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.

Let’s take a closer look at this process.

Enhancing Test Automation with NLP Capabilities

To put it simply, test automation is a way to create test cases without manual input, minimizing human intervention. While test automation commonly applies to graphical user interface testing and API-driven testing (among other types), it’s framework-based automation where NLP is best suited.

Keyword-driven and behavior-driven frameworks are the two most advanced methods here, bringing in vast opportunities for NLP applications.

Keyword-driven framework

This approach consists of two stages of test case creation: design and development, and execution. Here’s how it works.

A tester manually initiates a test in a special tool, which analyzes the sentences and divides them into segments. These segments can be organized into blocks that specify actions, targets, and required data.

When a test case gets divided into segments, it can be executed by a computer. As a tester uses limited and precise language, this is far easier to analyze than everyday speech. Yet, there are still many difficulties.

The program has to define verbs, nouns, and conjunctions in the sentences, and also understand keywords. To do this, test automation tool creators use POS tagging, a basic operation of NLP to define parts of speech.

The Pros

No need to rewrite testing scrips over and over again—existing ones can be reused.

It’s quite accurate at detecting issues (according to our observations).

Your test engineers are not required to have special programming skills to undertake the tasks, only specific training.

The Cons

Success depends on the complexity of the system tested.

Keyword limitations play a role in the accuracy and success of the test.

Although your team may not possess coding skills, knowledge of meta-languages is a must.

Finally, if the system fails, your team will still have to solve problems manually, requiring extra skills and time.

Photo by Peter Ivey-Hansen on Unsplash

Behavior-driven development (BDD)

BDD is a commonly-used approach for software projects. It’s characterized by the close cooperation of business operations and software development teams to create an effective, customized product. One way to do so is by actively involving customers in the process of product creation and development.

In this, a company involves user stories written in natural language, which show in which direction the product should be developed. From these stories, test engineers create tailored test cases to find out if the product is suitable for use.