Category Archive

Product Development

Scale yourself: Use Agile


What is Agile & why is it Important?

This way of working has become the most talked about topic in the past few years and yet unknown to many! One would be living in a stone age of Technology if this term is still unheard. Agile is everywhere: You name it and there you have it.

Since Agile is not a methodology but a mindset, there is still room for it to grow not only in the software development spectrum but across other businesses as well such as manufacturing, food & beverages, automobile industry and more. Agile mindset is a great asset to help businesses grow and come up with new products & services. Although Agile does not fall under the traditional business management techniques which are generally taught in top-notch business schools around the world, the good part is that the Agile mindset will not only survive in the coming years, but it will also show us the way to develop our mindset in business.

It`s not a surprise to hear why the new age organizations are trying hard to implement this. So, it’s important to understand what Agile means and why it is important.

Agile History: From where it all started

Going back to the history, the term Agile is derived from the Latin term “agilis” which means “nimble or quick,” and from the term “agree” which means “to set or keep in movement”.

Why is it becoming popular?

Agile has completely restructured the way the software industry works. This way of working has definitely boosted the success rate of projects by creating a great and new age work culture. With respect to the other ways of working, Agile process is quick enough to adjust to changes requested by the clients throughout the development lifecycle.

What is Agile Project Management?

– As per the Agile Manifesto (2001) approach, Agile project management is an iterative approach to product delivery & it incrementally starts from the very start of the project instead of trying to deliver the entire product at once near the end.
– Agile works by breaking projects down into little bits of user functionality, prioritizing them, and then continuously delivering them in 2-4-week cycles called iterations or sprints. The customer’s priorities are well analyzed, prioritized and the team estimates how much time work will take in an iteration, as well as how to do the work.
– Performance is measured by customers at the end of the iteration. The lessons learned in each iteration are captured in retrospectives and used in future iterations. In this way, the products are constantly improved and the process for developing them also improved.

Need for Agile Principles and Agile Methodology

Do we know why some organizations are tending toward Agile Project Management?

Agile Project Management helps:

– To create a High-Quality product
– In providing Higher Customer Satisfaction
– Faster ROI
– To increase Product Control
– To help increase team performance, improve customer satisfaction and increase project versatility
– To be able to respond to market dynamics well

– Companies to embrace the idea of delivering business value early in the process making it easier to lower risks associated with development.
– During the project, end-user involvement is encouraged, providing visibility and transparency. There is continuous planning and feedback throughout the process, delivering value to the business from the beginning of the project.

Need for Agile Trainings:

– Agile trainings can clear up many misconceptions / misunderstandings about the operations of Agile.
– It can also help expose the underlying Agile concepts and clarify the differences between the various implementation method.
– Having all project team members (both technical and business) attend common training, ideally in the same class, can eliminate some of these problems. The shared understanding among the team members strongly increases the probability of the team inspecting and adapting together using a common language and practices, thus reducing conflicts in the future.

Conclusion: “Future in the Market”

Agile ways of working have become mainstream today and most of the organizations which leverage Agile ways of working and the culture of Agility are going to dominate their industries in coming future. Those organizations that do not take advantage of Agile are going to struggle to retain both customers and talented employees. At some point, their lack of business agility is going to threaten their very survival. Think about it, scale yourself because it’s now or never!

And it’s rightly said:

“Agility is principally about mindset, not practices” – Jim Highsmith

How Infrastructure as Code Powers Your DevOps and Cloud Journey


Over the last decade or so, infrastructure management has changed fundamentally.

In the past, it involved manual management and configuration of systems and full-time administration to ensure smooth workflows and stable functions.

Today, the rise of DevOps culture and modernization of cloud infrastructure have revolutionized – and improved – the way organizations design, develop, and maintain their IT infrastructure.

One of the critical components at the centre of these trends is infrastructure as code (IaC).

In this post, we discuss this topic at length, its role in accelerating DevOps and cloud transformation, and how you can implement it in your organization.

The Fundamentals of Infrastructure as Code

As the name suggests, infrastructure as code simply implies the codification of the underlying infrastructure as software. Rather than manually configuring discrete hardware devices, the operations infrastructure is managed using the same rules and strictures that govern code development.

With IaC, infrastructure resources & configuration parameters are treated as programmable objects, and controlled them via code

Which also means that all time-tested software development practices should be applied to infrastructure. With infrastructure’s configuration written as a code file, it can go through the same version control to track code changes, automated testing to check for errors and oversights, and various other steps of a continuous integration and continuous delivery (CI/CD) pipeline that are applied for application code.

IaC is More than Just Automation

IaC is different from simple infrastructure automation, which involves replicating a series of static steps several times and reproducing them on multiple servers. IaC is a concept extends beyond that.

IaC Approaches & Methods

IaC tools can vary in terms of the specifics of how they function, but we can generally divide them into two key categories, related to programming language paradigms:

Declarative approach: This approach focuses on the what by declaring the desired outcome – instead of explicitly outlining the sequence of steps the infrastructure needs to reach the final result.

SQL is a popular declarative programming language. AWS CloudFormation templates, among others, are based on the declarative style.

Imperative approach: The imperative method focuses on the how by defining a sequence of commands/instructions so the infrastructure can reach the desired outcome.

Object-oriented languages, such as C++ and Java, are commonly used for imperative programming. A tool like Chef can be used in the declarative manner, but also imperatively as necessary.

In both these approaches, infrastructure as code is configured on a template. Templates use a simple, human-readable format and are simple text files, where the user specifies the resources needed for each server in the infrastructure.

Role of IaC in DevOps & Cloud Computing

There are several benefits to conducting operations based on IaC. Here’s why IaC is essential to your DevOps and cloud practices.

1. Stronger interdepartmental relationships: With IaC, the knowledge of server provisioning, configuration management, and deployment is no longer limited to the sys admins.

IaC is typically written in a high-level language. JSON, for example, is a lightweight and text-based language that allows IT operations admins to write infrastructure code alongside the development team. This helps to strengthen relationships between different teams that DevOps demands.

2. End-to-end automation: IaC helps eliminate manual processes not just in production environments, but also backward across a CI/CD pipeline – right from development and QA testing to deployment and management.

Teams can carefully control code versions, test iterations, and limit deployment until the software is approved for production. This helps in easier error-tracking, meaning solutions can also be offered with more rapid turnaround.

3. Easier management of cloud infrastructure: The scale and scope of cloud computing demands a high level of automation and governance to manage the cloud’s wide range of services, applications, and functions.

IaC plays an important role in automating resource and application configuration and deployment on cloud environments – whether public or private clouds. Organizations that opt for hybrid cloud benefit even more, as templated configurations and resources lend themselves to be applied across multiple cloud environments and ensure robust governance.

Sys admins can use a predefined set of instructions to:

o Provision resources
o Configure the instance
o Configure and deploy a workload
o Link all associated services
o Ensure continuous monitoring and management of the deployment over time

CloudFormation is AWS’ central mechanism for automating cloud resources. It enables teams to specify templates representing software stacks and automatically deploy them to cloud environments. Similarly, Azure uses Azure Resource Manager (ARM) to manage and deploy cloud resources.

4. Access to immutable infrastructure: Immutable infrastructure refers to the practice of assembling and setting components and resources to create a full service or application. If any change is needed for an individual component, they are not reconfigured – instead they are all updated and redeployed in an instance.

It is especially useful for cloud and microservices environments, which involve several interdependent components and services. Any manual updates performed at various occasions over time in such environments can introduce the risk of configuration drift — a situation where different servers develop different configurations and software versions.

5. Robust security: The codification of infrastructure is especially useful to replicate a network configuration for different projects, such as cloning a production network for test and development. Cloud-agnostic tools like Terraform allows IT admins to easily replicate the same configurations across various cloud providers, thereby reducing complexity in hybrid or multi-cloud environments.

The use of IaC also helps to introduce to code-level security practices, which is essential for a multilevel security strategy.

Implementing IaC in Your Organization

When implementing IaC in your organization, it is important to keep the following points in mind.

IaC is a crucial part of modern DevOps and cloud transformation initiatives, helping the software development and infrastructure management teams efficiently work together in order to provide predictable, repeatable, and reliable software delivery services.

However, coming up with the right IaC solution for your unique IT architecture isn’t something that should be approached lightly, or without the right guidance. Once you’ve set up your IaC environment the right way, you can start expecting quick results.

Virtual Digital Assistants: Is it the rise of machines already?


The term Rise of Machines should bring you memories of the movie Terminator series and Skynet.

Virtual digital assistants (VDAs) are rapidly gaining traction in both consumer and enterprise markets. So, what are these VDAs? Virtual digital assistants are nothing but automated software programs or platforms that help the user through understanding natural language in written or spoken form. Going by the current trend, virtual digital assistants are poised to digitally transform the user experience.

Apart from smartphone-based virtual digital assistants which are widely popular, VDAs are also beginning to enter other device types like smart home systems, fitness trackers, PCs, and automobiles. This rapid proliferation of virtual digital assistants is due to the accelerated innovation and scalability of associated technologies like AI and NLP (natural language processing).

In the future, you will be able to chat with your car about the best locations to visit. Your car will display the best possible route after analyzing driving time and getting your preferences conversationally. All these advancements will be due to the tremendous power of virtual digital assistants.

From the consumer-oriented virtual assistants like Siri, Amazon Echo, etc., to dedicated software for business use cases, virtual digital assistants are going to digitally transform the customer experience. Thus, an enterprise must build a VDA to stand out from the crowd. Here are some of the steps to build an effective virtual assistant.

Step 1: Build a flawless speech-recognition system. This process requires acoustic modeling, voice modeling, and a speech recognition engine

Step 2: Enable Natural Language Processing (NLP) which is the basic intelligence required to process semantics of a user’s speech input.

Step 3: Integrate machine learning or AI to improve the intelligence of the virtual digital assistant. This allows VDAs to learn, understand, and adapt based on the information available.

Step 4: Since responses should be instantaneous, VDAs need large scale systems that provide the power required for processing large amounts of data.

Step 5: Finally, all these modules should be secured using an API gateway to interface with several other systems. It is worth mentioning that, VDAs should be designed for a mobile-first and cloud-based environment.


VDAs would soon lead to the era of high customer satisfaction. With VDAs, there is tremendous opportunity to better engage customers and employees alike.

Customer services will become more proactive. Virtual assistants will learn more about you from your texts, searches, emails and it will start suggesting or predicting what you need, even before you ask.

Why It’s Vital for Companies to Focus on Data Engineering?


Digitalization is multiplying, making data the most prized asset in the world. Organizations are strategically moving towards insight-driven models where business decisions, process enhancement, and technology investments are handled with the knowledge gained from data. Big budgets are planned to make use of abundant data available, and this spending will only increase over the years. 

According to a recent IDC report, it is estimated that by 2025 the Global Datasphere will grow to 175 zettabytes (175 trillion gigabytes). It also states that 60% of this data will be created and managed by businesses, driven by Artificial Intelligence (AI), Internet of Things (IoT), and Machine Learning (ML). AI and ML are gaining mainstream focus among many industries, and global spending is expected to grow to $57.6B by 2021.

How Data Engineering is helping businesses succeed?

Organizations often consider Data Science to be the only method to gain meaningful insights necessary to drive their business goals. However, the real potential lies within Data Engineering, which allows companies to build large maintainable data reservoirs. These design data processes are scalable and ensure relevant data is available for Data Science and Data Analytics to process complex statistical programs and algorithms to provide useful results. Only with reliable and accurate insights created from diverse sources can help data analytics harness the full power of data. 

Today, AI and ML have become integral parts of organizations, helping them achieve higher operational efficiency, become agile, taper new market opportunities, launch new products with faster go-to-market, and provide higher customer satisfaction. But according to a survey done by MIT Tech Review, 48% of companies said that getting access to high quality and accurate data was the biggest obstacle in successfully implementing an AI program. To overcome this hurdle, businesses must focus on effective Data Engineering, which forms the basic building blocks for AI and ML.

Three advantages of effective Data Engineering:

1) Accelerates Data Science 

2) Removes bottlenecks from Data Infrastructure 

3) Democratizes data for Data Scientists and Data Analytics 

Once organizations understand and internalize this, it is easy to see how the potential of Data Engineering is limitless. 


How data engineering is helping businesses across industries

Industry influencers and other prominent stakeholders certainly agree that Data Engineering has become a big game-changer in most, if not all, types of modern industries over the last few years. As Data Engineering continues to permeate our day-to-day lives, there has been a significant shift from the hype surrounding it to finding real value in its use. 


Industry 4.0 is here, and the sooner organizations start their digital transformation, the better equipped they become to handle the evolving market conditions. What Industry 4.0 has brought is a significant shift in how manufacturing businesses are changing from being purely process-driven, to becoming data-driven. This essentially means that companies are either adding new digital components or updating their existing components with digital features. However, this creates a complex technology landscape where legacy systems have to interact with modern systems. 

An effective Data Engineering solution can communicate and retrieve data from different systems, sort out critical data from a pool of data, and process them to be analyzed further. Data Engineering bridges the gap between Production, Research Development, Maintenance, and Data Science. Data Engineering can help in enhancing the critical aspects of manufacturing industry—production optimization, quality assurance, preventive maintenance, effective utilization of resources, and, ultimately, cost reduction. 


Data has the power to make or break a business, and no one understands this better than Netflix. The incredibly successful data-driven company uses insights across its business functions to decide what new content to invest in and launch, enhance operational efficiency, and, most importantly, provide predictive recommendations for its global audience. 

Netflix has also used its robust Data Engineering system to convert over 700 billion raw events into business insights, which is one primary reason why the company continues to be the market leader.


The retail industry is continuously trying to tap into new business opportunities by gaining insights from data sources across the physical and virtual ecosystems. To gain these business insights, data must be gathered from a large network (comprising of POS systems, e-commerce platforms, social media, mobile apps, supply chain systems, vendor management systems, inventory management systems, in-store sensors, cameras, and a growing list of new sources). 

An effective Data Engineering solution can bring together massive sets of structured and unstructured data from entire value chain to provide trends, patterns, customer insights, and more. A retailer with stores across the globe and an omnichannel presence can harness data sources in innovative ways with Data Engineering to gain a detailed understanding of the market, the competition, and every step of the customer journey. 


Leading healthcare giants are progressively investing in integrating ML into their core functions. However, they are focusing on setting up their data infrastructure by building Data Engineering platforms. The healthcare industry is looking to unlock value from data to gain knowledge into the patient, healthcare worker, and the healthcare system on a large scale. 

Data Engineering brings together insights from electronic patient records and hospital data, as well as new advanced data sources like gene sequencing, sensors, and wearables. It offers them to Data Analytics to provide better medical treatment. 

How Data Engineering is fueling the businesses of the future

To manage data at large scale and segregate business-critical data from the rest, organizations need a long-term data strategy plan to be future-ready with Data Engineering as critical approach. 

Data Engineering creates scalable data pipelines

Distributed data processing systems can help create reliable data pipelines with low level of network management to meet huge volumes and tap into increasing data sources in a growing ecosystem of touchpoints. 

Data Engineering ensures that data is consistent, reliable, and reproducible

For data processing to be successful through the stages of ingestion, analytics, and insights, it is important that the data be compatible by ensuring it complies with the required formats and specifications. Data science can derive better insights from data by providing reliable and reproducible data.

Data Engineering helps ensure that processing latency is low

Most essential business insights are required to be in real-time to have an effective impact, be it with customer experience in the retail industry or predictive analysis in the financial sector. If the data being analyzed has a significant time delay, the insights can be less effective or completely ineffective. 

Data Engineering optimizes infrastructure usage and computing resources

Using the right algorithm for data engineering can save a considerable amount of money spent on resources. This can provide significant savings to organizations and help them optimally utilize their technology landscape. 

Businesses must design Data Engineering solutions that are unique to their needs and create customized frameworks rather than follow trends. At the same time, many new start-ups begin their data journeys with clearly defined data sets. In contrast, traditional organizations may have larger ones from legacy systems and data sets from new sources. It is important to understand that while the Data Engineering tools for a particular organization are zeroed, no general rule can be used. Only a comprehensive study of a company’s unique technology ecosystem and business needs can determine the type of Data Engineering systems that should be used. 

Data Engineering solutions must also be flexible. How data is produced and consumed is constantly evolving, so Data Engineering solutions or frameworks must be flexible to accommodate future requirements. Guiding the movement in this direction is the shift from traditional Extract Transform and Load (ETL) methods of the data pipeline to more pliable patterns like ingesting, model, enhance, transform, and deliver. The latter provides more flexibility by decoupling Data Pipeline services. 

Many experts focus on Data Engineering one step further by encouraging companies to adopt a Data Engineering Culture. This permanently recognizes the need for Data Engineering at all levels of an organization across functions and warns that business predictions will fail without effective Data Engineering and an appropriate ratio of Data Engineers to Data Scientists. 

The sooner organizations push for Data Engineering Culture and create organizational alignment, the more equipped they will be for the future, to which data holds the key. 

How TVS Next created a Data Engineering solution for one of India’s top utility companies

In the energy sector, large enterprises are turning real-time data to drive effective energy management. Energy corporations rely on data for efficient resource management, operational optimization, reduced costs, and increased customer satisfaction with better insights into supply and demand in real-time. 

TVS Next helped one of India’s leading utility companies build a distributed computing engine for processing and querying data at scale. The solution provided the company with tools to visualize key performance indicators using real-time data. With effective Data Engineering, the client improved the customer experience rather than relying on complex algorithms to predict outcomes. 

What are some of the achievements and challenges you have faced while planning a Data Engineering system for your organization? Share your story and get in touch with us here

Disruptive Technology for Healthcare


With immense technological advancement, global healthcare industry will transform tremendously and move towards digital. Factors like increasing population, declining healthcare budgets, and rise of chronic diseases add pressure on healthcare providers and governments to shift to innovative technologies. 

Consequently, the global healthcare industry is ideal for driving new technological advancements like Internet of Things (IoT). Smart devices such as smartphones, smartwatches, and other new emerging technologies will act as driving forces of this revolution.   

Let’s discuss some of the technologies that could transform the healthcare industry in the future. 

AI and Machine Learning

Machine Learning is a form of AI that offers learning capabilities to computers without being explicitly programmed. It means that computers can teach themselves to modify according to the need when exposed to new data. With AI, there will be a considerable amount of data to explore. Several tech conglomerates like Google, IBM, etc. have already started exploring these technologies’ potential applications in healthcare.  

There are truly exciting possibilities for the application of AI/ML for such digital surgery robots. 

  • A software-centric collaboration of robots with the aid of massive distributed processing 
  • Data-driven insights and guidance based on surgery histories (performed by both machines and humans) and their outcomes (favorable or not) 
  • AI-generated virtual reality space for real-time direction and guidance 
  • Possibility of telemedicine and remote surgery for relatively simple procedures 

Digital Therapy

These are healthcare interventions delivered to patients through smart devices like smartphones or laptops. They combine medical practice and therapy in a digital form. Computerized Cognitive Behavioral Therapy (CBT) is a new group of automated digital therapies that aims to provide CBT at scale with better engagement.  

“Digital Therapeutics: Combining Technology and Evidence-based Medicine to Transform Personalized Patient Care” 

Digital therapies are disease-specific treatment tools. Often, they are a substitute treatment (or the only treatment) with sensory stimuli; in other situations, they support or enhance conventional medicine incorporating electronic usage with tools or medications.

For example, some devices complement conventional treatment by helping patients manage and control their conditions, providing indicators such as remembering when and how much medication to take.

Apps and Smartphones

The full potential of smartphones is yet to be perceived by the healthcare sector. Companies are making efforts to curate quality apps, such as the NHS (National Health Service) app library. With powerful processing capabilities, smartphones can serve as the hub for new diagnoses and treatments.

Although the use of mHealth devices and applications is already common in clinical trials, pharmaceutical companies are now concentrating on connected drug delivery systems that will automatically identify and monitor medication usage by patients to enhance adherence. 

Health apps and smartphones help to: track personal health data, real-time communication with your doctor or another healthcare provider, and improve the quality of life for doctors and their patients. 

Portable Diagnostics

With devices like portable X-ray machines, we can bring cutting-edge diagnostics at our doorstep. Doctors can provide better quality of care by more profound and meaningful engagements with patients. It also leads to the continuous capturing of crucial health-related information, which will eventually reduce the overall healthcare costs. Assistive devices like smart wheelchairs are used by patients with permanent disabilities, which help them perform specific tasks and gather essential health information. This information can later be used for modifying treatment procedures.  

Online Communities

Online communities and famous healthcare networks like MedHelp bring medical experts and patients together to share health advice and tips. They also serve as a platform for tracking health data, which helps people better manage their conditions.  

Advanced technologies like these offer new opportunities to the healthcare system in improving correctness and usefulness of medical information. It also provides new ways to prevent, detect, and treat diseases at early stages rather than reach the terminal phase.  


Implantable Drug Delivery Systems  

It is estimated that nearly one-third of all medication prescribed to patients with long-term health conditions are not adequately taken as recommended by the physicians. Emerging technologies could change this by enabling healthcare professionals with continuous monitoring capabilities.  

In the future there could be sensors that are tiny and can be swallowed along with drugs. As soon as the pill dissolves in the patient’s stomach, the sensor will get activated and transmit data to a smartphone app. Patients and doctors can see how well they are adhering to the prescription, though it raises questions about the patient’s privacy.  


Blockchains are decentralized databases that keep record of how data is generated and changed over time. The main feature of Blockchains is that it can be trusted, as the records are authentic without a central authority guaranteeing accuracy and security.  

Though Electronic Health Records are commonly used, they are usually centralized. Some analysts state that Blockchain would bring several benefits to patients and doctors as compared to other records.  

Genome Sequencing

Breakthroughs in genome sequencing and its associated field will help us better understand the diseases. Genome sequencing gives a genetic profile of a patient’s illness by which doctors can predict their treatment.  

Globally, several big projects are underway to understand the association between genes and health conditions. In the UK, the government is funding 100,000 Genomes projects. In the US, one company has promised to build a database featuring 1 million genomes by 2020.  


Maintaining patient care in today’s hyper-connected environment depends almost entirely on maintaining and leveraging their network and services. Network administrators need to be vigilant and disciplined – not just for performance, but to prevent security disruptions. 

CIOs and CCIOs (chief clinical information officers) in healthcare organizations face the urgent need to keep pace with technology. They introduce next-generation technologies in an attempt to improve overall efficiency, speed and safety. 

Healthcare is evolving, and modern technologies will transform human life for the better again, just as antibiotics and anesthesia have changed for decades. For hospitals and suppliers working together to protect data, the future seems bright. 

Extracting Acronyms through Natural Language Processing



An acronym is a pronounceable word created from the first letter of each word in a phrase or title. An acronym is a kind of abbreviation consisting of a first letter or initial letters in a word. It’s also called short descriptors of phrase.  

Interesting Fact: Acronym was introduced as a modern linguistic element of English during the 1950s. Because acronym is called a term, its meaning is called expansion.  

Usage & Challenges 

An acronym is primarily used in language processing, web search, ontology mapping, question answering, text messaging, and social media sharing. Acronyms evolve each day dynamically, and finding their definition/expansion becomes a daunting task due to its diverse characteristics. Several researchers experimented with plain text and network expansion pairs for mining acronyms over the past two decades. Manually edited online archives have pairs of acronyms, but regularly reviewing all possible meanings is intimidating.  


To handle this issue, TVS Next has built a specialized product to extract acronyms from a document in a few seconds. This product is built on Python for Natural Language Processing.  

Below are some pointers that describe how our research works that help us solve the problem mentioned above.   

Heuristics Approach 

NLP (Natural Language Processing) and pattern-based methods include heuristics. 

  • The NLP-based approach uses a fuzzy-matching Statistical Model based on the principles of Levenshtein’s Distance algorithm.  
  • The pattern-based approach uses custom rules that work with data from multiple domains, combined with Statistical Modelling to extract the Acronyms and their Expansions. These methods are written after considering features in the text as characteristics of acronyms – ambiguity, nesting, uppercase letters, length, and para-linguistic markers.  

An Acronym Finding Program (AFP) is a simple, free-text expansion recognition method. This program applies an inexact matching algorithm for mining AE pairs. A tool known as Three Letter Acronym (TLA) uses para-linguistic markers such as parenthesis, commas, and periods to derive acronym meaning from technical and government documents.  

Developing the Product  

A Statistical model has created to provide the user with a Solution that gives ease of access to acronyms that appear throughout the document. The designed solution can be integrated into various tools and technologies that deal with text-based information. The solution proves to be useful while combining it with tools that parse PDF documents. It deals with – tables, free-flowing text.  

A document consists of multiple tables that are very similar in structure; hence our solution uses a Table Classification method to differentiate the acronym table from the rest. Various types of Statistical Methods were incorporated to quantify features/patterns that help define what an acronym will look like. This solution was used to classify an acronym table from the rest and then extract acronyms from the table.   

For free-flowing text, a similar technique has been used where the patterns/features of an acronym are incorporated to differentiate it from the rest of the free-flowing text. There are words extracted that can turn out to be acronyms. These words appear along with their expansion in the text. After extracting suspected acronyms, we quantify the words that consist of acronyms using statistical models and compare them to their expansions.  

By enforcing the following statistical models, 80% of acronyms are obtained that are present in a document. It is essential to accommodate variations in how text is written. Simple human punctuation errors can affect the entire acronym, not falling under rules of how acronyms are generally written. A dynamic method where custom rules that works with data from multiple domains are combined along with specific Statistical Models has been implemented that will uncommonly parse texts.  

On executing this dynamic method and testing various documents, we could conclude that the Statistical Model-based acronym extraction method has been performing with over 95% accuracy, even surpassing open source solutions provided by Spacy called Blackstone available in the market at the moment. Blackstone works on the techniques mentioned in a research paper written by Ariel S Schwartz et al. [2]., Multiple comparisons were made, between Blackstone and the Statistical-method based Acronym Extraction.


The Statistical Model-based acronym extraction method scanned an entire document of 100+ pages in milliseconds and displayed 98% accuracy. The average time taken to scan a document is a few seconds, and the accuracy of this product has been achieved between 94-98%. The product was tested on documents belonging to various domains, and it still yielded similar results. The product is developed on an experimental basis, and we are set to improve its efficiency and performance each day. There is plenty of room for improvement with subject to market changes. The product experiments with a set of Statistical models and custom rules, and the team is working on dynamic changes using AI that scans documents based on results. This product proves to be useful for lengthy and complicated engineering and medical documents. This product is one of its kind, and we are proud of our development.

At TVS Next, we re-imagine, design, and develop software to enable our clients to build a better world.  

How to Pivot to the Culture of Quality Assurance


The position of QA engineers has evolved from finding errors to enhancing user experiences. It indicates that QA engineers and testing must align themselves with business objectives and the larger picture.  

While technology plays a vital role in the entire quality ecosystem, here are five ways how it aids in improving quality processes.  

1. Advance data analysis and Machine Learning   

Artificial Intelligence (AI) is steadily gaining ground in the market. Machine learningsubset of AI, ‘learns’ programs by finding patterns from which results are predicted. An example may be a medical device company that analyzes complaint data to determine when a problem occurs. All complaint data are stored in a centralized digital archive for consistency in identifying and mitigating the problem.  

20% of businesses have already made use of on-demand applications such as Internet of Things (IoT), Robotics Process Automation, AI, and Machine Learning, according to 2019 KPMG report. In the same survey, 52% of organizations estimated their technology spending to be increased in the next year.  

2. Value beyond regulatory compliance  

While compliance is critical, quality also affects business. Sometimes quality takes a band-aid approach to solve problems quickly and does not examine the root cause or prevent future problems. Some companies are sufficiently taking a forward-thinking approach for proactive management of quality. It is becoming a necessity in today’s regulatory landscape. Firstly, many businesses globally sell through several regulatory bodies’ jurisdictions. Although specific regulations overlap, the effort to improve overall quality is always rewarded. In comparison to solving a single problem, this significant picture is changing an organization.  

Quality goes beyond its department as part of this significant approach. It must be part of the culture of an organization that permeates all the activities of the business. Quality 2030 is subjected to remove departmental siloes between quality and production, between manufacturers, suppliers, and regulatory agencies. Quality 2050 is predicted to eliminate siloes. By digitizing software systems that interact through several divisions, businesses are now able to take steps in that direction to eliminate these siloes.  

3. Customer analysis to find and resolve problems  

Medical device or pharmaceutical service departments usually do not communicate with patients who benefit from their products. Healthcare is becoming customer-focused and applies to companies in the life sciences space that supply consumer goods. The 21st Century Cures Act requires patient data to collected in drug development processes. It is mentioned in the Cures Act.  

Companies learn to improve quality beyond regulations when talking to consumers. However, given that patients play an active role in their healthcare, their perception of pharmaceutical and medical device companies becomes essential.  

4. Harmonization and monitoring of processes to reduce total costs  

The importance of quality can never be overemphasized. Excellence for all organizations is a competitive priority. Cost and quality were considered as two different ends of the same continuum but this view has changed dramatically over time. Improved quality has reduced the total cost function on a long-term basis thanks to the continuous improvement of processes. Doing things right for the first time, managing transactions based on facts, and applying statistical process controls has led to better quality for organizations and reduced costs.  

5. Training and communication systems to build quality competence and culture  

Training and learning programs used by organizations are successful ways to address quality proactively. The problem could mean a significant overhaul of the company’s current processes and systems. For example, if a company has a different Quality Management System (QMS) for each site, data for those sites must be compiled manually to be used. Manual data input increases the likelihood of human error and compromises the integrity of the data. It is better to switch from paper-based or silo-based systems to digital, centralized ones.  

Digitization and automation would not remove quality jobs – only the value-added activities would return to the focus of work. The tasks that are eliminated are repetitive and monotonous.  


Like software development, QA testing is a constantly changing field, with new developments every day. The need for dedicated QA professionals is only growing, as businesses are recognizing the need to produce high-quality software in a competitive market. The success of their products and businesses as a whole will depend on quality assurance.   

Can you be Agile without Continuous Delivery


Challenges During Agile Transition 

Organizations performing an agile transition are often unhappy with the results. Usually, they follow scrum structure and build cross-functional teams with all the skills required.  

Agile coaches and scrum masters explain the three foundations of scrum — transparency, inspection, and adaptation. They also help us understand the different scrum activities such as sprint, daily scrum, sprint planning, sprint analysis, and sprint retrospective.  

Teams are mentored on the value of quality improvement and they are demonstrated that every scrum procedure is an opportunity to inspect and adapt.  

Despite these efforts, the business value delivery seems to take far longer than expected and enterprises are not reaping the agile transformation rewards or promises.  

Software Development Evolution 

A usefully simplified description of the advances in software development are listed below: 

  • Waterfall claims that a team would only start getting the software ready for releases after all the release functionalities (i.e., full featureare created.  
  • Agile insists that the team will be ready to release its software throughout development. Most types of agile assume that this will happen regularly.  
  • Continuous delivery requires the team to keep the software always ready to be released during development. It is not traditional agile as it does not require waiting and making a special effort to build a releasing framework. 

Agile Software Development  

Agile software development is not a methodology in the strict sense of the term. It is more of a culture or an approach where you recognize the needs of the situation and adapt to it accordingly.  

This technology requires adaptive planning and evolutionary development in addition to early delivery. Hence, there is a need for continuous improvement as it encourages a flexible response to the changes in the surroundings.  

Applications of Agile: Simple Examples  

Agile software development has a variety of applications in diverse fields. You can sense the difference in the technologies involved in activities we do almost every day. Take the internet or mobile banking, for instance. Transferring funds from one account to another is a regular activity for any person or business. You need security layers to be in place to ensure the safety of your funds.  

Now, these security layers need constant updates so that hackers are not able to crack the code. Hence, based on feedback received from industry experts and consumers, the mobile banking app developers keep updating their systems. Therefore, you find something new every fifteen days or so. In the early days, there were only passwords as a layer of security. Now you have additional layers like grid combinations, 2-way authentication measures like OTP and so on. The process of improvisation is continuous.  

This is agile software for you. They cannot wait until something drastic happens. The process of delivery has to be continuous. Agile software specializes in identifying threats or problems before they materialize. Thus, it is ready with a solution beforehand. However, having a solution alone is not enough. The critical aspect is the delivery and utility of the solution. Thus, one can say that agile software is of no use unless there is continuous delivery.  

Hence, you can see that continuous improvement is always necessary for every industry.  

Continuous Delivery — A Subset of Agile 

The very definition of agile software development says that it is a group of software development procedures that are based on iterative and incremental development. There is a continuous evolvement that requires collaboration among various factors. Therefore, agile software cannot work without continuous delivery. There has to be a constant and continuous change in the circumstances.  

You can see the application of agile software in a project management process. Breaking down a large project into smaller doable actions is the best way to approach any project. In this way, you will always be ready to change your plan of action should anything go wrong somewhere in between. Web designing is also an excellent example of the application of agile. You keep on improvising the design to suit customer preferences. You gauge these preferences by interacting with the customers at frequent intervals. Thus, you can see that agile is all about flexibility and adaptability.  


This brings us back to the question Can you have agile without continuous delivery”? You can see that it is just not possible to do so. The principal characteristic of agile is adaptability. Now, adaptability means continuous change concerning the situation. When the situation demands a particular solution, you need to adapt. This is what makes agile an exciting piece of software development process.  

Kotlin For Android: A Boon To Developers



JetBrains, known for IntelliJ IDEA (Android Studio is based on IntelliJ), has introduced the Kotlin language. Kotlin is a statically-typed programming language that runs on the JVM. It can also be compiled to JavaScript source code. Kotlin has some amazingly cool features!

Kotlin is a great fit for developing Android applications, bringing all of the advantages of a modern language to the Android platform without introducing any new restrictions:

  • Compatibility: Kotlin is fully compatible with JDK 6, ensuring that Kotlin applications can run on older Android devices with no issues.
  • Performance: A Kotlin application runs as fast as an equivalent Java one, thanks to very similar bytecode structure.
  • Interoperability: Kotlin is 100% interoperable with Java, allowing to use all existing Android libraries in a Kotlin application.
  • Footprint: Kotlin has a very compact runtime library, which can be further reduced through the use of ProGuard.
  • Compilation Time: Kotlin supports efficient incremental compilation, so while there’s some additional overhead for clean builds, incremental builds are usually as fast as or faster than with Java.
  • Learning Curve: For a Java developer, getting started with Kotlin is very easy. The automated Java to Kotlin converter included in the Kotlin plugin helps with the first steps.

Kotlin has been successfully adopted by major companies, and a few of them have shared their experiences: Pinterest has successfully introduced Kotlin into their application, used by 150M people every month.

Basecamp’s Android app is 100% Kotlin code, and they report a huge difference in programmer happiness and great improvements in work quality and speed. Keepsafe’s App Lock app has also been converted to 100% Kotlin, leading to a 30% decrease in source line count and 10% decrease in method count.


Kotlin aims at creating a language that would be in line with the same principles. It drive tools to create something that helps developers with the tedious and mundane tasks, allowing them to focus on what’s truly important. And of course make the process as enjoyable and fun as possible.


The Kotlin’s goals are to be a language that is available on multiple platforms and this will always be the case. It will keep supporting and actively developing Kotlin/JVM (server-side, desktop and other types of applications), and Kotlin/JS. We are working on Kotlin/Native for other platforms such as macOS, iOS and IoT/embedded systems

When you run a Java application, the app is compiled into a set of instructions called Bytecode and runs in a virtual machine. Over the past several years, a number of new programming languages have been introduced to also run on the Java virtual machine. Whilst the resulting app looks the same for the virtual machine, the idea is the language features can help developers write simpler code and fix some of Javas issues.


Kotlin aims to fill that gap of a missing modern language for the Android platform. There are a few core tenets that Kotlin lives by; it strives to be:

  • Concise to reduce the amount of boilerplate code you need to write
  • Expressive to make your code more readable and understandable.
  • Safe to avoid entire classes of errors such as null pointer exceptions.
  • Versatile for building server-side applications, Android apps or frontend code running in the browser.
  • Interoperable to leverage existing frameworks and libraries of the JVM with 100 percent Java interoperability.