What Is NLP (Natural Language Processing)?

How NLP Testing Can Transform Automation Strategy Beyond Speed

Asad Khan is the founder & CEO of LambdaTest, an AI-powered unified enterprise test execution cloud platform.

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Test automation has always been about speed. We measured success by how many tests we ran per minute and celebrated shorter regression cycles. However, we now stand at the edge of the next evolution. With AI on the horizon and intelligent test automation through natural language processing (NLP), business stakeholders are taking notice.

The industry is recognizing this shift, as 78% of testers already use AI for productivity. Market projections confirm this: The NLP market is expected to grow from $18.9 billion in 2023 to $68.1 billion by 2028.

Understanding NLP In Test Automation

NLP in test automation is the convergence of linguistic computing and quality engineering. It helps with writing automation tests using natural language prompts.

Here, NLP acts like an intelligence layer that connects business requirements and technical implementation, enabling all stakeholders to execute test automation without being weighed down by the learning curve or technical aspects of a certain programming language or framework.

Unlike standard parsers that match keywords, AI-native testing tools help implement sophisticated computational linguistics, including:

• Intent recognition that distinguishes between verification objectives and test procedures.

• Semantic parsing that focuses on the intent behind test specifications instead of just the literal instructions.

• Domain-specific language that can understand QA terminology and patterns.

• Contextual understanding that maintains test coherence even if the interface changes.

Benefits Of NLP Test Automation

NLP test automation offers benefits that go beyond traditional testing approaches—making tests more resilient, accessible and aligned with modern development practices.

Resilience To UI Changes

Traditional tests break when developers rename a button (for example, renaming from "Submit" to "Send"). NLP-powered tests continue working because they understand purpose as opposed to just selectors, helping reduce maintenance costs for the overall team.

Democratized Test Creation

When business stakeholders can write "Verify premium customers see the discount offer" and have it automatically transform into executable tests, you can eliminate the translation bottleneck that slows development cycles.

Reduced Training Investment

New QA team members can understand and contribute to natural language test suites without having to spend months understanding complex frameworks or programming languages, helping to reduce onboarding by weeks. When requirements and tests share the same language, you can also help eliminate miscommunications.

Future-Proofed Test Assets

As your application evolves through redesigns and refactoring, NLP tests remain relevant. This is because they capture intent, not implementation. Your investment in test creation becomes a long-term asset.

Enhanced Developer Productivity

NLP testing reduces the barrier between thinking about a test and creating it. Developers can quickly write test scenarios in natural language during development rather than switching context to write complex test code.

Aids Shift-Left Testing

Building upon developer productivity, when NLP test cases can be created early during the development cycle, it helps aid your efforts towards shift-left testing.

Implementation Roadmap For Technology Leaders

For technology leaders evaluating this transition, a phased implementation yields the best results:

• Target high-maintenance tests first. Identify test cases that frequently fail due to UI changes but validate stable business logic. Prioritize converting these to NLP-driven tests to reduce maintenance effort and demonstrate early ROI.

• Facilitate cross-functional knowledge transfer. Host joint workshops with QA, developers and business teams. Use these sessions to align on testing goals, build shared understanding and develop internal advocates for the new approach.

• Convert critical existing tests. Migrate current test scripts that validate key features (e.G., premium customer functionality) to NLP-driven formats. Run them in parallel with legacy tests initially to ensure reliability and build team confidence.

• Empower business teams to author tests. Once the technical team is comfortable, introduce tools that allow business users to write and maintain tests using natural language.

• Integrate AI incrementally. Don't replace everything at once. Start by adding AI at specific points within current workflows to help teams gradually adjust while preserving familiar processes.

• Establish clear success metrics. Track key indicators such as reduced test maintenance time, improved resilience to UI changes and increased participation in test creation by non-technical staff.

NLP's Impact On Testing Roles And Responsibilities

NLP is transforming how testing responsibilities are distributed across teams, making quality assurance a more collaborative and efficient process. Here's how this looks today:

For Business Analysts

NLP enables business analysts to write test cases in plain language—such as "Verify the high value customers on the page and check if the premium offer activates for them"—directly into testing tools. This removes the need to translate requirements into code, closing communication gaps and ensuring that test cases align closely with business objectives.

For Testers

The focus shifts from maintaining fragile scripts tied to UI elements to ensuring robust, conceptual test coverage. Instead of fixing broken selectors every time the interface changes, testers can now concentrate on strategy, coverage and risk—becoming stewards of overall quality rather than code mechanics.

For Developers

NLP reduces the overhead of test maintenance during rapid development cycles. They can build features knowing that the automated tests verify intent, not just implementation details, minimizing disruptions from UI changes and allowing them to stay focused on delivering functionality.

For Managers

NLP brings transparency and clarity to the testing process. Because test cases are written in business-readable language, managers can better understand how quality efforts align with requirements, enabling more informed decision making and stronger governance over product quality.

The Future Of Intelligent Quality Assurance

We're witnessing the third wave of QA evolution: going from manual execution to script automation and now to NLP-based AI testing. When approached strategically, this evolution can help deliver three advantages for the modern DevOps pipeline:

1. Zero-Code Test Orchestration: Product owners and stakeholders author acceptance criteria that transform directly into executable test cases without technical debt accumulation.

2. Unified Quality Governance: The artificial boundary between technical and business teams dissolves as testing language becomes universal across the organization.

3. Resilient Test Intelligence: Test assets maintain validity through UI modernization cycles, preserving institutional knowledge across product iterations.

With 85% of companies already integrating AI tools in their tech stack, it's important to understand NLP for testing and determine a roadmap that works best for your organization.

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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. 

Disclaimer

Views expressed above are the author's own.

END OF ARTICLE

Intelligent Automation: From Streamlined Business Processes To Content Creation

Financial institutions are using intelligent automation to optimize productivity, reduce costs, transform insights, and elevate customer engagement

In pursuit of greater operational efficiencies, financial institutions are using intelligent automation for loan origination, compliance and regulatory reporting, contract management, marketing, fraud detection, risk management, and a slew of other functions. Intelligent automation combines traditional automation technologies with generative AI (GenAI), machine learning (ML), and natural language processing (NLP).

What makes this technology transformative is its ability to go beyond decisioning based on predetermined rules and to learn from the data it processes to make judgements without human intervention.

Intelligent automation frees staff from routine and repetitive tasks such as data entry, financial reporting, and loan processing to focus on more value-added activities. It also reduces the need for financial institutions to hire additional staff.

Malcolm DeMayo, Global Vice President - Financial Services Industry, NVIDIA

Malcolm DeMayo, Global Vice President - Financial Services Industry at NVIDIA, share his insights, "One of the biggest and most pressing challenges with generative AI is cost vs return on investment. Banks must transform from datacenters where data is stored and retrieved to AI Factories where the input is data and the output is real-time intelligence. Banks are seeing significant value creation with AI, and picking the right use case is critical to successful returns."

There are many practical use cases for these automated workflows, including translation, content summarization, data augmentation, and data analysis. Here are just three use cases that are promising for financial institutions:

Code Generation and Software Development

Call Center Audio Transcription, Sentiment Analysis and Summarization

Document processing 

Code Generation and Software Development

In a quickly evolving landscape, financial institutions are under increased pressure to bring systems and products to market quickly. However, the software development lifecycle can be lengthy and resource intensive.

Intelligent automation can speed up software development and make planning and design, code creation, testing, implementation, and maintenance less labor intensive. It can also facilitate code refactoring which improves reliability and performance while preserving functionality.

To address the challenges of traditional software development, financial institutions are increasingly turning to intelligent automation, with 44% currently using or planning to use it for code assist and software generation.[1]

Call Center Audio Transcription, Sentiment Analysis and Summarization

Financial institutions are inundated with vast amounts of data from customer support, from call center audio recordings to social media posts to interactions with chatbots via online and mobile banking.

Analyzing customer interactions is incredibly time consuming and labor intensive. Intelligent automation takes structured, unstructured, and semi-structured content and consumes, parses, and transforms this information into insights. Intelligent automation can even analyze customer sentiment to help financial institutions personalize customer interactions.

Data from customer interactions can help create marketing content such as blog posts, customized training and educational materials, reports, social media updates, infographics, and video scripts.

To make content relevant and aligned with business needs, generative AI models are augmented with internal data and use retrieval augmented generation (RAG) to access and use proprietary data to inform content creation. This means AI doesn't just create content on its own, it references your specific data to ensure the output is accurate and reliable. You can quickly and easily update your knowledge base to reflect new data or remove outdated information, ensuring that the generated content remains accurate and up to date.

Document Processing 

Financial institutions must process a tremendous volume of documents containing a vast array of data, such as transaction records, regulatory filings, loan records, and more. Processing these documents manually is time consuming and while standard automated document processing makes these tasks more manageable, they are still inefficient.

These document processing challenges are why 53% of financial institutions are using or planning to use generative AI for report generation, synthesis, and investment research, up from 27% the prior year.[2] According to NVIDIA's 2025 State of AI in Financial Services report, more than half of financial institutions are using or plan to use generative AI for document processing and analysis and 43% are using or plan to use it for enterprise search such as searching through internal documents.[3]

Intelligent automation can extract relevant information from documents and summarize findings. These insights can be used by the C-suite for strategic decision-making and customer service agents to better service customers. For example, intelligent automation can speed mortgage origination and closing by extracting relevant information from title searches, W-2 forms, bank statements, and purchase and sale agreements.

Getting the Most Bang for the Buck

Intelligent automation enables financial institutions to immediately extract actionable knowledge from vast stores of internal and external documents. It also powers faster software development lifecycles, streamlines processes across the organization, and creates new content using proprietary data. To get the most out of an investment in intelligent automation, first identify use cases that would add the most value to the organization, Next, implement pilot projects that promise high ROI and expand use across other areas of the organization.

"Taking the 'wait and see' approach to adopting AI can prove very problematic," shared DeMayo. "The pace of innovation with generative AI is so rapid, if you don't embrace this technology, and make investments today, your business could be disrupted."

The Dell AI Factory with NVIDIA can accelerate time-to-value of intelligent automation with generative AI, offering microservices that can be immediately deployed for data-curation, retrieval augmentation, and customizing generative AI models to business needs.

About the Dell AI Factory with NVIDIA

Dell Technologies and NVIDIA can help you leverage AI to drive innovation and achieve your business goals. The Dell AI Factory with NVIDIA is the industry's first and only end-to-end enterprise AI solution*, designed to speed AI adoption by delivering integrated Dell and NVIDIA capabilities to accelerate your AI-powered use cases, integrate your data and workflows, and enable you to design your own AI journey for repeatable, scalable outcomes.

* Based on Dell analysis, July 2024. Dell offers solutions with NVIDIA hardware and software engineered to support AI workloads from PCs with AI-powered features and workstations to Servers for High-performance Computing, Data Storage, Cloud Native Software-Defined Infrastructure, Networking Switches, Data Protection, HCI and Services.

[1] https://resources.Nvidia.Com/en-us-2025-fsi-survey/ai-financial-services[2] https://resources.Nvidia.Com/en-us-2025-fsi-survey/ai-financial-services[3] https://resources.Nvidia.Com/en-us-2025-fsi-survey/ai-financial-services

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