Natural Language Processing (NLP) Based Test Automation

At Next Generation Automation, Studying future technologies of Automation one area where team fascinated about. And recently we have developed Proof Of Concept for our Client "How NLP Based Automation can be technology for automation in future".

In this article, We will share with you high level understanding What NLP all about and how it can be useful for any automation project?

What is Natural Language Processing?

Natural language processing (NLP) is a sub field of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Building Block of NLP based Application:

Any NLP Application contains 3 Core Components:

1. Understanding

2. Processing

3. Generation

Before you actually start building your NLP algorithms you need to understand 3 Core Components: Understanding, Processing and Generation.

At every stage specific set of tasks performed

1. Understanding component helps in building vocabulary

2. Processing component creates meaningful statements

3. Generation component creates meaningful context

We will not go deep details for every component in the post but now look at how NLP can be applied from automation stand point.

First Understand before we apply NLP in Automation.

The Problem with Testing

Although there’s a trend towards facilitating software development with newer, more efficient tools, it’s surprising that test cases are still mostly created manually, which is time-consuming and challenging. This raises the following problems:

  • Those who write the cases are people, and that means there’s always room for human error. This is particularly true for inexperienced testers who may fail to meet functional requirements, as their tests are overly ambiguous or simply incorrect.

  • It’s often impossible to use the same tests in regressive testing if application requirements change, meaning that a lot of test cases have to be written from scratch or, at the very least, heavily amended.

  • As testers are time-limited in creating or amending existing tests, it’s impossible to conduct regressive testing with 100% efficiency. This results in possible bugs and system failures, risking a negative first impression regarding the released application.

Each of these drawbacks may affect the competitiveness of a product among end users, resulting in revenue loss.

This is where test automation kicks in to speed up testing routines and eliminate room for error (when tuned properly). With NLP, however, test automation can be made even more efficient.

Enhancing Test Automation with NLP Capabilities

Testers create test cases based on the requirements of a customer taken from user stories but how can a tool using NLP do that? For this process, a tester needs to input the following details so that the software can generate an accurate test case:

  • User story — Contains the requirements and descriptions of software features provided by the end user.

  • Acceptance criteria — A description of how the created software should work to meet the requirements provided.

  • Test scenario description — Shows the interaction between the user and the product, which will generate the test case.

  • Dictionary — This consists of keywords that the program uses to generate test cases.

All the information is analyzed with NLP techniques and processed into the frames, where it’s then converted into the unified modeling language(UML). UML is a modeling language in software engineering that turns the requirements into a set of diagrams and linkages between them. The resulting output is an automatically-generated test case.

Drawbacks and Advantages of NLP

As with any method, there are pros and cons to the use of NLP in test automation:

  • Firstly, you need to make sure you provide all the necessary information during creation of the test scenario descriptions. If not, your end results may be inaccurate at best, or may not work at all at worst.