Performance Testing – Assured Speed, Scalability and Stability of Applications

Today we have more expectations from software than we used to have earlier. This is the primary reason why Performance testing is turning out to be so critical. Performance testing is a part of any organization IT system. It’s a given that modern application regardless of volume usage should undertake standard Performance testing. These tests reveal defective assumptions about how applications handle high volume, guarantee framework-scaling works as anticipated and recognize load-related defects. Performance testing’s capacity to identify defects happening under high load can help enhance applications regardless of scale.

It is unusual that organizations keep on ignoring the significance of Performance testing, often-deploying applications with slight or no understanding of their performance. This mentality has changed little in the course of recent years as failure of high-profile software applications remains the bottleneck.

In short, Performance testing should be the topmost priority of the organization before releasing a software or an application.

Why Performance Testing?

Performance testing is used to check how well an application can deal with user traffic. By putting a repeated scenario on an application or site, it is likely to analyse breaking points and assess projected behaviour. In particular, Performance testing is performed to identify reliability, speed, scalability, responsiveness and stability of the software.

As a code change from a team that is endlessly incorporating new features, bug fixes can influence how an application looks, and functions on different devices and browsers.  It can change how rapidly that application loads across machines.erformance testing is used to check how well an application can deal with user traffic. By putting a repeated scenario on an application or site, it is likely to analyse breaking points and assess projected behaviour. In particular, Performance testing is performed to identify reliability, speed, scalability, responsiveness and stability of the software.

This is why performance testing is so crucial to a well-rounded QA strategy— checking an application’s performance, ensuring consumers are experiencing acceptable load time, and site speed is foundational to high-quality software.

Importance of Performance Testing

1.     Quick functional flows matters

Every end user of Software expects that each transaction s/he makes should be completed quickly, or take less time. Performance Testing plays crucial role in the same.

2.     Capacity Management

A performance test gives inputs on whether hardware or production configuration needs any improvement, before a new software is released on a live environment.

3.     Software Health Check-up

A performance test helps check the health of any software, and gives inputs for additional fine-tuning.

4.     Quality Assurance

A performance test also inspects the quality of code written in the development life cycle. It is a crucial part to identify if the development team needs special training, etc.  to create more fine-tuned code.

Now that you clearly know the importance of Performance testing, finding the bottleneck should be your next goal.

In a complex system, built with many pieces like application servers, network, database servers and many more there are high chances of you facing a problem. Let us discuss about the possible Bottlenecks.

What are Bottlenecks?

Performance bottlenecks can lead an otherwise functional computer or server to slow down or crawl. The term “bottleneck” can be used for both an overloaded network and the state of a computing device where any two components at any given time do not match pace, thus slowing down the overall performance. Solving bottleneck problems usually results in returning the system to operable performance levels; yet, addressing bottleneck fix involves first identifying the underperforming component.

Here are four common causes of bottlenecks

CPU Utilization

According to Microsoft, “processor bottlenecks occur when the processor is so busy that it cannot respond to requests for time.”  Simply put, these bottlenecks are a result of an overloaded CPU that is unable to perform tasks in a timely manner.

CPU bottlenecks appear in two structures:

  • a processor running at more than 80 percent volume for a prolonged period, and
  • an excessively long processor queue

CPU usage bottlenecks regularly originate from the lack of a system memory and constant disruption from I/O devices. Settling these issues includes improving CPU power, including more RAM, and enhancing programming coding proficiency.

Network Utilization

Network failures happen when the correspondence between two devices comes up short on bandwidth or processing capacity and is unable to finish the task rapidly. According to Microsoft, “network bottlenecks occur when there is an overloaded server, an overburdened network communication device, and when the network itself loses integrity”. Solving network usage issues normally includes adding or upgrading servers, and upgrading network hardware like hubs, routers and access points.

Software Limitation

Often problems related to performance occurs from within the software itself. At times, programs can be designed to deal with just a limited number of tasks at once, this makes it impossible for the program to use up any extra CPU or RAM assets even when they accessible. Furthermore, a program may not be written to work with numerous CPU streams, thus only using a single core on a multicore processor.

These issues are settled through rewriting and fixing software.

Disk Usage

The slowest segment inside a PC or server is generally a long-term storage, which involves HDDs and SSDs, and is usually an inevitable bottleneck. Additionally, the most rapid long-term storage solutions have physical speed limits, making this bottleneck cause one of the most troublesome ones to investigate. In most cases, disk usage speed can develop by reducing fragmentation problems and increasing data caching rates in RAM. On a physical level, you can solve insufficient bandwidth problem by moving to faster storage devices and expanding RAID configurations.

High-level activities during Performance Testing

Test Coverage

Test coverage includes a colossal ability to cover all functionalities while conducting performance testing. Although, the scenarios must be exhibitive of different parameters, you can attempt automating key functionalities by assembling many scenarios. User data must be projected properly, as there would be several users using the system in their own context.

Non-Functional Requirements

Functional as also non-functional requirements  hold equal importance in performance testing. Functional requirements are far more specific and contain within them input data types, algorithms, and functionality to be covered. The real challenge is identifying less specific non-functional requirements- some of which are  stability, capacity, usability, responsiveness, and interoperability.

Performance Test Analysis

Analysing the performance test results is the most challenging and key task in performance testing. It requires you to have detailed knowledge and good judgment to analyse reports and tools. Moreover, you need to regularly update the tests based on the situation.


Proactive Performance testing efforts help customers get an early feedback and assist in baselining application performance. This in turn ensures that cost of fixing the performance bottlenecks at later stages of development is drastically reduced. It is always easier and less costly to redesign an application in its early stages of development than at a much later stage.

This also makes sure that performance bottlenecks such as concurrency, CPU or memory utilization & responsiveness are addressed early on in the application life cycle.

Nitor excels at evaluating  the performance of different technology& domain applications. It has well defined processes & strategies for baselining the application performance.

Nitor TCoE has expert performance testers who are capable of executing the performance engagement with close coordination with various stakes holders. Nitor performance testers are highly skilled in carrying out performance testing activities through open source tool or Microsoft tools set.

For more information, please contact

Open Source Full-Stack Millennial Programmers (MPs) at Nitor

Software Products and Enterprise Digital Platforms are disrupting the market  So are development tools, programming languages, and development platforms. All of these  impact the job description of a software developer. The paradigm of the Full stack and Cross stack developers (often used interchangeably) has been there for some time now. However, the compelling options offered by brand new development platforms are  making this paradigm almost inevitable to adapt.

We at Nitor call this evolution in software development as an era of “Open Source Full Stack**” software development. This era is reflective of Millennial Programmers (MPs) who have the attitude and aptitude to embrace Full Stack development. A quick glimpse of how these  millennial developers think, work and adapt based on some real incidents:

Functional Programming (FP) Languages: One of our groups of MPs was tasked with evaluating the possible usage of functional programming languages like Scala or Kotlin (which interoperates with Java) to specifically benchmark performance and code maintainability. MPs self-trained themselves on Kotlin. We reviewed these prototypes on Kotlin in a matter of days. It was overwhelming to see how these guys were able to adapt to complex FP concepts such as pure functions, lambda calculus and higher order functions with equal ease as that of traditional object-oriented programming concepts.

Design thinking in action: We were challenged to build a very complex document parsing engine which would parse terabytes of documents every day. Given the tight timelines, our team convinced the customer to use design thinking to do rapid prototyping instead of a  lengthy design (documentation) phase. What followed was a crisp prototype built using open source cloud-based software, and design that could seamlessly auto-scale and parse TBs of documents daily. Machine learning was applied for identifying the right document template to parse, and an open source rule engine was used to configure rules for the parsing algorithm – all in just a month’s time!

Patterns and Practices in Software: Robust software products should be built using principles of configurability, modularity with security, interoperability, and interfaces (APIs, queues, and service bus), monitoring and health checks. Traditionally, architects used to toil hard to build these blocks as custom frameworks in the software. However, things have evolved very fast in this area. Fast forward to last week, when one of our developers used Spring Boot just to click through and add some annotations to create a solution structure with most of these abilities plugged into the software.

Overall, the whole approach and lingo have changed. Here’s how:

  1. The debate of the API-first approach has been settled, and MPs are now widely using new frameworks to build loosely coupled microservices.
  2. JavaScript (JS) and JS stack programming has evolved. Typescript is the new JavaScript. ReactJS has emerged as a preferred hybrid mobile-first platform for many. MPs have adapted to this change with great ease.
  3. First Time Quality is an old mantra. However, advanced tooling for TDD/BDD/DevOps have empowered MPs to make it the de-facto way to build code.
  4. Docker is disrupting the manner in which cloud-based software is built – this needs a learning curve to design around these new capabilities
  5. Serverless code architecture with Amazon Lambda and Azure Functions is re-defining the paradigm of pay.

Data lakes and high- performance columnar DBs are replacing traditional DWH (Data Warehouses) and traditional MPP (Massively Parallel Processing) DWH. Data Analytics has become synonymous with predictive analytics and Machine Learning. They have started to build visual tools for data scientists and data analytics. The important  point is that the fine line between application developers and data developers is getting blurred.

In fact, Microsoft, which was once a very close development platform for Windows OS, is now making forays into Open Source. It has been  in the news for its open source platforms,.Net/ASP.Net Core, which have introduced modular deployments. Java 9 with Modular JDK and Modular Source code stands toe to toe.

All these changes call for full stack developers who can keep learning and adapting faster than ever before. Organizations will find it compelling to hire “Open Source Full Stack Millennial Programmer” instead of  specialist developers.

Nitor being a niche technology company in software product engineering services, we at Nitor see an increase in demand from our customers for such developers. Our target is to incubate, i.e.hire and ensure that we increase the head count of full stack developers at least four-fold by the end of this year.

For organizations like us, this also calls for a major cultural and mindset change in how we incubate and manage these programmers, including adapting ourselves to the technology of millennials. Please watch this space for more on how we plan to do this. Till then, Go Full Stack!

** Please write to us at if you want to join us as a Full Stack Developer.