concise syntax that usually helps to avoid boilerplate, developers can still find situations where code becomes repetitive and cannot be easily refactored for reuse. Scala macros can be used with quasiquotes to overcome such issues, keeping Scala code clean and maintainable.

There are also popular libraries, like Macwire, that leverage Scala macros to help developers generate code. I strongly encourage every Scala developer to learn more about this language feature, as it can be a valuable asset in your tool set.

Original article source at: https://www.toptal.com/

#scala #macro 

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Samanta  Moore

Samanta Moore

1621137960

Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review

Houston  Sipes

Houston Sipes

1604088000

How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash

Problems

  • Readability
  • Maintainability
  • Code Quality
  • Premature Optimization

Solutions

  1. Refactor the code
  2. Use better names

Examples

  • Optimized loops

Exceptions

  • Optimized code for low-level operations.

Sample Code

Wrong

function primeFactors(n){
	  var f = [],  i = 0, d = 2;  

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       f[i++]=(d); 
	       n /= d;
	    }
	    else{
	      d++;
	    }     
	  }
	  return f;
	}

Right

function primeFactors(numberToFactor){
	  var factors = [], 
	      divisor = 2,
	      remainder = numberToFactor;

	  while(remainder>=2){
	    if(remainder % divisor === 0){
	       factors.push(divisor); 
	       remainder = remainder/ divisor;
	    }
	    else{
	      divisor++;
	    }     
	  }
	  return factors;
	}

Detection

Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells

Fannie  Zemlak

Fannie Zemlak

1604048400

Softagram - Making Code Reviews Humane

The story of Softagram is a long one and has many twists. Everything started in a small company long time ago, from the area of static analysis tools development. After many phases, Softagram is focusing on helping developers to get visual feedback on the code change: how is the software design evolving in the pull request under review.

Benefits of code change visualization and dependency checks

While it is trivial to write 20 KLOC apps without help of tooling, usually things start getting complicated when the system grows over 100 KLOC.

The risk of god class anti-pattern, and the risk of mixing up with the responsibilities are increasing exponentially while the software grows larger.

To help with that, software evolution can be tracked safely with explicit dependency change reports provided automatically to each pull request. Blocking bad PR becomes easy, and having visual reports also has a democratizing effect on code review.

Example visualization

Basic building blocks of Softagram

  • Architectural analysis of the code, identifying how delta is impacting to the code base. Language specific analyzers are able to extract the essential internal/external dependency structures from each of the mainstream programming languages.

  • Checking for rule violations or anomalies in the delta, e.g. finding out cyclical dependencies. Graph theory comes to big help when finding out unwanted or weird dependencies.

  • Building visualization for humans. Complex structures such as software is not easy to represent without help of graph visualization. Here comes the vital role of change graph visualization technology developed within the last few years.

#automated-code-review #code-review-automation #code-reviews #devsecops #software-development #code-review #coding #good-company