The Cause Of The Application Is Often Out Of Its Own

The Cause Of The Application Is Often Out Of Its Own – Change is good. Changes are inevitable. Changes keep the application relevant and compatible, improve the user experience and provide more value to the end user.

Change can also be bad. Changes may cause program errors, system errors, performance problems, or output errors.

The Cause Of The Application Is Often Out Of Its Own

The Cause Of The Application Is Often Out Of Its Own

Fear not, modern development best practices excel at minimizing the risk of change and maximizing the ability to deploy changes with high frequency—you know, DevOps and whatnot. It’s just a matter of taking those practices and adapting them to the z/OS batch world.

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Learn the basics of z/OS batch applications – read my blog on Mainframe Batch 101 – Concepts and why it matters.

As mentioned in my previous blog on Mainframe Batch 101, there are many ways to streamline, automate and reduce the risks associated with batch application changes. One of them is to automate the testing of the batch application at different levels. Let’s explore

I conducted a survey of z/OS batch applications in SurveyMonkey using social media and various mainframe watering holes such as Open Mainframe Project Slack, CA Brightside Slack, and the Mainframe subreddit.

The blog post and poll are part of a technical session I’m giving at the SHARE event in Fort Worth.

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By calculating the average response, we can conclude that a single change will require about 25 man-hours of testing before the change can go into production.

During the QA testing phase, problems often arise that cause the change request to be pushed to the development phase. It is also interesting that a significant number of users do not have their own QA team. It can be good or bad. If the development team is agile and embraces quality as part of their delivery, then that’s good. On the other hand, if there is no dedicated QA team because quality is not a priority, it can be bad.

Mass production problems caused by change requests are quite common. This shows that the techniques used in the quality assurance phase are not sufficient.

The Cause Of The Application Is Often Out Of Its Own

Application program errors were expected to be the most likely cause after deployment of change requests. However, it is interesting to see that JCL errors are number 2 – this could easily have been avoided if some production sanity checks were done as part of the deployment.

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Arguably, batch application testing is already somewhat automated. In many cases, test task sequences are already batch scheduled and run automatically. While it can automatically submit the jobs and produce output, it still requires the tester to manually find the output, correlate it with specific test scenarios, and ensure that the resulting data matches the expected results. These manual tasks are the best candidates for further automation.

In addition to saving time and reducing human error by automating these tests, other well-known benefits of automated tests include cost savings, early detection of errors, thoroughness and even information security.

My previous blog on Mainframe Batch 101 illustrates the lifecycle of an application change and the various teams involved in the overall process. These lifecycle stages can be easily mapped to established test levels and automated.

My Zos Batch Test repository on Github provides a framework for a hierarchical set of automated tests for z/OS batch applications.

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I will be using Jest as the testing framework in all my examples on this blog. For instructions on setting up Jest for your testing, see Dan Kelosky’s Building Mainframe Metal C and Testing with Jest and the Zowe CLI.

In the following sections, I will present sample tests of these different test levels. Note that these are examples that raise trivial claims – the tests can certainly deal with more complex claims. The goal is to give you an idea of ​​how to organize and write your tests. Feel free to clone the git project and use it as a starting point.

Unit testing is designed to test the smallest testable unit of code. For testing batch applications, we consider a callable program as the smallest unit.

The Cause Of The Application Is Often Out Of Its Own

Unit tests are great for the development phase because developers can run the specific unit tests that are relevant to the changes they are making. This should keep the test duration short and still give the developer feedback on their intended changes.

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Ideally, you should be able to run unit tests on your developer workstation without requiring platform-specific infrastructure. Unfortunately, this is not possible with current technology, as the two unit test options listed below require tests to run on z/OS. The only way to run these tests on a workstation is for a developer to use IBM’s zD&T to emulate z/OS on their workstation and target it for unit testing. Although it sounds tempting, z/OS emulation in zD&T is resource intensive and most developer workstations simply don’t have enough juice to run it with adequate performance. Hopefully this will change in the future and developers will be able to have the freedom and independence to run unit tests entirely on their own workstations without the need for z/OS on real Z Metal.

This approach creates unit tests for programs written in COBOL or PL/I. Tools such as IBM’s Z Open Unit Test (also called zUnit) allow you to create test cases for a specific COBOL program by extracting test data from a test run of the application as a model. The user can then manually change the expected result if the extracted test data does not match the desired unit test logic. Z Open Unit Test currently requires IBM’s Eclipse IDE called IDz to generate the tests. Once produced, these tests can be run via terminals in open source IDEs such as Visual Studio Code using the Zowe CLI:

This command can be embedded in task runners such as npm, Gradle or Gulp scripts for easier access to the IDE, or called remotely from a CI (Continuous Integration) tool such as Jenkins.

This method is useful for securing all callable programs that are part of your batch application. You can even generate code coverage statistics that tell you what percentage of your application’s code base is tested by the generated test cases. This can be added as a requirement to your quality gates before the code can be promoted to other stages of the application lifecycle. However, this type of language-specific unit testing brings with it some limitations for COBOL and PL/I.

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An alternative to language-specific unit tests is to create language-independent tests that focus on all possible I/O at the step-level entry points of a callable program. The nature of automated testing mimics the way developers manually test their code changes today. Since we don’t test each call option separately, preferring to focus only on step-level calls, this method is not as detailed as option 1, but can provide a practical and realistic way to build more unit test automation.

The advantage of such a method is that it removes limitations imposed by language-specific unit tests and provides more flexibility in the test logic. It also allows the tester to concentrate on the external parts of the program (inputs/outputs) rather than delving into the internals of program-to-program logic flow.

The disadvantage of this method is that it does not automatically generate code coverage statistics or generate test cases from the model test data. You can still artificially generate code coverage metrics by taking inventory of the number of programs you can call and tracking the programs you’ve developed with a comprehensive set of unit tests. Running this method may take longer than a true unit test, described in option 1.

The Cause Of The Application Is Often Out Of Its Own

Typically, developers make a code change and spend 15 minutes manually testing it. Now imagine doing this 10 times a day for 10 days after each small code change. That’s about 30 hours a fortnight spent on manual unit testing. Regardless of whether you choose option 1 or option 2, automating such operations frees the developer to test and return to coding. After the automated test is complete, they can use the results as a health check or to verify that the desired change is working. Of course, the creation of these tests involves an initial overhead, but in software development it is an acceptable compromise to invest once and reap the rewards forever. Nothing revolutionary here, just an adaptation of modern software development principles to the world of z/OS batch applications.

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The next level of testing involves testing each batch job that is part of the batch application. These are the batch tasks that are individually scheduled in the batch scheduler to create the logical batch application. Each batch job can have multiple job steps that call multiple programs.

A sample unit test for a multi-step job that is part of a series of jobs in a batch application

It is not necessary to perform component-level testing every time a developer makes a code change, but it makes sense to do so as part of the transition from development to QA. The automated component tests reduce the quality assurance phase considerably and can spend more time

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