Niruphan Rajendran


Niruphan Rajendran

A creative, self-motivated and results driven individual with over 11+ years of experience as a Non-Functional Engineering Consultant. Proficient in handling and demonstrating end to end performance testing/engineering and tuning activities for large programs.

Desire to present key achievements internally & externally and give back to the wider software community at meet-ups and conferences. Always strive to convert success stories into valuable mind share. Have implemented my innovative ideas and published them as whitepapers,

  •  IoT Device Security Firmware Analysis
  •  Adopt NFT to Realize the Full Potential of Connected Devices
  •  Telemetry Performance Test in Connected World – IoT Smart Devices
  •  How to design your tool Infrastructure to test SAP products

Technical Expertise

SAP Cloud Migration Architect | Cloud Certified (Microsoft Azure – Azure for SAP Workloads Specialty (AZ-120), Azure Administrator Associate (AZ-104) | Smart IoT Devices | IoT Device Security | Telemetry Data Simulation/Ingestion | Cloud Migration Consultant | Intercepting Networks | Code Tuning | Code Profiling | Tool Orchestration | Mobile Apps Architect

Project Management & Consulting

Strategic Planning | Providing Solutions & Strategies – IoT, Cloud, Mobile, Data, ERP | Establishing key performance indicators (KPI) | NFR Elicitation | Team Development | Business Development | Demonstrating NFT services


Title: Unlocking the power of Machine Learning in Mobile NFT world


Machine learning (ML) is a branch of artificial intelligence (AI) which focuses on the use of data and algorithms
to imitate the way that humans learn, gradually improving its accuracy. It is an application of Artificial
Intelligence which can automatically learn and improve from experience without human interference. Machine
learning (ML) has transformed how we as humans interact with machines, technologies, and data. But it wasn't
always so popular; what started out as a niche industry has grown into a billion-dollar market. Today, machine
learning forms a part of every industry you could think of healthcare, finance, entertainment, retail, and
manufacturing. It has become essential for businesses to adopt ML to boost revenue, cut costs, and automate

Here are some fascinating ML statistics to highlight its vital role in the modern world. Netflix saved $1 billion due to its machine learning algorithm for the combined effect of personalization and content recommendations. Adding to this, 60% reduction in Google Translate errors was found when changed to GNMT—a translation algorithm powered by machine learning. The accuracy of Google's AI machine learning algorithm in predicting a patient's death is 95%. This also plays important role in Mobile Users of which 97% of mobile users are using AI-powered voice assistants. ML have been aggressively launched to serve Mobile application development.

We all know that a mobile device has become an integral part of our life. Applications deployed on devices struggle a bit even, it’s a “Yucky” situation to us. Mobile NFT helps to a certain extent in overcoming these challenges. But the big question is, does it fix all the issues prevailing to each release in this DevOps world? The answer is “NO”.

When it comes to Non- functional there must be more focus on data analytics which will be key to analyse
reports/events successfully. Predicting the behaviour to understand the metrics in simpler and much easier way
is a challenge. As we have huge chunks of data where we want to put together & provide a valuable outcome to
business is a big ask. Below paper will focus more on the smarter & intelligent approach to overcome all this

ML based AI Non-functional testing teams are facing an extraordinary challenge today is, post automated
process, the overall release report must be consolidated for analysis and predicting the report by learning,
training & evaluating it. In this DevOps world, there is a business need to share the predictive analysis to
enhance the business process. From that perspective, the key asks are:

  1. How would they ensure the coverage of the testing across Mobile OS versions in real world use case
    scenarios are performant?
  2.  How to align the business to focus on the required non-functionality of the entire Ecosystem under
    effective cost

To overcome all these challenges, this paper will speak about the solution for each challenge from our point of view.

In this Point of View [PoV], our focus is more on Data processing, Data Training and Data Evaluation. There
are huge chunks of data residing with Non-Functional team. But for better evaluation & prediction we don’t
have any specific technique or approach to take it up to Business. Adapting to the ML can help us fix those
challenges & enhance the business. A smarter & intelligent approach to predict the user behaviour will help us
understand metrics in much better way. Here we are proposing a newer way using TensorFlow framework by
integrating ML algorithms which will put together a huge chunk of data & provides a view on predicting the
future behaviour. For example, consolidating response time across different bandwidths, devices, OS versions
and predicting a future pattern of behaviour. This helps in analysing the flavour that needs to be tested, more
importantly do we really need additional performance test and helps BA to align their businesses accordingly.
By this way this reduces the overall cost that spent on different services.

For successful implementation, it needs to shift its focus at macro level and overcome below all the challenges:

  • Understanding the data
  • Data Visualization & Analysis
  • Data Prediction

Our solution will focus on modern day Non-functional testing approach for Mobile NFT world using advanced
ML technique, identify its key performance indicators and provide prediction for end-to-end process.

Solution Overview:

  1. Understanding the data: Collecting and organising data is a need of the hour. Key data will be
    collated and that helps in training and use it for prediction.
  2. Data Visualization & Analysis: Once data is loaded the next step is to train those data for better
    prediction. In this step we will visualize all those data to plot it in graph.
  3. Data Prediction: The main factor of prediction comes over here. Here to predict right metrics for the
    Application with different parameters and combinations
  • After successful implementation of this solution, below listed benefits can be achieved
  • Algorithm for prediction will have an effective impact in delivery
  • This solution will save huge effort & cost for customers where they didn’t require to procure new
    devices for different testing
  • It helps Scrum Masters/Business to decide, do we really need additional performance test for release.
    This in turn reduces cost for customers.
  • Recommending approach will help the customer to predict the key non-functional information for
    different OS versions and will support to tune App releases.

Above solutions aided the business to derive key non-functional metrics at different predictions and this helps to
visualise and decide performance of the application. This diverse solution certainly benefits not only for the
Mobile user’s performance but also across several other type of users.