API 4 Future

Many ideas are excellent on paper. However, people often lack the knowledge of how to implement brilliant concepts into their everyday work. This short workshop aims to bridge the gap between theory and practice and demonstrates the steps needed to achieve a stable API in the long term.

(c) 2021 Marco Schulz, Java PRO Ausgabe 1, S.31-34

When developing commercial software, many people involved often don’t realize that the application will be in use for a long time. Since our world is constantly changing, it’s easy to foresee that the application will require major and minor changes over the years. The project becomes a real challenge when the application to be extended is not isolated, but communicates with other system components. This means that in most cases, the users of the application also have to be adapted. A single stone quickly becomes an avalanche. With good avalanche protection, the situation can still be controlled. However, this is only possible if you consider that the measures described below are solely intended for prevention. But once the violence has been unleashed, there is little that can be done to stop it. So let’s first clarify what an API is.

A Matter of Negotiation

A software project consists of various components, each with its own specialized tasks. The most important are source code, configuration, and persistence. We’ll be focusing primarily on the source code area. I’m not revealing anything new when I say that implementations should always be against interfaces. This foundation is already taught in the introduction to object-oriented programming. In my daily work, however, I often see that many developers aren’t always fully aware of the importance of developing against interfaces, even though this is common practice when using the Java Standard API. The classic example of this is:

List<String> collection = new ArrayList<>();

This short line uses the List interface, which is implemented as an ArrayList. Here we can also see that there is no suffix in the form of an “I” to identify the interface. The corresponding implementation also does not have “Impl” in its name. That’s a good thing! Especially with the implementation class, various solutions may be desired. In such cases, it is important to clearly label them and keep them easily distinguishable by name. ListImpl and ListImpl2 are understandably not as easy to distinguish as ArrayList and LinkedList. This also clears up the first point of a stringent and meaningful naming convention.

In the next step, we’ll focus on the program parts that we don’t want to expose to consumers of the application, as they are helper classes. Part of the solution lies in the structure of how the packages are organized. A very practical approach is:

  • my.package.path.business: Contains all interfaces
  • my.package.path.application: Contains the interface implementations
  • my.package.path.application.helper: Contains internal helper classes

This simple architecture alone signals to other programmers that it’s not a good idea to use classes from the helper package. Starting with Java 9, there are even more far-reaching restrictions prohibiting the use of internal helper classes. Modularization, which was introduced in Java 9 with the Jingsaw project [1], allows packages to be hidden from view in the module-info.java module descriptor.

Separatists and their Escape from the Crowd

A closer look at most specifications reveals that many interfaces have been outsourced to their own libraries. From a technological perspective, based on the previous example, this would mean that the business package, which contains the interfaces, is outsourced to its own library. The separation of API and the associated implementation fundamentally makes it easier to interchange implementations. It also allows a client to exert greater influence over the implementation of their project with their contractual partner, as the developer receives the API pre-built by the client. As great as the idea is, a few rules must be observed to ensure it actually works as originally intended.

Example 1: JDBC. We know that Java Database Connectivity is a standard for connecting various database systems to an application. Aside from the problems associated with using native SQL, MySQL JDBC drivers cannot simply be replaced by PostgreSQL or Oracle. After all, every manufacturer deviates more or less from the standard in their implementation and also provides exclusive functionality of their own product via the driver. If you decide to make extensive use of these additional features in your own project, the easy interchangeability is over.

Example 2: XML. Here, you have the choice between several standards. It’s clear, of course, that the APIs of SAX, DOM, and StAX are incompatible. For example, if you want to switch from DOM to event-based SAX for better performance, this can potentially result in extensive code changes.

Example 3: PDF. Last but not least, I have a scenario for a standard that doesn’t have a standard. The Portable Document Format itself is a standard for how document files are structured, but when it comes to implementing usable program libraries for their own applications, each manufacturer has its own ideas.

These three small examples illustrate the common problems that must be overcome in daily project work. A small rule can have a big impact: only use third-party libraries when absolutely necessary. After all, every dependency used also poses a potential security risk. It’s also not necessary to include a library of just a few MB to save the three lines required to check a string for null and empty values.

Model Boys

If you’ve decided on an external library, it’s always beneficial to do the initial work and encapsulate the functionality in a separate class, which you can then use extensively. In my personal project TP-CORE on GitHub [2], I’ve done this in several places. The logger encapsulates the functionality of SLF4J and Logback. Compared to the PdfRenderer, the method signatures are independent of the logging libraries used and can therefore be more easily exchanged via a central location. To encapsulate external libraries in your own application as much as possible, the following design patterns are available: wrapper, facade, and proxy.

Wrapper: also called the adaptor pattern, belongs to the group of structural patterns. The wrapper couples one interface to another that are incompatible.

Facade: is also a structural pattern and bundles several interfaces into a simplified interface.

Proxy: also called a representative, also belongs to the category of structural patterns. Proxies are a generalization of a complex interface. They can be understood as complementary to the facade, which combines multiple interfaces into a single one.

It is certainly important in theory to separate these different scenarios in order to describe them correctly. In practice, however, it is not critical if hybrid forms of the design patterns presented here are used to encapsulate external functionality. For anyone interested in exploring design patterns in more depth, we recommend the book “Design Patterns from Head to Toe” [3].

Class Reunion

Another step toward a stable API is detailed documentation. Based on the interfaces discussed so far, there’s a small library that allows methods to be annotated based on the API version. In addition to status and version information, the primary implementations for classes can be listed using the consumers attribute. To add API Gaurdian to your project, you only need to add a few lines to the POM and replace the ${version} property with the current version.

 <dependency>
    <groupId>org.apiguardian</groupId>
    <artifactId>apiguardian-api</artifactId>
    <version>${version}</version>
 </dependency>

Marking up methods and classes is just as easy. The @API annotation has the attributes: status, since, and consumers. The following values ​​are possible for status:

  • DEPRECATED: Deprecated, should not be used any further.
  • EXPERIMENTAL: Indicates new features for which the developer would like feedback. Use with caution, as changes can always occur.
  • INTERNAL: For internal use only, may be discontinued without warning.
  • STABLE: Backward-compatible feature that remains unchanged for the existing major version.
  • MAINTAINED: Ensures backward stability for the future major release.

Now that all interfaces have been enriched with this useful meta information, the question arises where the added value can be found. I simply refer you to Figure 1, which demonstrates everyday work.

Figure 1: Suggestion in Netbeans with @API annotation in the JavaDoc

For service-based RESTful APIs, there is another tool called Swagger [4]. This also follows the approach of creating API documentation from annotations. However, Swagger itself scans Java web service annotations instead of introducing its own. It is also quite easy to use. All that is required is to integrate the swagger-maven-plugin and specify the packages in which the web services reside in the configuration. Subsequently, a description is created in the form of a JSON file for each build, from which Swagger UI then generates executable documentation. Swagger UI itself is available as a Docker image on DockerHub [5].

<plugin>
   <groupId>io.swagger.core.v3</groupId>
   <artifactId>swagger-maven-plugin</artifactId>
   <version>${version}</version>
   <configuration>
      <outputFileName>swagger</outputFileName>
      <outputFormat>JSON</outputFormat>
      <resourcePackages>
          <package>org.europa.together.service</package>
      </resourcePackages>
      <outputPath>${project.build.directory}</outputPath>
   </configuration>
</plugin>
Figure 2: Swagger UI documentation of the TP-ACL RESTful API.

Versioning is an important aspect for APIs. Using semantic versioning, a lot can be gleaned from the version number. Regarding an API, the major segment is significant. This first digit indicates API changes that are incompatible with each other. Such incompatibility includes the removal of classes or methods. However, changing existing signatures or the return value of a method also requires adjustments from consumers as part of a migration. It’s always a good idea to bundle work that causes incompatibilities and publish it less frequently. This demonstrates project stability.

Versioning is also recommended for Web APIs. This is best done via the URL by including a version number. So far, I’ve had good experiences with only incrementing the version when incompatibilities occur.

Relationship Stress

The great advantage of a RESTful service, being able to get along well with “everyone,” is also its greatest curse. This means that a great deal of care must be taken, as many clients are being served. Since the interface is a collection of URIs, our focus is on the implementation details. For this, I’ll use an example from my TP-ACL project, which is also available on GitHub.

RolesDO role = rolesDAO.find(roleName);
String json = rolesDAO.serializeAsJson(role);
if (role != null) {
    response = Response.status(Response.Status.OK)
            .type(MediaType.APPLICATION_JSON)
            .entity(json)
            .encoding("UTF-8")
            .build();
} else {
    response = Response.status(Response.Status.NOT_FOUND).build();
}

This is a short excerpt from the try block of the fetchRole method found in the RoleService class. The GET request returns a 404 error code if a role is not found. You probably already know what I’m getting at.

When implementing the individual actions GET, PUT, DELETE, etc. of a resource such as a role, it’s not enough to simply implement the so-called HappyPath. The possible stages of such an action should be considered during the design phase. For the implementation of a consumer (client), it makes a significant difference whether a request that cannot be completed with a 200 failed because the resource does not exist (404) or because access was denied (403). Here, I’d like to allude to the telling Windows message about the unexpected error.

Conclusion

When we talk about an API, we mean an interface that can be used by other programs. A major version change indicates to API consumers that there is an incompatibility with the previous version. This may require adjustments. It is completely irrelevant what type of API it is or whether the application uses it publicly or internally via the fetchRole method. The resulting consequences are identical. For this reason, you should carefully consider the externally visible areas of your application.

Work that leads to API incompatibility should be bundled by release management and, if possible, released no more than once per year. This also demonstrates the importance of regular code inspections for consistent quality.

Resources

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jConf Peru 2021 [2]

Mission accomplished: deploy to Maven Central

Many developers have ideas they work on it in their spare time. The most of us are convinced about open source and share their own projects on platforms like GitHub. But what happen after a publication of the source code? If you really want to gain people to use your project you’re not done yet. It’s also a good idea to publish your artifacts for a simple usage.

The most famous storage for binary Java Artifacts is Maven Central. Doesn‘t matter if you use in your projects Ivy, Gradle or Maven as dependency management, all those technologies access to Maven Central. In this talk you will learn how to publish your artifacts with Maven to Sonatype Nexuss OSS. We pass through all steps from creating accounts until the the binaries are available. In between I give some general hints about the usage of repository managers and helpful tricks for a lightweight Release Management.

Learn things about token replacement, executable jar, BOM, Dependency Management, enforcement, reporting and much more in live demonstrations.

BarCamp República Dominicana 2021

API4Future: A journey how to create a stable API

By experience, enterprise applications have a long lifetime period. This is one oft the  reasons why we need to pay a lot of attention for maintenance. But how we are able to avoid chaos in the project? With some Code Examples in Java I will demonstrate how a stable API get developed. Some questions I will answer in this talk:

  • Quality and Maintenance
  • Architecture & Design Pattern for APIs
  • API Documentation & Versioning

{j}DD Poland 2021

Refactoring Disasters: A Story how I failed

For my small Open Source project TP-CORE, you can find it on GitHub, I had the gorgeous Idea to replace the iText library for OpenPDF. After I made a plan how I could reach my goal I started all necessary activities. But in real life the things never that easy like we have originally in mind. I failed with my idea and in this talk I will let you know what happened exactly. I talk about my motivation why I wanted the replacements and how was my plan to success all activities. You will get to know how it was when I reached the point, I realized I will not make it. I give a brief explanation what I did that this short adventure did not affect the rest of the project.

Version Number Anti-Patterns

After the gang of four (GOF) Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides published the book, Design Patterns: Elements of Reusable Object-Oriented Software, learning how to describe problems and solutions became popular in almost every field in software development. Likewise, learning to describe don’ts and anti-pattern became equally as popular.

In publications that discussed these concepts, we find helpful recommendations for software design, project management, configuration management, and much more. In this article, I will share my experience dealing with version numbers for software artifacts.

Most of us are already familiar with a method called semantic versioning, a powerful and easy-to-learn rule set for how version numbers have to be structured and how the segments should increase.

Version numbering example:

  • Major: Incompatible API changes.
  • Minor: Add new functionality.
  • Patch: Bugfixes and corrections.
  • Label: SNAPSHOT marking the “under development” status.

An incompatible API Change occurs when an externally accessible function or class was deleted or renamed. Another possibility is a change in the signature of a method. This means the return value or parameters has been changed from its original implementation. In these scenarios, it’s necessary to increase the Major segment of the version number. These changes present a high risk for API consumers because they need to adapt their own code.

When dealing with version numbers, it’s also important to know that 1.0.0 and 1.0 are equal. This has effect to the requirement that versions of a software release have to be unique. If not, it’s impossible to distinguish between artifacts. Several times in my professional experience, I was involved in projects where there was no well-defined processes for creating version numbers. The effect of these circumstances was that the team had to secure the quality of the artifact and got confused with which artifact version they were currently dealing with.

The biggest mistake I ever saw was the storage of the version of an artifact in a database together with other configuration entries. The correct procedure should be: place the version inside the artifact in a way that no one after a release can change from outside. The trap you could fall into is the process of how to update the version after a release or installation.

Maybe you have a checklist for all manual activities during a release. But what happens after a release is installed in a testing stage and for some reason another version of the application has to be installed. Are you still aware of changing the version number manually? How do you find out which version is installed or when the information of the database is incorrect?

Detect the correct version in this situation is a very difficult challenge. For that reason, we have the requirement to keep the version inside of the application. In the next step, we will discuss a secure and simple way on how to solve an automatic approach to this problem.

Our precondition is a simple Java library build with Maven. By default, the version number of the artifact is written down in the POM. After the build process, our artifact is created and named like: artifact-1.0.jar or similar. As long we don’t rename the artifact, we have a proper way to distinguish the versions. Even after a rename with a simple trick of packaging and checking, then, in the META-INF folder, we are able to find the correct value.

If you have the Version hardcoded in a property or class file, it would also work fine, as long you don’t forget to always update it. Maybe the branching and merging in SCM systems like Git could need your special attention to always have the correct version in your codebase.

Another solution is using Maven and the token placement mechanism. Before you run to try it out in your IDE, keep in mind that Maven uses to different folders: sources and resources. The token replacement in sources will not work properly. After a first run, your variable is replaced by a fixed number and gone. A second run will fail. To prepare your code for the token replacement, you need to configure Maven as a first in the build lifecycle:

<build>
   <resources>
      <resource>
         <directory>src/main/resources/</directory>
         <filtering>true</filtering>
      </resource> 
   </resources>
   <testResources>
      <testResource>
         <directory>src/test/resources/</directory>
         <filtering>true</filtering>
      </testResource>
   </testResources>
</build>

After this step, you need to know the ${project.version} property form the POM. This allows you to create a file with the name version.property in the resources directory. The content of this file is just one line: version=${project.version}. After a build, you find in your artifact the version.property with the same version number you used in your POM. Now, you can write a function to read the file and use this property. You could store the result in a constant for use in your program. That’s all you have to do!

Example: https://github.com/ElmarDott/TP-CORE/blob/master/src/main/java/org/europa/together/utils/Constraints.java

Non-Functional Requirements: Quality

By experience, most of us know how difficult it is to express what we mean talking about quality. Why is that so?  There exist many different views on quality and every one of them has its importance. What has to be defined for our project is something that fits its needs and works with the budget. Trying to reach perfectionism can be counterproductive if a project is to be terminated successfully. We will start based on a research paper written by B. W. Boehm in 1976 called “Quantitative evaluation of software quality.” Boehm highlights the different aspects of software quality and the right context. Let’s have a look more deeply into this topic.

When we discuss quality, we should focus on three topics: code structure, implementation correctness, and maintainability. Many managers just care about the first two aspects, but not about maintenance. This is dangerous because enterprises will not invest in individual development just to use the application for only a few years. Depending on the complexity of the application the price for creation could reach hundreds of thousands of dollars. Then it’s understandable that the expected business value of such activities is often highly estimated. A lifetime of 10 years and more in production is very typical. To keep the benefits, adaptions will be mandatory. That implies also a strong focus on maintenance. Clean code doesn’t mean your application can simply change. A very easily understandable article that touches on this topic is written by Dan Abramov. Before we go further on how maintenance could be defined we will discuss the first point: the structure.

Scaffolding Your Project

An often underestimated aspect in development divisions is a missing standard for project structures. A fixed definition of where files have to be placed helps team members find points of interests quickly. Such a meta-structure for Java projects is defined by the build tool Maven. More than a decade ago, companies tested Maven and readily adopted the tool to their established folder structure used in the projects. This resulted in heavy maintenance tasks, given the reason that more and more infrastructure tools for software development were being used. Those tools operate on the standard that Maven defines, meaning that every customization affects the success of integrating new tools or exchanging an existing tool for another.

Another aspect to look at is the company-wide defined META architecture. When possible, every project should follow the same META architecture. This will reduce the time it takes a new developer to join an existing team and catch up with its productivity. This META architecture has to be open for adoptions which can be reached by two simple steps:

  1. Don’t be concerned with too many details;
  2. Follow the KISS (Keep it simple, stupid.) principle.

A classical pattern that violates the KISS principle is when standards heavily got customized. A very good example of the effects of strong customization is described by George Schlossnagle in his book “Advanced PHP Programming.” In chapter 21 he explains the problems created for the team when adopting the original PHP core and not following the recommended way via extensions. This resulted in the effect that every update of the PHP version had to be manually manipulated to include its own development adaptations to the core. In conjunction, structure, architecture, and KISS already define three quality gates, which are easy to implement.

The open-source project TP-CORE, hosted on GitHub, concerns itself with the afore-mentioned structure, architecture, and KISS. There you can find their approach on how to put it in practice. This small Java library rigidly defined the Maven convention with his directory structure. For fast compatibility detection, releases are defined by semantic versioning. The layer structure was chosen as its architecture and is fully described here. Examination of their main architectural decisions concludes as follows:

Each layer is defined by his own package and the files following also a strict rule. No special PRE or POST-fix is used. The functionality Logger, for example, is declared by an interface called Logger and the corresponding implementation LogbackLogger. The API interfaces can detect in the package “business” and the implementation classes located in the package “application.” Naming like ILogger and LoggerImpl should be avoided. Imagine a project that was started 10 years ago and the LoggerImpl was based on Log4J. Now a new requirement arises, and the log level needs to be updated during run time. To solve this challenge, the Log4J library could be replaced with Logback. Now it is understandable why it is a good idea to name the implementation class like the interface, combined with the implementation detail: it makes maintenance much easier! Equal conventions can also be found within the Java standard API. The interface List is implemented by an ArrayList. Obviously, again the interface is not labeled as something like IList and the implementation not as ListImpl .

Summarizing this short paragraph, a full measurement rule set was defined to describe our understanding of structural quality. By experience, this description should be short. If other people can easily comprehend your intentions, they willingly accept your guidance, deferring to your knowledge. In addition, the architect will be much faster in detecting rule violations.

Measure Your Success

The most difficult part is to keep a clean code. Some advice is not bad per se, but in the context of your project, may not prove as useful. In my opinion, the most important rule would be to always activate the compiler warning, no matter which programming language you use! All compiler warnings will have to be resolved when a release is prepared. Companies dealing with critical software, like NASA, strictly apply this rule in their projects resulting in utter success.

Coding conventions about naming, line length, and API documentation, like JavaDoc, can be simply defined and observed by tools like Checkstyle. This process can run fully automated during your build. Be careful; even if the code checkers pass without warnings, this does not mean that everything is working optimally. JavaDoc, for example, is problematic. With an automated Checkstyle, it can be assured that this API documentation exists, although we have no idea about the quality of those descriptions.

There should be no need to discuss the benefits of testing in this case; let us rather take a walkthrough of test coverage. The industry standard of 85% of covered code in test cases should be followed because coverage at less than 85% will not reach the complex parts of your application. 100% coverage just burns down your budget fast without resulting in higher benefits. A prime example of this is the TP-CORE project, whose test coverage is mostly between 92% to 95%. This was done to see real possibilities.

As already explained, the business layer contains just interfaces, defining the API. This layer is explicitly excluded from the coverage checks. Another package is called internal and it contains hidden implementations, like the SAX DocumentHandler. Because of the dependencies the DocumentHandler is bound to, it is very difficult to test this class directly, even with Mocks. This is unproblematic given that the purpose of this class is only for internal usage. In addition, the class is implicitly tested by the implementation using the DocumentHandler. To reach higher coverage, it also could be an option to exclude all internal implementations from checks. But it is always a good idea to observe the implicit coverage of those classes to detect aspects you may be unaware of.

Besides the low-level unit tests, automated acceptance tests should also be run. Paying close attention to these points may avoid a variety of problems. But never trust those fully automated checks blindly! Regularly repeated manual code inspections will always be mandatory, especially when working with external vendors. In our talk at JCON 2019, we demonstrated how simply test coverage could be faked. To detect other vulnerabilities you can additionally run checkers like SpotBugs and others more.

Tests don’t indicate that an application is free of failures, but they indicate a defined behavior for implemented functionality.

For a while now, SCM suites like GitLab or Microsoft Azure support pull requests, introduced long ago in GitHub. Those workflows are nothing new; IBM Synergy used to apply the same technique. A Build Manager was responsible to merge the developers’ changes into the codebase. In a rapid manner, all the revisions performed by the developer are just added into the repository by the Build Manager, who does not hold a sufficiently profound knowledge to decide about the implementation quality. It was the usual practice to simply secure that the build is not broken and always the compile produce an artifact.

Enterprises have discovered this as a new strategy to handle pull requests. Now, managers often make the decision to use pull requests as a quality gate. In my personal experience, this slows down productivity because it takes time until the changes are available in the codebase. Understanding of the branch and merge mechanism helps you to decide for a simpler branch model, like release branch lines. On those branches tools like SonarQube operate to observe the overall quality goal.

If a project needs an orchestrated build, with a defined order how artifacts have to create, you have a strong hint for a refactoring.

The coupling between classes and modules is often underestimated. It is very difficult to have an automated visualization for the bindings of modules. You will find out very fast the effect it has when a light coupling is violated because of an increment of complexity in your build logic.

Repeat Your Success

Rest assured, changes will happen! It is a challenge to keep your application open for adjustments. Several of the previous recommendations have implicit effects on future maintenance. A good source quality simplifies the endeavor of being prepared. But there is no guarantee. In the worst cases the end of the product lifecycle, EOL is reached, when mandatory improvements or changes cannot be realized anymore because of an eroded code base, for example.

As already mentioned, light coupling brings with it numerous benefits with respect to maintenance and reutilization. To reach this goal is not that difficult as it might look. In the first place, try to avoid as much as possible the inclusion of third-party libraries. Just to check if a String is empty or NULL it is unnecessary to depend on an external library. These few lines are fast done by oneself. A second important point to be considered in relation to external libraries: “Only one library to solve a problem.” If your project deals with JSON then decide one one implementation and don’t incorporate various artifacts. These two points heavily impact on security: a third-party artifact we can avoid using will not be able to cause any security leaks.

After the decision is taken for an external implementation, try to cover the usage in your project by applying design patterns like proxy, facade, or wrapper. This allows for a replacement more easily because the code changes are not spread around the whole codebase. You don’t need to change everything at once if you follow the advice on how to name the implementation class and provide an interface. Even though a SCM is designed for collaboration, there are limitations when more than one person is editing the same file. Using a design pattern to hide information allows you an iterative update of your changes.

Conclusion

As we have seen: a nonfunctional requirement is not that difficult to describe. With a short checklist, you can clearly define the important aspects for your project. It is not necessary to check all points for every code commit in the repository, this would with all probability just elevate costs and doesn’t result in higher benefits. Running a full check around a day before the release represents an effective solution to keep quality in an agile context and will help recognizing where optimization is necessary. Points of Interests (POI) to secure quality are the revisions in the code base for a release. This gives you a comparable statistic and helps increasing estimations.

Of course, in this short article, it is almost impossible to cover all aspects regarding quality. We hope our explanation helps you to link theory by examples to best practice. In conclusion, this should be your main takeaway: a high level of automation within your infrastructure, like continuous integration, is extremely helpful, but doesn’t prevent you from manual code reviews and audits.

Checklist

  • Follow common standards
  • KISS – keep it simple, stupid!
  • Equal directory structure for different projects
  • Simple META architecture, which can reuse as much as possible in other projects
  • Defined and follow coding styles
  • If a release got prepared – no compiler warnings are accepted
  • Have test coverage up to 85%
  • Avoid third-party libraries as much as possible
  • Don’t support more than one technology for a specific problem (e. g., JSON)
  • Cover foreign code by a design pattern
  • Avoid strong object/module coupling

Acceptance Tests in Java With JGiven

Most of the developer community know what a unit test is, even they don’t write them. But there is still hope. The situation is changing. More and more projects hosted on GitHub contain unit tests.

In a standard set-up for Java projects like NetBeans, Maven, and JUnit, it is not that difficult to produce your first test code. Besides, this approach is used in Test Driven Development (TDD) and exists in other technologies like Behavioral Driven Development (BDD), also known as acceptance tests, which is what we will focus on in this article.

Difference Between Unit and Acceptance Tests

The easiest way to become familiar with this topic is to look at a simple comparison between unit and acceptance tests. In this context, unit tests are very low level. They execute a function and compare the output with an expected result. Some people think differently about it, but in our example, the only one responsible for a unit test is the developer.

Keep in mind that the test code is placed in the project and always gets executed when the build is running. This provides quick feedback as to whether or not something went wrong. As long the test doesn’t cover too many aspects, we are able to identify the problem quickly and provide a solution. The design principle of those tests follows the AAA paradigm. Define a precondition (Arrange), execute the invariant (Act), and check the postconditions (Assume). We will come back to this approach a little later.

When we check the test coverage with tools like JaCoCo and cover more than 85 percent of our code with test cases, we can expect good quality. During the increasing test coverage, we specify our test cases more precisely and are able to identify some optimizations. This can be removing or inverting conditions because during the tests we find out, it is almost impossible to reach those sections. Of course, the topic is a bit more complicated, but those details could be discussed in another article.

Acceptance test are same classified like unit tests. They belong to the family of regression tests. This means we want to observe if changes we made on the code have no effects on already worked functionality. In other words, we want to secure that nothing already is working got broken, because of some side effects of our changes. The tool of our choice is JGiven [1]. Before we look at some examples, first, we need to touch on a bit of theory.

JGiven In-Depth

The test cases we define in JGiven is called a scenario. A scenario is a collection of four classes, the scenario itself, the Given displayed as given (Arrange), the Action displayed as when (Act) and Outcome displayed as then (Assume).

In most projects, especially when there is a huge amount of scenarios and the execution consumes a lot of time, acceptance tests got organized in a separate project. With a build job on your CI server, you can execute those tests once a day to get fast feedback and to react early if something is broken. The code example we demonstrate contains everything in one project on GitHub [2] because it is just a small library and a separation would just over-engineer the project. Usually, the one responsible for acceptance tests is the test center, not the developer.

The sample project TP-CORE is organized by a layered architecture. For our example, we picked out the functionality for sending e-mails. The basic functionality to compose an e-mail is realized in the application layer and has a test coverage of up to 90 percent. The functionality to send the e-mail is defined in the service layer.

In our architecture, we decided that the service layer is our center of attention to defining acceptance tests. Here, we want to see if our requirement to send an e-mail is working well. Supporting this layer with our own unit tests is not that efficient because, in commercial projects, it just produces costs without winning benefits. Also, having also unit tests means we have to do double the work because our JGiven tests already demonstrate and prove that our function is well working. For those reasons, it makes no sense to generate test coverage for the test scenarios of the acceptance test.

Let’s start with a practice example. At first, we need to include our acceptance test framework into our Maven build. In case you prefer Gradle, you can use the same GAV parameters to define the dependencies in your build script.

<dependency>
   <groupId>com.tngtech.jgiven</groupId>
   <artifactId>jgiven-junit</artifactId>
   <version>0.18.2</version>
   <scope>test</scope>
</dependency>
XML

Listing 1: Dependency for Maven.

As you can see in listing 1, JGiven works well together with JUnit. An integration to TestNG also exists , you just need to replace the artifactId for jgiven-testng. To enable the HTML reports, you need to configure the Maven plugin in the build lifecycle, like it is shown in Listing 2.

<build> 
   <plugins> 
      <plugin>
         <groupId>com.tngtech.jgiven</groupId>
         <artifactId>jgiven-maven-plugin</artifactId>
         <version>0.18.2</version>
         <executions>
            <execution>
               <goals>
                  <goal>report</goal>
               </goals>
            </execution>
         </executions>
         <configuration>
            <format>html</format>
         </configuration>
      </plugin>
   </plugins>
</build>
XML

Listing 2: Maven Plugin Configuration for JGiven.

The report of our scenarios in the TP-CORE project is shown in image 1. As we can see, the output is very descriptive and human-readable. This result will be explained by following some naming conventions for our methods and classes, which will be explained in detail below. First, let’s discuss what we can see in our test scenario. We defined five preconditions:

  1. The configuration for the SMPT server is readable
  2. The SMTP server is available
  3. The mail has a recipient
  4. The mail has attachments
  5. The mail is full composed

If all these conditions are true, the action will send a single e-mail got performed. Afterward, after the SMTP server is checked, we see that the mail has arrived. For the SMTP service, we use the small Java library greenmail [3] to emulate an SMTP server. Now it is understandable why it is advantageous for acceptance tests if they are written by other people. This increases the quality as early on conceptional inconsistencies appear. Because as long as the tester with the available implementations cannot map the required scenario, the requirement is not fully implemented.

Producing Descriptive Scenarios

Now is the a good time to dive deeper into the implementation details of our send e-mail test scenario. Our object under test is the class MailClientService. The corresponding test class is  MailClientScenarioTest, defined in the test packages. The scenario class definition is shown in listing 3.

@RunWith(JUnitPlatform.class) 
public class MailClientScenarioTest
       extends <strong>ScenarioTest</strong><MailServiceGiven, MailServiceAction, MailServiceOutcome> { 
    // do something 
}

Listing 3: Acceptance Test Scenario for JGiven.

As we can see, we execute the test framework with JUnit5. In the  ScenarioTest, we can see the three classes: Given, Action, and Outcome in a special naming convention. It is also possible to reuse already defined classes, but be careful with such practices. This can cost some side effects. Before we now implement the test method, we need to define the execution steps. The procedure for the three classes are equivalent.

@RunWith(JUnitPlatform.class) 
public class MailServiceGiven 
       extends <strong>Stage</strong><MailServiceGiven> { 

    public MailServiceGiven email_has_recipient(MailClient client) {
        try { 
            assertEquals(1, client.getRecipentList().size());
        } catch (Exception ex) {
            System.err.println(ex.getMessage);
        }
        return self(); 
    } 
} 

@RunWith(JUnitPlatform.class)
public class MailServiceAction
       extends <strong>Stage</strong><MailServiceAction> { 

    public MailServiceAction send_email(MailClient client) {
        MailClientService service = new MailClientService();
        try {
            assertEquals(1, client.getRecipentList().size());
            service.sendEmail(client);
        } catch (Exception ex) { 
            System.err.println(ex.getMessage);
        }
        return self();
    }
}

@RunWith(JUnitPlatform.class)
public class MailServiceOutcome 
       extends <strong>Stage</strong><MailServiceOutcome> {

    public MailServiceOutcome email_is_arrived(MimeMessage msg) { 
         try {
             Address adr = msg.getAllRecipients()[0];
             assertEquals("JGiven Test E-Mail", msg.getSubject());
             assertEquals("noreply@sample.org", msg.getSender().toString());
             assertEquals("otto@sample.org", adr.toString());
             assertNotNull(msg.getSize());
         } catch (Exception ex) {
             System.err.println(ex.getMessage);
         }
         return self();
    }
}

Listing 4: Implementing the AAA Principle for Behavioral Driven Development.

Now, we completed the cycle and we can see how the test steps got stuck together. JGiven supports a bigger vocabulary to fit more necessities. To explore the full possibilities, please consult the documentation.

Lessons Learned

In this short workshop, we passed all the important details to start with automated acceptance tests. Besides JGiven exist other frameworks, like Concordion or FitNesse fighting for usage. Our choice for JGiven was its helpful documentation, simple integration into Maven builds and JUnit tests, and the descriptive human-readable reports.

As negative point, which could people keep away from JGiven, could be the detail that you need to describe the tests in the Java programming language. That means the test engineer needs to be able to develop in Java, if they want to use JGiven. Besides this small detail, our experience with JGiven is absolutely positive.