NLP advancements are driving deep QA automation and software quality

Star Wars fans would be familiar with the golden life-sized hospitality robot – C-3PO. While Star Wars might be set in a galaxy far away, having machines communicate with us in a human-like manner is already a reality, which keeps getting more realistic with each passing day. The chatbots on websites, smart assistants, or the calls we make over the internet, all of them have one thing in common – ‘none of them are human’. So, how do they seem and sound so humanlike? How do they respond to us intelligently and how are they so articulate? Well, this is the magic of Natural Language Processing or NLP. 

In layman’s terms, NLP is the ability of a computer program to understand human language as it is spoken and written. It is a component of Artificial Intelligence (AI) and has existed for more than 50 years with its roots in linguistics. NLP technologies have significantly revolutionized the business landscape and are continuously driving innovations in every industry. Their incredible ability to understand the nuances of human languages, such as sentiment and intent, and generate a humanlike response is driving businesses to deliver better customer experiences and generate immense value. Numerous enterprises across the world are implementing NLP as they have demonstrated their capabilities of opening new avenues for business growth and continuity. 

It wasn’t until the advent of advanced, powerful, cheaper hardware (leading to cloud computing), and newer processes (including statistics into expert systems and moving to Machine Learning models) that NLP got its second wind, and this time, there was no stopping it. In the year 2011, Siri was launched on Apple iPhone 4S and became one of the world’s first successful NLP and AI assistants to be used by consumers.  The subsequent launches of Alexa by Amazon, Cortana by Microsoft, and Google Assistant ushered NLP to the world stage.

A quick NLP primer

In today’s digital era, the use of NLP in everyday life is often underestimated. Sentiment Analysis is one of the major use cases of NLP, as it determines the right emotions and acts as ‘opinion mining’ –

• Email Filters: Email users often face a lot of spam. Here is when NLP engines can differentiate between various categories of emails and perform complex sorting to avoid malware attacks and phishing links 
• Spam Detection: It helps in eliminating spam pages that match the user’s search query-related keywords but do not offer a satisfying answer to the search results 
• Auto-correct: It identifies the matching keyword in case the user misspelled it or typed a less commonly used term

NLP applied to the realm of software testing and quality

NLP has unlocked new horizons across diverse spaces including smart assistants, chatbots, email filters, and language translation. It is now seeing a large uptick even within the areas of software development and is being implemented across the whole lifecycle including software testing and quality engineering. Recognizing real-world applications is an important part of the larger NLP journey. Modeling this complexity is an exciting task and non-trivial one, moreover, the real-world use cases are creating challenges for machine learning models as well. Additionally, NLP can also be applied in the development lifecycle in a variety of ways, including quality engineering and continuous code quality improvement.

In today’s world of software engineering, testing is entwined with DevOps. Besides, NLP and ML provide a plethora of opportunities to improve software quality and delivery in a seamless CI/CD pipeline by overcoming the challenges for effective DevOps. They can be categorized into four areas:

• • Differential tools: These use NLP and ML to automatically identify code quality issues, regressions, security vulnerabilities, etc. 
  • Visual AI testing tools: Visual testing brings the user experience layer of testing and uses ML to scale these validations across the increasing set of digital platforms (mobile, web, and connected devices) 
  • Declarative tools: While this aims to improve test automation stability and productivity, these tools also focus on the automation of repetitive processes and tasks 
  • Self-healing tools: This is one of the most exciting areas of application. Test automation is successful at scale if they are stable and can keep up with the pace of development. Code-based test automation is by nature less stable since they depend on application objects, XPATHS, platforms, and environments that change frequently. Thus, the use of NLP, AI, and ML to address test maintenance is where self-healing tools have emerged.
• Reporting and analysis tools: NLP and ML play a crucial role in reporting while sorting and slicing through the data and further help in auto-classification of defects and root causes into categories. 

Future of NLP in Software QA and Automation

Automation plays a critical role in the testing cycle. An automation tool can utilize NLP to identify bugs and simplify the tester’s job by creating test cases based on the requirements of a customer taken from relevant inputs such as user stories, acceptance criteria, test scenario descriptions, etc. Data analyzed with NLP techniques bring forth critical information for automated testing, which is translated into Unified Modeling Language (UML) to check all links and parameters, along with the interface between each piece of code and external services.

As the world revolves around AI and automation, the next-generation technologies for machine-to-machine interaction are about to take off and NLP is at the heart of it all. With constant advancements taking place in the area of NLP, its future in the automation journey and software QA is bright, and it will continue to affect more aspects of our daily lives.