Enterprise Testing in Java
- A Typical Enterprise Scenario
- A Concrete Example
- Test Implementation
- Exploring the Competing Consumers Pattern
- The Role of Refactoring
Before we delve into the issues surrounding enterprise testing in Java, it's important to define exactly what we mean by enterprise.
It's hard to conceive of a word with as many meanings and connotations (and misconceptions!) as enterprise in Java. For many, this word is tied to the usage of the Java Enterprise Edition (J2EE, or its current incarnation, Java EE), whose APIs enable us to bless our applications with the enterprise stamp. For others, enterprise applications have specific features regardless of what APIs or even specific languages are used.
An example of using the enterprise API is an intranet application that manages a fixed set of entities, with its own backing store. It is likely that this application has a Web-based UI and that it uses some combination of servlets, JSP pages, and a persistence mechanism. In this example, the use of the ubiquitous term refers only to the API usage, and it is a relatively simple matter to ensure that this application can be tested easily, if one uses the right tools for the job.
Another example is an integration project in which a new middle tier is being added between two existing legacy systems, with the hope of slowly phasing out the old back end. This new layer has to be able to encapsulate the mapping between the two legacy systems, but more often than not, it is not allowed to modify either of the legacy systems. The mapping will likely be complex and require orchestration between a number of other external systems. In this case, we are much less likely to achieve our ideal of easy, quick-to-run unit tests and are far more likely to benefit from integration and functional tests.
That is not to say that enterprise projects cannot benefit from unit tests. It is also almost always possible to break down components into small enough pieces that meaningful unit tests can be derived, and all three types of tests go together hand in hand.
This chapter and the following one discuss testing issues with both definitions of enterprise. We need to be aware of a number of key concepts and issues when testing enterprise applications. These issues are not concerned with APIs but rather with the very nature of enterprise systems: complex integration issues, legacy system support, black-box testing, and so on. Generally, the assumption is that we have either a body of existing code that we need to integrate with or a system that is already in use but needs tests. Once we've established this foundation, the following chapter will discuss how to test specific J2EE or Java EE components.
Before we start, here's a brief recap of the different types of tests.
- Unit tests: A unit test tests an individual unit in the system in isolation. Unit tests run very quickly since they have little to no start-up costs, and almost no external dependencies.
- Functional tests: A functional test focuses on one piece of functionality. This usually involves interactions between different components.
- Integration tests: An integration test is an end-to-end test that exercises the entire stack, including any external dependencies or systems.
A Typical Enterprise Scenario
To illustrate the concepts around enterprise integration and functional testing, it's helpful to examine a real-world example. Let's say that we're consulting for a financial institution that has a legacy back-end database that houses most of its financial data. This database is one of the major bottlenecks of the system. The database is the central point for all financial trade information and is directly read by a number of front- and back-office applications.
In addition to that, some of the newer applications talk to a recently implemented abstraction layer. The abstraction layer grew organically based on the needs of specific applications and was not designed up front to be a middle tier. It has many idiosyncrasies and is so convoluted and complicated right now that it is no longer possible for new applications to easily use it.
The company decides that it is time to revamp the system. The goal is to introduce a middle tier designed from the outset to service most if not all applications that need data from the database. The database is split into a number of smaller instances and the data partitioned according to business requirements.
After the new system is implemented, it quickly proves itself profitable. Due to the phased approach of development, some applications still talk to the old legacy database, but a number have been ported over to the new system. The new system acts as a mediator between the various components and includes transformation components to ensure the correct data is still fed to legacy systems that expect the old formats and schemas.
Confused yet? You shouldn't be. Chances are that most developers have been in this situation during one project or another. Is this project bizarre or extreme in its complexity? Perhaps in the details it is, but the overall issues confronting it are fairly standard and commonplace. Let us step back a bit and see if we can identify the main participants:
- The legacy database: the source of all evil
- The shiny new API: the source of all good
- Dozens of legacy systems: the nature of the business, neither good nor bad
- Transformers: a necessary evil to allow components to talk to one another
This probably is starting to sound more familiar. Most if not all enterprise applications have to deal with legacy data at some point. This could be a migration issue, it could be a transformation issue, or it could be simply the introduction of a new layer on top of existing systems.
So what testing methodology does this successful new project employ? Judging by its success, it must consist of rigorous unit tests, countless integration and functional tests, nightly builds, email notifications of test failures—all the good developer testing habits that every successful project has.
As a matter of fact, it has none of these. The testing methodology of this project consists mainly of developers writing the odd class with a main(String args) method, running that against their data, and eyeballing the results. If it looks good, the functionality is deemed complete, the code checked in, and that's the end of that. Before a production release, there is a one- or two-week period where a QA team goes through the application and tries to find bugs. This is a manual process, but by the time it's done, the production release is in pretty good shape. The code is deployed, and everyone is happy.
The developers involved in this project range from experienced team leads to average developers. Almost all of the developers know about unit testing and have written a unit test in the past. The project did not mandate formalized test code, so there was no requirement to develop a test harness or automated tests.
Furthermore, all the developers agreed that it does not make sense to unit test the code. It is an integration project and therefore impossible to capture the important business aspects that need to be tested in a single unit test. The tests written would violate any number of popular testing recommendations; they would take a long time to run (many seconds), have complicated setup requirements (a few more seconds), and require a specific environment in that they would be highly dependent on a specific database schema, with specific data and stored procedures.
We suspect that this conclusion is far more common than many testing advocates would like us to believe. It is tempting to dismiss developers who are not obsessive about writing tests as ignorant or incompetent. Both assumptions are rather incorrect. JUnit, for example, currently makes it difficult to think in terms of integration or functional testing; there is a stigma of sorts attached to tests that have complicated environment requirements (and as a byproduct, slow running tests). Developers shy away from them. Yet for enterprise projects, such tests are far more valuable than unit tests. An integration project, unsurprisingly one would think, is exactly what integration tests excel at.
Issues with the Current Approach
So where's the problem? The project works and is a success, and everyone is happy. As the popular saying goes, if it ain't broke, why fix it? However, it turns out that the current approach has a number of inefficiencies.
QA Cycle Is Too Long
Currently, every release requires one or two weeks of full-time testing. Bugs discovered during this testing phase are added to a list of issues that should always be tested. The testing cycle often runs late if many issues are found, as many things need to be retested once the first batch of issues has been resolved.
Poor Test Capture
Developers currently write plenty of tests that are discarded as soon as the functionality being tested starts working. The main method is simply rewritten, or code is commented out and commented back in to reconfirm a test. There is no growing body of tests, nor is there a way to automate these informal tests.
Regression Testing Effort Grows Linearly
With every QA cycle, issues found are added to a growing master list of issues that need to be tested for every release. It becomes the QA team's job to perform all regression testing. This isn't such a problem with just a handful of releases, but the new system in place is expected to have a lifetime of at least five years, with many more enhancements and changes to come in future releases. Within a year or two, the mountain of regression tests is very likely to have a significant negative impact on the manual test cycle.
Lack of Unit Tests
The developers often argue that the system is too complex to be tested usefully through unit tests. This could well be true, in the general case. However, it is highly likely that a number of components or pieces of functionality do lend themselves well to unit testing. In a large, complex system, it can be a daunting task to identify these components, so the tendency is to stick to integration and functional tests.
Once we do have integration tests, unit tests more often than not will naturally emerge. Because the testing infrastructure is already in place, debugging an integration test is quite likely to result in a unit test, simply to try to narrow the scope of the bug.