Een van de redenen waarom ik het vak van developer zo bijzonder vind, is dat je niet alleen aan technische oplossingen werkt, maar ook de kans krijgt om te leren over onderwerpen en werelden waar je anders misschien nooit mee in aanraking zou komen. Een mooi voorbeeld hiervan is hoe belangrijk de binnenvaart is en hoe IT-systemen een cruciale rol spelen om dit mogelijk te maken.

Digitale Waterwegen: De Rol van Informatiesystemen in het Binnenvaartbeheer bij Rijkswaterstaat

Nederland staat bekend als een waterland. Met een uitgebreid netwerk van rivieren, kanalen en meren speelt de binnenvaart een onmisbare rol in onze economie en samenleving. Achter de schermen zorgt Rijkswaterstaat (RWS) ervoor dat dit complexe netwerk efficiënt, veilig en duurzaam functioneert. Binnen RWS speelt de afdeling Scheepvaartmanagement een sleutelrol in het dagelijks beheer van de binnenvaart.

Rijkswaterstaat (RWS) is een overheidsorganisatie die valt onder het Ministerie van Infrastructuur en Waterstaat. Sinds de oprichting in 1798 heeft RWS zich ontwikkeld tot een van de belangrijkste spelers op het gebied van infrastructuurbeheer in Nederland. Van snelwegen tot waterwegen, RWS zorgt ervoor dat Nederland in beweging blijft.

De afdeling Scheepvaartmanagement van RWS houdt zich bezig met het beheer van de Nederlandse vaarwegen en het waarborgen van een veilige en vlotte doorstroming van het scheepvaartverkeer. Dit omvat het monitoren van waterstanden, verkeersmanagement op rivieren, evenals onderhoud en de bediening (op afstand) van sluizen en bruggen.

Daarnaast levert de afdeling essentiële diensten zoals actuele scheepvaartberichten, scheepvaartverkeersbegeleiding en nautisch beheer. Ook biedt zij voorlichting en begeleiding aan zowel beroeps- als pleziervaart, met nadruk op veiligheid en gastvrijheid. Zo blijven de waterwegen efficiënt en toegankelijk voor alle gebruikers.

Binnenvaart: De Ruggengraat van de Nederlandse Logistiek

De binnenvaart is van groot belang voor de Nederlandse economie. Elk jaar worden miljoenen tonnen goederen vervoerd over waterwegen zoals de Rijn, Maas en Waal. Deze transportwijze is niet alleen aanzienlijk goedkoper dan wegvervoer – tot wel 75% voordeliger op langere afstanden – maar ook veel duurzamer. Waar vrachtwagens gemiddeld 60-150 gram CO₂ per tonkilometer uitstoten, ligt dit bij binnenvaartschepen tussen de 15 en 25 gram CO₂ per tonkilometer. Bovendien helpt de binnenvaart om verkeersdrukte op de wegen te verminderen, wat bijdraagt aan een efficiënter transportnetwerk.

Rijkswaterstaat maakt gebruik van meerdere, vaak in eigen beheer ontwikkelde informatiesystemen om zijn taken uit te voeren. Zonder deze systemen zouden de vele medewerkers van Rijkswaterstaat hun werk niet kunnen uitvoeren. Twee voorbeelden hiervan zijn BICS (Berichtencentrum voor de Binnenvaart) en IVS Next (Informatie- en Volgsysteem voor de Scheepvaart). Beide systemen worden intern ontwikkeld en beheerd.

BICS: Slimme Communicatie tussen Schippers en Beheerders

BICS is een digitale applicatie waarmee schippers eenvoudig en veilig reis- en ladinggegevens kunnen communiceren met vaarwegbeheerders zoals Rijkswaterstaat en havenautoriteiten. Het systeem vergemakkelijkt de uitwisseling van gestandaardiseerde gegevens en voldoet aan internationale meldplichtnormen, zoals de elektronische meldplicht voor schepen.

Elektronisch aanmelden: Schippers melden hun gegevens digitaal aan, waarna deze direct worden verwerkt in IVS Next of andere systemen.

Internationale standaardisatie: Ondersteuning voor meerdere talen (Engels, Frans, Duits en Nederlands) bevordert integratie met Europese vaarwegsystemen, met als doel een brede uitrol in Europa.

Gratis beschikbaarheid: Rijkswaterstaat biedt BICS kosteloos aan voor schippers, wat de toegankelijkheid vergroot.

Toepassing in Europa: Het systeem wordt al gebruikt langs de Rijn en wordt gefaseerd ingevoerd in andere Europese landen, zoals België en Roemenië.

Door de gegevensuitwisseling via BICS verloopt de communicatie tussen schippers en vaarwegbeheerders sneller en efficiënter, wat bijdraagt aan een veiligere en duurzamere scheepvaart.

IVS Next: Real-time Monitoring en Beheer van Scheepvaart

IVS Next is een geavanceerd informatiesysteem dat vaarwegbeheerders (sluis- en brugbedienaars en/of verkeersleiders) een actueel overzicht biedt van scheepvaartverkeer op de Nederlandse waterwegen. Het systeem, operationeel sinds 2019, verzamelt gegevens via onder andere AIS-signalen* en aanmeldingen vanuit BICS. Hierdoor kunnen schepen real-time worden gevolgd en wordt de doorstroming geoptimaliseerd.

Real-time monitoring: IVS Next biedt een actueel overzicht van beroeps- en zeevaart dankzij verplichte AIS-signalen, BICS-aanmeldingen en andere systemen.

Gegevensbeheer: Het systeem deelt scheepvaartdata met stakeholders zoals waterbeheerders en havens, waarbij privacy en vertrouwelijkheid hoog in het vaandel staan.

Efficiëntie: IVS Next verbindt bruggen, sluizen en verkeersposten, zodat schepen sneller kunnen passeren zonder hun gegevens telkens opnieuw door te geven. Zo kunnen sluis- en brugbedienaren het verkeer efficiënt en veilig plannen, terwijl verkeersleiders een volledig overzicht van reis- en ladingsgegevens hebben om potentieel gevaarlijke situaties te voorkomen.

*   AIS-signalen (Automatic Identification System) zijn automatische scheepvaartuitzendingen die informatie delen over positie, snelheid, koers en identiteit van schepen, bedoeld voor navigatie en verkeersbeheer.

Ontdek hoe IT een een cruciale rol speelt in de wereld van de binnenvaart.

Opdracht in de spotlight: Rijkswaterstaat

Java has come a long way since its early days as an object-oriented, imperative programming language. Over time, it has steadily adopted features inspired by functional programming, a paradigm focused on immutability, pure functions, and type safety—principles long embraced by languages like Haskell.

From lambda expressions and functional interfaces in Java 8 to records in Java 11, Java has been evolving to make functional programming more accessible. However, one crucial feature has been missing—the ability to define algebraic data types (ADTs) in a concise and type-safe way. With the introduction of sealed classes, Java now fills this gap, enabling developers to write code that mirrors the expressiveness and immutability of functional languages like Haskell.

In this blog, I’ll explore how Java is embracing functional programming principles and demonstrate why sealed classes are the missing piece of the puzzle for writing fully functional code in Java, using the purely functional language Haskel as an example.

A sealed class in Java restricts which other classes or interfaces can extend or implement it, enabling developers to define a controlled hierarchy and enforce inheritance rules. This restriction is explicitly enforced by the Java compiler, ensuring that only specified, predefined subclasses are allowed to inherit from the sealed class. 

To declare a sealed class (or interface), you use the `sealed` keyword on the class definition, followed by a `permits` clause that lists all allowed subclasses:

public sealed class Shape permits Circle, Triangle, Rectangle {
    // class contents
}

In this example, `Shape` is a sealed class that is extended by `Circle`, `Triangle`, and `Rectangle`.

When a sealed class defines its permitted subclasses, those subclasses must be declared within the same module or package as the sealed class and compiled together. Each subclass of a sealed class must include one of the following modifiers. The final modifier indicates that the subclass is the last type in the hierarchy and cannot be extended further. The sealed modifier allows the subclass to define its own permitted subclasses, continuing the sealed hierarchy. The non-sealed modifier removes inheritance restrictions, allowing any class to extend this subclass.

public sealed class Shape permits Circle, Triangle, Rectangle {}

final class Circle extends Shape {}
sealed class Triangle extends Shape permits Pyramid {}
non-sealed class Rectangle extends Shape {} 
final class Pyramid extends Triangle {} 

// And an unknown, at runtime, number of children of Rectangle e.g.
final class Square extends Rectangle {} 

In this example, Shape is the sealed class that is extended by ‘Circle’, ‘Triangle’ and ‘Rectangle’. Circle is final and cannot be subclassed. Triangle is defined as sealed, and therefore must declare permitted subclasses. Rectangle is non-sealed meaning it can be subclassed, meaning it can be subclassed without restriction.

Haskell is a purely functional programming language that emphasizes immutability, lazy evaluation, and strong static typing. It supports higher-order functions, allowing functions to take other functions as arguments or return them—similar to lambda expressions and functional interfaces introduced in Java 8. Haskell also includes algebraic data types (ADTs), which enable expressive and type-safe data modeling. Its product types resemble records introduced in Java 11, while its sum types are similar to Java’s sealed classes introduced more recently.

Java has increasingly adopted features inspired by functional programming, particularly to address challenges in concurrency. Functional programming minimizes the use of variables and promotes immutability by default, ensuring computations are thread-safe. This makes functional programs highly scalable and reduces the risk of bugs caused by shared mutable state, offering significant advantages for modern, parallel systems.

In the following sections, I’ll walk you through how a (simple) List is implemented in Haskell, followed by its functional equivalent in Java. I chose the List because it’s often considered the "Hello, World" of Haskell programming—simple, fundamental, and a great starting point for understanding functional concepts. 

-- Define a List data type 
data List a = Empty             -- Represents an empty list 
            | Cons a (List a)   -- Represents a non-empty list 
                                -- (head + tail) 

-- Add an element to the list 
addElement :: a -> List a -> List a 
addElement element list = Cons element list

In the first two lines, the List type is defined with two constructors: Empty, representing an empty list, and Cons (Constructor) , representing a value (the head) followed by the rest of the list (the tail). Notice the use of recursion. The letter ‘a’ is used in the same way as ‘T’ in Java to represent a generic type.

Next, a single global function called addElement is declared to add an element to the beginning of a list. This function is defined in two parts. The first line specifies its type signature—it takes a value of type a and a List of type a, then returns a new List of the same type. The second line provides the implementation, which is notably concise. It creates a new Cons entity, setting the provided value as the head and the existing list as the tail.

In the following code, a list is declared, and a new list is created by prepending an element. Notice that addElement returns a new list object, leaving the original list unchanged. This design ensures that the list is immutable by definition.

myList :: List Int
myList = Cons 1 (Cons 2 (Cons 3 Empty))       -- [1 2 3]

let newList = addElement 0 myList             -- [0 1 2 3]

Now let's write the same code in Java, in a pure functional way.

// Define the sealed class hierarchy
sealed interface List<T> permits Empty, Cons{} 

// Represents an empty listrecord 
Empty<T>() implements List<T> {}

// Represents a non-empty list (head + tail)record  
Cons<T>(T head, List<T> tail) implements List<T> {}

// Utility class to work with the List ADT
class ListUtils {
    // A useful to String method
    public static <T> String toString(List<T> list) {
        return switch (list) {
            case Empty<T> ignored -> "";
            case Cons<T> cons -> cons.head().toString() + " " 
               + toString(cons.tail());        
    };    
}    
    // Add an element to the list    
    public static <T> List<T> addElement(T element, List<T> list) {     
        return new Cons<T>(element, list);    
    }
}

By using a sealed interface List that only permits the records Empty and Cons, we can create an immutable algebraic data type (ADT) in Java. While some of Haskell’s compactness and conciseness is lost in translation, the result is still quite readable and maintains the functional programming style.

The global functions, toString and addElement, are defined in a utility class, although they could also be implemented directly within the List interface. Notably, the toString method uses pattern matching and ensures exhaustiveness in the switch statement, making the code both safe and expressive. This design maintains immutability and type safety, showcasing how Java can incorporate functional programming concepts inspired by languages like Haskell.

public class Main {
    public static void main(String[] args) {
        // Create a list: [ 1 2 3 ]
        var myList = new Cons<>(1, new Cons<>(2, 
                     new Cons<>(3, new Empty<>())));
        var newList = ListUtils.addElement(0, myList);

        // Will print: [ 0 1 2 3 ]
        System.out.println("[" + ListUtils.toString(newList) + "]"); 
    }
}

This code closely mirrors the previous Haskell example. It demonstrates how to initialize a List and create a new one by prepending a zero to the beginning. While Java already offers a variety of predefined thread-safe data structures, this example highlights how Java has taken further steps toward embracing functional programming.

In this blog, I demonstrated how Java has steadily adopted features from functional programming languages like Haskell. With the introduction of lambda expressions, records, and most recently, sealed classes, Java has evolved to support immutability, type safety, and expressive data modeling.

I also showed that Java can achieve many of the same benefits as Haskell—such as thread safety andscalability—while retaining its object-oriented foundation. With sealed classes filling the final gap, Java is now fully equipped to support a functional programming style, allowing developers to write cleaner, safer, and more maintainable code.

Sealed classes and functional programming in Java

Haskell Did It First: Sealed classes and functional programming in Java

In early 2024, the 1 Billion Row Challenge (1BRC) went viral. The goal was simple yet daunting: process a file with 1 billion rows of temperature measurements and calculate the minimum, maximum, and mean temperatures for each weather station in the dataset. This benchmark pushed developers to explore extreme optimization techniques. The winning solution completed this challenge in just a mere 1.535 seconds, decisively debunking the long-standing myth that Java is inherently slow.

The question I aim to answer in this blog is: how was this incredible feat achieved, and what techniques can we apply to make Java code faster?

The first and simplest approach is to leverage the ability of modern processors to handle multiple tasks simultaneously—also known as parallelization. Parallelization makes work faster by dividing a task into smaller parts that can be completed simultaneously. Instead of processing everything one step at a time (sequentially), parallelization assigns different parts of the work to multiple processors or cores.

Java offers powerful libraries for parallelization, making it easy to divide tasks and process them simultaneously. Key tools include the Fork/Join Framework for divide-and-conquer problems, Parallel Streams for effortless parallel data operations, and the Executor Framework for managing threads. Features like CompletableFuture enable asynchronous parallel tasks, while third-party libraries like Akka and RxJava support advanced concurrency. These tools simplify parallelization, boosting performance by leveraging modern multi-core processors.


The fastest solutions took this approach to the next level. A common bottleneck in parallel processing is that files are often read sequentially, even when the data is processed in parallel. Without delving too deeply into the details, the fastest developers overcame this by writing custom code to split the text into chunks optimized for the number of cores and CPU cache sizes, allowing the file to be read and processed simultaneously across all cores—fully maximizing their utilization. Understanding your architecture is key.

Working hard isn’t enough—you need to work smart. While reading each byte of every line, the data must also be understood. Since they were dealing with a CSV file, this involves identifying the separators and efficiently parsing each line into meaningful pieces for further processing.

A simple way to handle this is by using regular expressions, often the go-to approach in everyday programming. However, for extreme performance, regular expressions are too slow due to their sequential nature. To optimize processing, it’s necessary to break down each line into bytes and process them in parallel, leveraging the capabilities of modern CPUs.

The first approach is using Java’s Vector API, which enables SIMD (Single Instruction, Multiple Data) operations. SIMD allows a single instruction to process multiple bytes simultaneously, significantly boosting performance. For example, instead of checking each character one by one, the Vector API processes entire chunks of data (e.g., 128 bits) in parallel, identifying target values like commas in a single operation. This method is ideal for maximizing CPU core utilization;  however, it does have some limitations, such as incompatibility with GraalVM.

The second approach involves masks, which efficiently process data at the byte level. A mask is an array that marks positions of target values (e.g., 1 for a match, 0 otherwise) using branchless comparisons. Masks avoid the performance penalties of conditional logic, ensuring predictable, high-speed processing. While less abstract than the Vector API, masks provide a scalable and hardware-agnostic solution for parallel data parsing.

3. Efficient Data Structures – Be Organized

When processing data, temporary results need to be stored efficiently while handling the next piece of data. A commonly used data structure for this purpose is the standard Java hash map (HashMap), a reliable solution for key-value storage. However, while HashMap is versatile, it can become a performance bottleneck due to hash collisions and the overhead involved in resolving them.

To address this, some developers explored alternative storage methods. One particularly intriguing approach was creating a custom hash map with forward probing. A standard Java HashMap works by transforming a key into a hash value and storing the value at an index determined by the hash. However, hash collisions occur when multiple keys are assigned the same index. In such cases, the HashMap stores these values in a list. Retrieving a value then requires accessing the list and traversing it to find the desired value, which can slow down performance.

In contrast, a hash map using forward probing avoids storing values with the same index in a list. Instead, it searches for the next available slot in its array and places the value there. This approach is faster, provided collisions are kept to a minimum. Since the set of weather stations in this case was fixed, developers could design a custom hash function optimized to minimize collisions. By fully understanding the constraints of their data, they were able to implement extremely efficient organized data.

This approach is less about optimizing the code itself and more about optimizing how Java runs the code. As you may know, Java source code is first compiled into an intermediate format called bytecode, which is platform-independent. At runtime, the JVM (Java Virtual Machine) uses a Just-In-Time (JIT) compiler to convert this bytecode into native machine code specific to the underlying hardware. This process allows Java programs to achieve performance close to that of native applications.

However, JIT compilation happens during program execution, which can create overhead. The JVM spends time analyzing and optimizing code paths at runtime, leading to initial "warm-up" delays. This can slow down execution, especially for performance-critical or short-lived applications.

To address this, contestants used GraalVM, which improves runtime performance by leveraging an Ahead-Of-Time (AOT) compiler. GraalVM compiles Java bytecode into native machine code before the program runs, eliminating the need for JIT compilation during execution. This reduces warm-up times and improves startup performance. Additionally, GraalVM applies advanced optimization techniques to generate highly efficient native code, resulting in faster execution overall, particularly for applications that demand high performance or quick startup times.

For some developers, even this wasn’t fast enough. As you may know, Java uses a Garbage Collector (GC) to clean up objects that are no longer referenced. Without this process, a program running long enough would eventually run out of memory. However, while the Garbage Collector is working, the CPU is occupied and not running your program, which can cause slowdowns.

To remedy this, some developers simply turned off the Garbage Collector. (Technically, they used the Epsilon GC that just doesn’t do anything.) This is feasible for short-lived programs because all memory is automatically released once the program finishes execution. By disabling the GC, developers eliminated this overhead, allowing the CPU to focus entirely on the program and achieve faster execution times.

One of the key benefits of using Java over languages like C or C++ is its strong emphasis on safety. Java is designed to minimize the risk of causing harm to your system while running code. Each Java application operates within its own dedicated memory space and cannot access or interfere with memory allocated to other processes. This isolation, often referred to as a sandbox, ensures that Java programs cannot disrupt other processes or compromise system stability.

Java is also memory-safe, meaning it prevents unauthorized access to memory. Unlike C or C++, where developers manage memory manually, Java uses built-in mechanisms to handle memory allocation and cleanup. For example, Java’s automatic Garbage Collector ensures that unused objects are safely removed without risking memory leaks or invalid memory references. Combined with its robust runtime checks and strict type system, Java significantly reduces vulnerabilities, making it a highly secure choice for developers.

However, this level of safety comes at a cost: it can slow down your code. To achieve even greater performance, some developers bypassed these safety features by using Java's Unsafe library for direct memory manipulation. The Unsafe library allows developers to perform low-level operations, such as directly allocating and accessing memory, circumventing Java’s built-in memory management and safety checks.

One specific optimization involved bypassing the unmapping of data, which is typically handled automatically by Java’s memory management system. By taking control of these processes, developers were able to reduce overhead and execute operations significantly faster. 

The above-mentioned techniques sacrifice safety for speed and should, therefore, be used only in extreme cases when a developer truly knows what they are doing.

Parallelization – Teamwork: Break tasks into smaller pieces and execute them in parallel to maximize CPU utilization, ensuring no processing power goes to waste.

Efficient Parsing – Work Smart: In programming, solving problems efficiently matters. While there are many ways to achieve a solution, some paths are significantly faster—choose the shortest road to your goal.

Efficient Data Structures – Be Organized: Every data structure has strengths and weaknesses. Understand your data and select the structure that best fits the problem for optimal performance.

GraalVM – Skip Unnecessary Steps: Fast code isn’t everything; how your code is executed matters too. Tools like GraalVM can eliminate unnecessary runtime overhead, allowing your code to run more efficiently.

Unsafe – Don’t Try This at Home: In extreme cases, bypassing Java’s safety features with tools like the Unsafe library can deliver higher performance. However, this approach should only be used with careful consideration and a strong justification.

https://www.morling.dev/blog/one-billion-row-challenge/

Inside the Fastest Solutions of the 1 Billion Row Challenge

About a year ago I wrote a blog with the title ‘How to write a simple IntelliJ plugin’. In the previous blog I explained why I wrote the plugin and which steps I had to take. Furthermore you will also read that I set out to create a proper plugin. That is a plugin that can easily be installed by downloading it from the Jetbrains Marketplace. This turned out to be much harder than I expected, so that is why I settled for a live plugin last year. The blog ends with the suggestion that I will write a proper plugin in the future and will blog about it. Well the future is now, as this blog will be about creating this proper plugin. 

The main reason to pick up this pet project again is the fact that I will be giving my talk ‘Improving your skills with the debugger’ at JavaLand. The old plugin is part of this talk, but I always try to update my talk with the latest and greatest. So I decided it was time to finally create a proper plugin. 

The difference between the previous and the current title is exactly two words. The word ‘write’ became ‘create’ and ‘simple’ became ‘proper’. I talked enough about the second difference, so let’s explain the first. The meat of the previous blog was how to write (or code) a plugin. This blog will focus more on how to get this plugin in the JetBrains marketplace. So it also covers the process after writing the plugin. 

In the previous blog I wrote that I tried to create a proper plugin, so my first step was to open this old project and to make sure that everything still functioned. The previous project was around a year old and to my surprise I could not get it working again. Software decay/rot is real! I spent a few hours before I decided to throw the towel. I solved this issue by starting with a clean slate. Jetbrains provides a template plugin repository (link) that you can fork. So I did this and added my code from the previous project. The code compiled again, so the problem was solved!

The first thing I noticed when using the provided template was the feature that one can start a completely new instance of IntelliJ with your plugin pre-installed (see Image 1). This was something I did not notice the previous time I tried to create a plugin. A very useful feature which lets you properly manually test your plugin before you upload it to the marketplace. 

Image 1 - Run Plugin starts a new instance of the IDE with your plugin pre-installed.

The observant reader noticed the word ‘manually’ in the previous sentence. This is a fine start, but in the end we need automated tests as well. It turns out that it is pretty hard. The documentation gives a few examples, but I still spent hours and hours before I got a green unit test. In those hours I had to figure out why a part of my test context was not initialized properly. I spent a lot of time in the source code of IntelliJ to find clues. In the end it turned out to be a dependency problem. I had to specify that my plugin was dependent on the Java features of IntelliJ. For people with the same problem Image 2 shows where to add dependencies. After addressing this omission, things went pretty smooth and I had a lovely green unit test in the end.

Image 2 - This is where one adds dependencies.

With this test in place, I could finally do some refactorings and remove some unnecessary boilerplate from the template plugin project. Furthermore I found a bug while manually testing the plugin. So I updated the test (and fixed the bug) to prevent this ever happening again. As far as I was concerned the plugin was ready to share with the world.

The first step in publishing the plugin is signing it with a private key. The Jetbrains marketplace will use the public key to see if everything is in order. You only have to create a private-public key pair and the template takes care of the heavy lifting after you add a few environment variables. This is a pretty standard procedure for app (or plugin) stores. 

The next step is uploading the plugin to the marketplace. Again a straightforward process. You can choose between a manual upload or an upload via the Gradle scripts in the template. In the former case you can upload your .jar file in your browser. In the latter case you first need to generate a token in your Jetbrains account. The next step is to add this token as an environment variable. You can run the Gradle scripts locally or you can use the included GitHub Actions pipeline. 

The last step is the most difficult one. You have to wait till the people of the Jetbrains Marketplace approve your plugin. This process takes up to two business days. Luckily I had something to do in the meantime, because the dependabot found a lot of dependencies to upgrade. In case you do not know what dependabot is: it is a GitHub bot that creates pull requests in your project whenever it finds a newer version of a dependency.

I am really excited to have my first plugin published on the Jetbrains Marketplace! I learned a lot in the process of creating, testing and publishing the plugin. Normally I use Maven as my go-to buildtool. So it was a nice change to be forced to use Gradle. Secondly, I have never used GitHub Actions before and it is nice to see it in action (pun intended). Thirdly, I had a reason to browse the IntelliJ codebase, which was interesting. Finally, it gave me an excuse to write a new blogpost. 

The plugin can be found here:
https://plugins.jetbrains.com/plugin/23796-log-selection-with-breakpoint

Please visit it, as I've put in extra effort to create an animated gif to demonstrate what the plugin does.
The source code can be found here:
https://github.com/BoukeNijhuis/breakpoint-log-selection

So, there is only one question left to answer: how will I continue this blog series? Good question! My intention is to add a feature to the plugin that enables you to change the hard-coded log expression. However, to do so, I need a UI where you can edit the current expression. So, that sounds like a lot of work. Till next year!

Image 3 The plugin can be found on the IntelliJ marketplace

In this blog Bouke Nijhuis explains how he created a proper plugin for IntelliJ and how he published it on the IntelliJ Marketplace.

How to create a proper IntelliJ plugin

De CTO's van onze drie units - Data, DevOps en Development - vormen samen met de CTO van CINQ een denktank: de CINQTank. Zij houden zich bezig met het “voorspellen” van de toekomst. Samen met de mensen uit de units kijken zij in de verschillende vakgebieden naar de ontwikkeling van technieken en tools. Hiermee wordt een technische visie gecreëerd. Deze wordt onder andere gebruikt om richting te geven aan de opleiding van medewerkers. Tevens wordt het gebruikt als input voor de jaarplannen van elke afdeling. Tot slot is het natuurlijk ook gewoon leuk om na te denken hoe de toekomst eruit zou kunnen zien. Hieronder lees je onze verwachtingen op het gebied van Development in 2024.

Als we terugblikken op de verwachtingen van het afgelopen jaar, voorspelden we een toenemende vraag vanuit de markt naar gespecialiseerde ontwikkelaars die bereid zijn buiten hun gebruikelijke rol te treden. Deze ontwikkeling heeft zich gedurende het afgelopen jaar voortgezet.

Kort gezegd zijn onze klanten op zoek naar ontwikkelaars die goed zijn in het ontwikkelen binnen hun specialisme (bijvoorbeeld backend Java), maar niet terughoudend zijn om andere ontwikkelwerkzaamheden (bijvoorbeeld frontend Javascript) op te pakken. Er wordt van ze verwacht dat ze buiten hun eigen vakgebied kunnen acteren en ook verantwoordelijk zijn voor het ontwerpen, testen, beveiligen en beheren van hun software samen met de specialisten op deze onderwerpen.

Als we de cloud providers analyseren die actief zijn in ons marktsegment, valt op dat Microsoft Azure veruit het grootste marktaandeel heeft. Dit is een opvallende trend, aangezien er een wereldwijde voorkeur voor AWS is. Bij het trachten te verklaren van deze trend in Nederland, kom je uit op een historisch sterke vertegenwoordiging van Microsoft Nederland en de aanwezigheid van ervaren Nederlands personeel met betrekking tot Microsoft Azure.

Bovendien markeerde het afgelopen jaar als het 'Jaar van Artificial Intelligence', met als primaire katalysator de vrijgave van ChatGPT aan het grote publiek. Hierdoor kwam iedereen ermee in aanraking, wat resulteerde in een situatie waarin eenieder er gelijktijdig gebruik van kon maken. Deze dynamiek verschilt aanzienlijk van de gebruikelijke manier waarop baanbrekende technologie de markt betreedt. Normaliter is het aanvankelijk te kostbaar voor de gemiddelde persoon en komt het eerst ten goede aan grote ondernemingen die er royaal voor betalen, alvorens het zich verspreidt naar de open source-gemeenschap.

Door deze nieuwe manier van technologie op de markt brengen, constateren we dat veel bedrijven nog niet volledig zijn voorbereid op de implementatie van AI. We zien dat technologiebedrijven intensief bezig zijn met het verdedigen of vergroten van hun marktpositie door AI toe te voegen aan hun portfolio.

Tegelijkertijd merkten we op dat bedrijven die software ontwikkelen nog niet volledig gereed waren om AI in de praktijk te ondersteunen voor hun medewerkers. Veel grote ondernemingen waren hierdoor zelfs genoodzaakt AI in de ban te doen,  uit vrees voor datalekken, ongeoorloofd gebruik en andere risico's.

Ten slotte, als engineer, was het mooi om te zien dat gedurende het hele jaar wekelijks nieuwe ideeën en toepassingen op het gebied van generatieve AI opdoken. Zelfs aan het einde van het jaar lijkt deze trend niet af te nemen. Dit maakte het jaar buitengewoon interessant.

De opkomst van open toegankelijke Large Language Models heeft ertoe geleid dat we een herdefinitie hebben gekregen van AI. Waar we vorig jaar nog intensief debatteerden over de kwestie "het is machine learning, geen AI", lijkt iedereen zich er nu langzaam bij te hebben neergelegd dat AI de overkoepelende term is geworden waaronder we alles scharen.

Ook het komende jaar zal in het teken staan van AI. Terwijl de technologiereuzen Microsoft, Google en Amazon zich blijven concentreren op het aanbieden van Large Language Models, heeft de rest van de AI-markt een aanzienlijke toename van aandacht en innovatie ervaren. Ook het komende jaar belooft daarom een voortdurende stroom van nieuwe tools en ideeën met betrekking tot het gebruik van AI.

Ook op andere technologie segmenten zijn bedrijven druk bezig hun producten aan te passen aan de komst van AI. Het meest sprekende voorbeeld voor ontwikkelaars zijn de code assistenten die voor veel ontwikkelaars een tool zullen worden / blijven voor dagelijks gebruik ter ondersteuning van hun werk.

Steeds meer bedrijven hebben al AI-richtlijnen vastgesteld of zijn bezig met het formaliseren van het gebruik van AI. We zien hierbij een duidelijke trend waarbij veelal wordt gekozen voor het hosten van Large Language Models binnen de eigen private cloud. Hierdoor zullen veel ontwikkelaars het komende jaar de mogelijkheid krijgen om ook code assistenten te gebruiken in het kader van hun opdrachten.

Wat betreft de backend constateren we dat de JVM-talen nog steeds de dominante meerderheid vertegenwoordigen in ons marktsegment, waarbij Java nog steeds de grootste speler is. Echter, steeds meer klanten maken ook gebruik van Kotlin. De verwachting is dat beide talen in de komende jaren steeds meer gezamenlijk zullen worden ingezet, waarbij Kotlin een steeds prominentere rol zal gaan vervullen.

Als we naar backend frameworks kijken, blijft Spring (Boot) nog steeds de grootste speler in ons marktsegment. Desalniettemin groeit de interesse voor alternatieven zoals Quarkus en Micronaut. Op dit moment wordt echter vaak nog niet heel concreet gevraagd naar werkervaring met deze laatste twee frameworks en is ervaring met Java/Spring voldoende. Mocht de populariteit van Quarkus en Micronaut echter snel toenemen, dan zal bij het werven van ontwikkelaars ook een grotere voorkeur ontstaan voor specifieke ervaring met deze frameworks.

Het javascript ecosysteem blijft één van de meest veranderlijke omgevingen in de IT met constante ontwikkelingen op gebied van runtimes, libraries, build tools en zeer actieve ontwikkelingen van Typescript met de komst van versie 5 in het afgelopen jaar. Deze trend zal doorzetten en onze frontend ontwikkelaars zijn hier aan gewend en dusdanig intrinsiek gemotiveerd dat ze makkelijk met deze ontwikkelingen mee gaan.

Echter is er ook een stabiele factor op het gebied van frontend. Wat betreft frameworks blijven React en Angular dominant. In ons marktsegment constateren we een groeiende vraag naar React ten opzichte van Angular.

Over het algemeen blijft het voor onze ontwikkelaars raadzaam om technische kennis in tweevoud te verwerven en te experimenteren met nieuwe technieken. Denk hierbij aan bijvoorbeeld twee verschillende frontend frameworks, twee verschillende programmeertalen of twee verschillende cloud omgevingen. Dit is raadzaam omdat het beheersen van kennis in tweevoud, een bredere kennis ondersteunt en meer inzichten met zich meebrengt. Bovendien maakt de eerder verkregen kennis het leren van een derde onderwerp in dezelfde categorie sneller en gemakkelijker. 

Als we specifiek kijken naar development, is er op dit moment geen onderwerp of technologie die duidelijk zal verdwijnen in het komende jaar. Het lijkt erop dat de focus voornamelijk zal liggen op het verfijnen en optimaliseren van bestaande toepassingen. Kosten en duurzaamheid zijn hier voornamelijk de drijfveer.

De meeste bedrijven hebben hun cloudtransities voltooid of zijn hierin vergevorderd, waarbij veel applicaties nu cloud native zijn. Desondanks overwegen enkele bedrijven in specifieke gevallen delen van hun IT-infrastructuur terug te halen uit de cloud of te migreren naar hybride situaties. Vaak wordt deze overweging ingegeven door minder positieve ervaringen met cloud providers of wordt het gedreven door kostenoverwegingen. 

De verwachting is dat deze discussie de komende jaren steeds prominenter zal worden en dat de strategie van "alles in de cloud" verschuift naar "in de cloud, tenzij."

Als we de backend-architectuur onder de loep nemen, is de verwachting dat er een spanningsveld zal ontstaan tussen micro-architecturen en monolieten. Gedurende vele jaren werden monolieten als ongunstig beschouwd. Ze moesten worden vernieuwd en werden als ongeschikt gezien voor de cloudtransformatie.

Echter, dankzij katalysatoren zoals Java-modules, Spring Modulith, en soms negatieve ervaringen met de complexiteit van een microservices-architectuur, wordt verwacht dat de vertrouwde monoliet in sommige bedrijven en situaties een nieuw leven zal krijgen.

Microservices-architecturen zullen gedurende vele jaren een prominente rol blijven spelen en zijn uitermate geschikt om schaalbare, cloud-native, gedecentraliseerde, en herbruikbare architecturen op te zetten. De complexiteit van deze systemen ligt in de onderlinge interactie.

De verwachting is dat er een niche zal ontstaan waar bedrijven met kleine softwareteams ervoor kiezen om de complexiteit van onderlinge systemen weer in de applicatie zelf te integreren. Dit doen ze door applicaties op te splitsen met behulp van modules in plaats van microservices, waardoor dezelfde ontkoppeling en herbruikbaarheid gefaciliteerd kan worden.

In de wereld van Artificial Intelligence bevinden we ons in een periode waar alle tech giganten op meerdere fronten strijden om de overhand op de markt. Op het gebied van zoekmachines, AI assistenten en cloudproviders concurreren voornamelijk dezelfde bedrijven om marktaandeel, met name Microsoft, Amazon en Google.

Businesspartners voor het hosten van Large Language Models (LLM) en andere generatieve AI-toepassingen.

Direct in het ecosysteem van ontwikkelaars door middel van code-assistenten.

In het consumentensegment door de potentiële verschuiving van zoekmachines naar AI assistenten, met als onderliggende reden advertentie-inkomsten.

Wat opvalt, is dat Microsoft op al deze fronten zeer prominent aanwezig is:

Door investeringen in OpenAI heeft Microsoft toegang tot de grootste en beste LLM's ter wereld.

Hierdoor kon Microsoft LLM snel implementeren als assistent voor hun Bing-zoekmachine.

In het enterprise-segment is Microsoft, vanwege zijn positie op de Azure-markt, een aangewezen partner om LLM en andere generatieve AI-toepassingen in private clouds aan te bieden.

Met GitHub Copilot positioneert Microsoft zich ook in het IDE-segment met betrekking tot AI.

Microsoft lijkt daarmee op meerdere fronten direct marktaandeel te willen veroveren, zowel op het gebied van zoekmachines ten opzichte van Google als in directe concurrentie met AWS door middel van AI cloudservices en code assistenten. Of dit zal resulteren in een situatie waar mensen eindelijk Bing gaan gebruiken, valt nog te bezien. 

AI heeft de potentie om de markt volledig te ontwrichten en op dit moment zijn de meningen over de mate hiervan nog sterk verdeeld. Binnen CINQ zien we veel dynamiek rondom dit onderwerp en een actieve discussie over de toekomstige rol van AI binnen onze markt. 

Het lijkt onvermijdelijk dat er verschuivingen zullen plaatsvinden, hoewel de mate van deze verschuivingen nog moeilijk te voorspellen is. Het is nog niet duidelijk of de AI samen met ons of in plaats van ons gaat programmeren. In het meest extreme geval zal de ontwikkelaar van vandaag een sturende en controlerende rol krijgen.

Als we specifiek kijken naar wat er zal verdwijnen, zullen AI tools delen van ons werk overnemen. De mate hiervan is echter nog moeilijk te voorspellen, omdat het afhangt van te veel factoren.

Hier bepalen we de potentie van technieken ten opzichte van de inspanning die het kost om ze eigen te maken.

Inspanning: Bij inspanning gaat het om de inspanning voor onze developers om kennis en kunde op te doen van een onderwerp. Hierbij telt mee dat als onze developers bij een klant al veel met soortgelijke technieken in contact komen, dat de inspanning over het algemeen lager zal liggen. Bijvoorbeeld de inspanning voor CINQ om nieuwe Cloud technieken op te pakken is laag, omdat de meeste developers al veel basis kennis hebben opgedaan bij verschillende klanten.

Potentieel: Bij potentieel gaat het niet alleen om het potentieel van de techniek in de hele markt, maar ook om impact op CINQ en zijn klanten. Voorbeeld: Quantum Computing heeft het potentieel de hele IT markt te veranderen. Echter voor de diensten die wij leveren is het aannemelijk dat Quantum Programming voorlopig nog niet aan de orde is. Hierdoor is de potentie laag. 

Voor CINQ is het goed als de units elkaar beter kunnen vinden en hierdoor elkaar makkelijker kunnen ondersteunen.

Dit willen we bereiken door meer kruisbestuiving te bewerkstelligen tussen de units. Dit doen we door expertise met elkaar te delen en door gezamenlijk projecten te gaan doen. 

Vanuit zowel de DevOps als de Data unit is er behoefte aan meer programmeer expertise. Hierbij kan de Development unit goed ondersteunen. 

Omdat er steeds meer verantwoordelijkheid komt te liggen bij ontwikkelaars, is de kennis die de Data unit heeft van monitoring en observability zeer waardevol voor onze ontwikkelaars. Daarnaast kunnen we van deze expertise gebruikmaken om productieproblemen beter te analyseren en meer controle te hebben over ons productielandschap.

In dit artikel lees je wat onze verwachtingen zijn op het gebied van Development in 2024. We beschrijven trends in de markt en ontwikkelingen binnen de CINQ Development unit.

Onze visie op Development in 2024

Wil je een native app naast een web applicatie? Dan is dit nu duur en tijdrovend. Bij het bouwen van native apps gebruiken ontwikkelaars meestal andere talen, tools en frameworks. Dat betekent extra ontwikkelkosten en een langere time-to-market. Met Flutter bouw jij voortaan een app voor elk scherm vanuit een single codebase. Een win-winsituatie voor veel bedrijven en ontwikkelaars, want de ontwikkelkosten worden verlaagd en de time-to-market wordt versneld. Ik neem je mee waarom Flutter het platform is voor de toekomst.

Bekijk hieronder de presentatie van Jeroen Burgers op de CINQ.Conferentie 2023.

Tegenwoordig kies ik voor Flutter - Jeroen Burgers @ CINQ. Conferentie 2023

Bekijk de presentatie van Jeroen Burgers op de CINQ.Conferentie 2023: "Tegenwoordig kies ik voor Flutter"

Tegenwoordig kies ik voor Flutter

Op donderdag 5 oktober stond CINQ CTO Bouke Nijhuis op Devoxx Belgium met zijn talk "The Battle of the AI coding assistants". 

"So you are hearing more and more about AI coding assistants, but you do not know where to start? In this talk the biggest names in the AI coding space will go head to head. After a short introduction, I (or the AI?) will do several live coding sessions. All the assistants will get some time on stage. Furthermore, the pros and the cons of every tool will be discussed, but also the use of these tools in general. I will give my opinion on when to use which assistant. Finally, I will provide a glimpse into the future of AI coding assistants. After attending this session you should be able to improve your productivity by using the right coding companion."

Bekijk de opname van zijn presentatie hieronder:

The battle of the AI coding assistants By Bouke Nijhuis

The battle of the AI coding assistants

In my previous blog post -  Refactor that ugly code (Part 1) -  I talked about getting an ugly piece of code under test. Getting that code under test enables us to refactor it in a confident and safe manner. A safety net is in place. It is time to drive this thing home. In this post we are going to see if we actually can get rid of those pesky code smells.     

At the end of part 1 we ended up with this code: 

Now it is time to see if this code needs any refactoring. Let’s try to get a whiff of the code smells. Before we start, it is important to notice that the fact of code smells being present in your code base doesn’t necessarily mean that the code needs to be refactored. Code smells aren’t a bad thing, they are just signs that your code might give you headaches in the future. Afterall, why should we go on a refactor frenzy if the code never changes? If it never changes you might as well do it quick and dirty, commit, push and don’t look back.

Unfortunately (but fortunately for this blog, otherwise it would be a really short blog) in almost every case code changes. So we try to anticipate changes. We just don’t know exactly when they will come and what that change actually will be. At the risk of sounding a bit like captain obvious or come across as extremely boring, the easiest way to brace yourself for future changes is to keep your methods small. This doesn’t mean we dive into the code and start breaking it up and extract everything into its own class/method until we have a huge collection of one-liner methods. No, there has to be a reason or thought behind it. The number one reason to break things up in smaller pieces/modules is almost always: “each class should have its own responsibility or reason to exist”.   

We don’t want hollow middleman classes that just pass everything on to the next class. Neither do we want big “God” classes that do everything by themselves. “But what do we want?” you might ask yourself then. When looking at a class, the most important question I ask myself is: “What is the purpose of this class?”. Another great question to ask yourself would be: ”Is this class doing too much?”. If so, we might need to do something about all those responsibilities. 

To see if a class has too many responsibilities we need to determine what the purpose of this class is. Judging by the name of our class (AlarmService) it seems to be some kind of service class that coordinates the process of determining whether the alarm should be switched on or not. If I had to describe what a service class is/does, I would try to explain it with a real world analogy. A service kind of acts like a human traffic controller. The traffic controller blows his whistle, signals you to stop or shouts at you to drive your car in a certain direction when needed. What he certainly does not do is get in the car and drive it himself into the desired direction. Do you see the similarities? The service class shouldn’t be bothered with all that boring low-level stuff. A service should be delegating, that is what it is good at.

Looking at the service class in the code I see that the service has a state (the mutable field alarmStatus). This is a code smell and a red flag! You should never have state in a service class. There are several reasons for not having state in your service. For me, reusability and testability are the main reasons. Currently the alarm service is tightly coupled with the alarm state. The reusability is not as good as it could be. If we receive a new requirement which states that we want to be able to turn on the alarm manually for a reason not related to the pressure of a tire then we still have to use the entire AlarmService to achieve this. The method AlarmService.check() alters the state. AlarmService should not care how to turn on the alarm. It should delegate that to another class. Another thing that seems odd is the fact that the AlarmService itself is performing some sort of calculation to check if the pressure of a tire is within or outside a certain range. 

To sum it all up we can see the following responsibilities for this class:

The class coordinates the decision making logic for turning on an alarm. 

The class also does the decision making itself.

The class behaves like some sort of instrument to measure the pressure.

Ideally  a class should have one responsibility. Although the AlarmService in this example is very small, it already looks like a Swiss army knife! It knows and does too many things. Not ideal if your code is going to change in the future. If we don’t do something about it now, the cost of changing your code in the future can turn out to be really high.  

Another thing to look for in code is the readability of the code. Can we comprehend the code without reading the code over and over again? For example, if we look at the check method in the screenshot below we see that it isn’t a very long and complex method. But, besides the wrong naming of the threshold variables (variables that start with a capital letter?), that if-statement isn’t very reader-friendly so to speak. Every time I read it I am mentally reasoning what it actually is doing. This could be optimized. Let’s add this to the list of things to refactor.

Okay, enough complaining. Let's actually do something about that code. Based on the gathered responsibilities and weird things already spotted I came up with the following todo list:

break the hard dependency that the service has on the TireSensor

get rid of the state in the service and extract the alarm logic into its own class

delegate the actual assessment of the pressure into its own class

make sure the code is readable/understandable

First thing on the todo list: We want to break the hard dependency that the service has on the TireSensor class. We want that ugly “new TireSensor()” statement out of the code. 

We could accomplish this by injecting the sensor into the AlarmService. My first thought would be to do that via the constructor. And this is where my first design decision comes into play. Because, a service is typically instantiated as a Singleton and if I then inject the sensor via the constructor I would never be able to use/check multiple sensors without instantiating multiple alarm services (which is very hard to do when the service in question is a Singleton). If I always want to check the same sensor during the lifetime of the app, injecting it via the constructor would be fine. However, in this case, I think it would be wiser to inject it via the AlarmService.check() method. We now have some flexibility and anticipate being able to measure multiple sensors in the future. Sounds like a good reason that justifies the decision to inject it through the method.

By the way, I say "justifies" which kind of implies that taking another decision is wrong. But that’s not the case at all. I rather prefer the phrase “each design decision has a trade off” instead of talking about right or wrong.

After altering and adding unit tests and changing my code, it now looks like this. We are providing the dependency to the service through the method. Sweet! One down, several to go! 

Oh and by the way. You remember in my previous blog Refactor that ugly code (Part 1) we ended up with an inner TestableAlarmService class? We don't need that anymore! We can supply the mock through the check method. Cleaner tests! Win-win!

The next thing we want to improve is separating the actual alarm from the service. The AlarmService shouldn’t be responsible for keeping track of the state of an alarm. It’s time to create an alarm and release the service from that burden.

The way the alarm is working now is that you can turn it on once and you can never turn it off again. It sounds like that could use some improvement too. Let’s also change that while we are at it. STOP! This would be the point to stop typing, get away from the keyboard as far as you can and try to remember what the goal of this whole change was in the first place. The goal here is not to add any additional requirements and/or features. We are refactoring! Which means we want to improve the code (quality) without altering the behavior of the system. We can put this new idea on the backlog and carry on with our initial goal. After writing the tests upfront we can now start to implement the Alarm class.

And of course we want to use this alarm in our AlarmService. We inject it into our service. Via the constructor this time. This is also a design decision! I expect that we don’t need to use several different alarms when using the AlarmService. A constructor injection seems alright here. If this decision doesn’t turn out to be the right decision, it isn’t the end of the world! The main thing to keep in mind is that after refactoring you are always in a much better shape for maintaining your code than before we started this whole excercise. So, don’t be scared to make decisions. I would rather make a decision with certain trade offs which might be “wrong” but still improve your code quality. It is always better than doing nothing and having a crappy code quality.

We are progressing quite nicely. The alarm is extracted and injected, the sensor is injected… What else? Next up is moving the logic for the actual assessment of the pressure out of the class.

Now that we are on a roll we want to take away even more responsibilities from the service class. We could (and we should) put the logic/calculation, to determine if the measured pressure falls within or outside a certain range, into a separate class. 

One of the other things which is also very important is naming things. The naming should be logical (well, duh!). As logical as it sounds, it is also a hard thing to do. You don't want to end up with long class names like “TheTireSensorPressureRangeChecker”. On the other hand, you don’t want to be too vague either, like calling it “SensorManager” or something along those lines. What helps me name things is to try and think of a real world example that resembles what you want the class to do or to represent. 

“PressureGauge” seems to be a good name. And as always, we write the tests first! And we can do that fairly quickly because we already have the test cases in the AlarmServiceTest class from part 1. After that it is time to implement the gauge. In this implementation the gauge accepts a TireSensor, measures the pressure, assesses whether the pressure is too low, too high or optimal (based on the thresholds) and returns the result of the assessment (PressureState) to the calling client .  

A practical tip: it is good practice to “return” to the calling class as soon as you know the answer, instead of storing your result in a temporary variable and “returning” at the end of the method. Another great tip is that if for some reason you do need to store the value in a temporary variable, place it as close as you can to the place where you need the variable (pressure variable is right above the if-statement where it is first used). 

And just like we have seen with the alarm we also want to use this implementation and inject it in the AlarmService. And we're done! I already mentioned that naming things is important. I chose to rename the AlarmService into PressureMonitorService. This way the class name reflects the purpose of the service way better.

Note how we wrapped the delegation of the pressure assessment and alarm activation in a “pressureIsNotOptimal()” and an “activateAlarm()” method. The check method now almost reads like a normal sentence. The responsibilities are separated quite nicely and if another developer now looks at the code, he can immediately see what the intention of the class is and what the PressureMonitorService.check() method does. 

By the way, I also removed the method getAlarmStatus(). It feels a bit out of place. Since we now have a separate alarm, other classes that are depending on the alarm shouldn’t have to call that method via the AlarmService. Every other class in the system that needs to know the status of the alarm should be depending on the alarm and not this service class. Important thing to keep in mind is that other classes in the system could already call the getAlarmStatus() method. If we remove the method we also have to fix that. So before you delete a method you should always check where that method is used. If you have to change the whole application when you delete the method then you might want to postpone this action and do this in another user story. 

We refactored a lot but looking at the PressureMonitorService it was all worth it. We are ready for future changes! At the beginning we started with three classes/objects and no tests. Now that we are finished we ended up with twice as many classes/objects plus a fair amount of unit tests. The code has been split up in cohesive and logically bundled code blocks. The responsibilities are finally in the classes where they should be!

One last thing… When I show this code to other developers, there will always be a few that say: “But now I can’t see in one glance what the AlarmService is doing exactly”, “The calculation for assessing the pressure is in a separate class”, “The alarm and its logic is in another class”. "The code is scattered all over the place", “In the old code the only thing I had to do was open the AlarmService to know everything”.

My question to them would be: “Why do you want to know everything?”. You should not need to know everything in order to change something. If I had to choose between: 

putting all the logic in the AlarmService in order to literally know everything at a glance or

separating all the responsibilities into well designed/balanced classes that work together in a logical way and be better prepared for future changes…

… I would always go for the latter!  And if you do want to know everything in one glance, then you probably should have chosen a procedural language instead of Java to begin with.

As always, the code is available on my GitHub:

https://github.com/vlouisa/tire-pressure-kata/tree/tpms-service-refactoring

If you check out my code you might notice that the pressureGauge class looks entirely different than the one in this blog. That's because I was not done yet with my refactoring. I was experimenting and seeing if it is handy and/or useful to apply the Chain of Responsibility pattern to that class. But including that also in this blog is beyond the point I was trying to bring across. Zooming in on that though might be a nice sequel to this part. If you think this would be a great topic for another blog, let me know.

In this blog software engineer Victor Louisa shows you how to refactor existing code to get rid of the code smells.

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