Appian Automated Testing

Appian Automated Testing (AAT) by CEITA is a visual test automation tool, fully integrated with any Appian release, and customized to improve your testing experience. Manual testing is no longer affordable as an overall strategy , although there will be items to be tested manually, our statistics show that up to 95% of your Appian applications can be automated easily with AAT.

Why do our customers use AAT?

Return On Investment.

Common examples have a ROI of one year or less.

Optimize QA workforce.

Analysts and testers can be released in specific cases during the sprints of a project.

Shorten UAT.

The time for UAT will decrease 30%.

High speed interface automation.

The user experience has been improved thanks to a great interface design, therefore the usability has increased the automation experience.

Data generation and connectors.

AAT creates new file formats, new attributes, and thousands of combinations per file.

Delivery On Time.


See the changes from the beginning. You will be satisfied with this powerful tool from the second week.

Efficient.


Improve the efficiency of your Quality Assurance process.

Decrease your QA overall costs.

Empower your quality standards with an innovative testing style, and with a ROI in months.

Helps to adopt BDD.


An improvement of Agile methodologies which perfectly suited Appian, and makes your projects even more efficient.

Perform faster User Acceptance Testing.



By simply planning your full test overnight, and checking the results before your daily stand-up meeting.

Run it everywhere.




Run it from on Cloud, or we can install on your premises. AAT can be runned from docker containers, and be integrated into your DevOps strategy.

AAT allows the composition of automated testing in Cucumber syntax.

Using the powerful FitNesse console, besides, is focused on regression and on testing, while designing (Behavior Driven Development).


BDD



BDD (Behavior Driven Development), is a behavior-directed development strategy, which has evolved from TDD (Test Driven Development), although it is not a testing technique.

In BDD, tests must be defined before development. These are focused on the user and the behavior of the system.

The main advantage is that all BDD definitions are written in a common language. The main objective is for the team to describe the details of how the application to be developed should behave, and in this way it will be understandable by everyone.



Cucumber syntax



Cucumber is a tool used to implement methodologies such as BDD (Behavior Driven Development), which allow executing functional descriptions written in plain text as automated software tests.

These functional descriptions are written in a specific and readable language called "Gherkin", which serves as development documentation and for automated testing.



Gherkin language


The Gherkin language defines the structure, and a basic syntax for the description of the tests that can be understood both, by the technical members of the team, as well as, by the Analysts / PO or whoever is there as a representative of the client.

In this way, while tests are being generated, living documentation is being generated that perfectly describes how the system behaves, enriching and maintaining the documentation.



User’s Perspective



User works in his/her workspace which defines its tests.

Integrated repository to share tests between users.

Functional / Technical view of every test. Functional users can see your tests from their perspective and review with you the acceptance criteria so the test definitions are always up to date.

Cucumber-like syntax, which makes it structured and easy to read for anyone.

Your tests can be modular and be reused infinitely, with or without parameters.

Super simple navigation by Appian Menus, Sites, Actions, any kind of buttons, drop down lists, grids, etc.

Group your tests in UATs and execute UATs when you want.

Take screenshots of the navigation and upload the results to any testing repository existing in your organization.

Generate your test data. Define one test and run it with multiple data sets to easily validate borderline conditions and limits.