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Who is the father of artificial intelligence (AI) in India?
Hey junior, great question! "Who is the Father of AI in India?" – This comes up in a lot of interviews and college quizzes, and it's one of those topics that shows how deep India's tech roots go. Let me explain it to you like we're having tea in the cafeteria. No fluff, just the real story. To reallRead more
Hey junior, great question! “Who is the Father of AI in India?” – This comes up in a lot of interviews and college quizzes, and it’s one of those topics that shows how deep India’s tech roots go. Let me explain it to you like we’re having tea in the cafeteria. No fluff, just the real story. To really get this, you need to know more than just the name. You need to understand the history of AI in India and how one person basically built the foundation for everything from chatbots to research on self-driving cars today.
First off, who’s globally the big boss of AI?
Before we dive into India, quick context: Who is the father of artificial intelligence worldwide? That’s John McCarthy, the American genius who coined the term “artificial intelligence” back in 1956 at the Dartmouth Conference. Who is known as the father of AI, who is considered the father of artificial intelligence, and who is called the father of artificial intelligence – all point to him. He invented LISP (that legendary programming language for AI) and kicked off the whole field. But India? That’s a different chapter, and ours has its own legend.
Enter Dr. Raj Reddy: The undisputed Father of AI in India
If there’s one name that screams “father of AI in India“, it’s Dr. Dabbala Rajagopal “Raj” Reddy. Born in 1937 in Andhra Pradesh, this guy didn’t just study AI – he pioneered it in a country that was still figuring out computers. An Indian-American computer scientist, Raj Reddy is the first Asian to snag the Turing Award (the Nobel Prize of computing) in 1994, shared with his colleague Edward Feigenbaum for their work on expert systems.
Why him? Let’s rewind to the history of AI in India. AI didn’t explode here overnight with ChatGPT hype. It started humbly in the 1960s-70s when India was importing computers (remember the balance of payments crisis?). Early sparks came from profs like H.N. Mahabala at IIT Kanpur and G. Krishna at IISc Bangalore, who introduced the first AI courses. But Reddy? He took it global. In the 1970s at Carnegie Mellon University (CMU), he built the Hearsay I system – one of the world’s first speech recognition programs that could understand spoken English. Imagine: machines hearing humans in real-time. That was revolutionary, especially for a developing nation like ours dreaming of tech self-reliance.
Back home, Reddy didn’t just theorize. He co-founded the Robotics Institute at CMU (1979), which trained hundreds of Indian researchers. And get this – he’s also called the father of robotics in India because his work bridged AI and robots, inspiring everything from industrial arms to modern drones. In 1998, he helped establish IIIT Hyderabad, turning it into India’s AI powerhouse. Today, IIIT-H pumps out startups like Sarvam AI and Krutrim, India’s homegrown LLMs. Without Reddy’s vision, our AI ecosystem might still be crawling.
Why Reddy stands above the rest
Look, there are other heroes in the history of AI in India – no denying that. Prof. B.L. Deekshatulu kicked off pattern recognition at IIT Kanpur in the 1970s. Bidyut Baran Chaudhuri’s OCR work at ISI Kolkata in the 1980s was groundbreaking for digitizing Indian scripts. Vijay Chandru at IISc pushed robotics and AI simulations. And don’t forget the 1986 Knowledge-Based Computing Systems (KBCS) program under Rajiv Gandhi, which funded AI at IITs, IISc, and CDAC. But Reddy? He’s the connector – the one who brought global cred, trained generations, and made AI practical for India’s problems like multilingual speech or rural automation.
His mantra? “AI for the masses.” He focused on real-world apps: speech tech for non-English speakers, robots for agriculture, AI for healthcare in villages. Padma Bhushan (2001), Padma Vibhushan (2011), Legion of Honor from France – the awards stack up because his impact does too. Even PM Modi has shouted him out for shaping India’s AI journey.
The roadmap: How India’s AI story unfolded
To really get why Reddy is the father of AI in India, you gotta see the timeline:
2000s-2010s: AI boom with Haptik’s chatbots, IITs churning PhDs. Reddy mentors the next wave.
Reddy didn’t just code – he built institutions. IIIT-H alone has spawned 100+ AI startups. He’s 87 now, still active, pushing “AI for Bharat” – ethical AI that serves 1.4 billion people, not just elites.
Busting myths and honorable mentions
Some folks mix up father of AI in India with global figures or hardware pioneers like Ajai Chowdhry (“father of Indian hardware”). Nope – Reddy’s the AI guy. Globally, who is the father of artificial intelligence is McCarthy, but in India, it’s Reddy hands-down. Robotics? He’s that too, via his CMU institute influencing Indian labs.
Shoutouts to runners-up:
But the title? Reddy’s. As he said in a TEDx: “AI must solve India’s problems first.”
Why this matters for the learners trying to excel in artificial intelligence career?
In your career – data science, ML engineering, whatever – knowing the father of AI in India isn’t trivia. It reminds us: India wasn’t late to AI; we were building it quietly. Reddy shows persistence pays – from rural Andhra to Turing glory. Study his Hearsay papers, join IIIT-H hackathons, build something for India’s 500M non-English speakers.
Next time an interviewer asks “who is known as father of AI in India“, drop Reddy’s name with this story. You’ll stand out. Got follow-ups on his projects or modern AI leaders? Hit me up – I’ve got notes from my prof’s lectures!
Sources: Wikipedia on Raj Reddy, IndiaAI.gov, Skillschool.co.in. Stay curious!
See lessWhat are primary and secondary technical domain skills?
Primary and secondary technical domain skills are simply two different layers of your professional toolkit. Primary skills are the core, “centre‑stage” capabilities that define what you do. Secondary skills are the supporting abilities that make you more effective, flexible and valuable, but they arRead more
Primary and secondary technical domain skills are simply two different layers of your professional toolkit. Primary skills are the core, “centre‑stage” capabilities that define what you do. Secondary skills are the supporting abilities that make you more effective, flexible and valuable, but they are not the main reason a company hires you.
Below is a detailed, fully original explanation with lots of real‑world examples.
1. What are primary technical domain skills?
Think of your career like a building. The primary technical domain skills are the foundation and main pillars. If those are weak or missing, the building simply cannot stand. These are the skills that:
Define your role (data analyst, backend developer, DevOps engineer, etc.).
You use almost every single day on the job.
Are evaluated deeply in technical interviews (coding rounds, case studies, whiteboard discussions, etc.).
When a recruiter or hiring manager looks at your profile, the first question is: “Does this person have the primary skills needed to do this job?” If the answer is no, secondary skills hardly matter.
Examples by role
1) Data Analyst
Primary technical domain skills typically include:
SQL: Ability to write complex queries, joins, aggregations and window functions to pull and manipulate data from databases.
Spreadsheets / Excel / Google Sheets: Using formulas, pivot tables, lookups and basic automation for analysis.
Data cleaning and exploration: Handling missing values, outliers and basic data transformation.
Basic statistics: Understanding averages, distributions, correlations and basic hypothesis testing.
If a data analyst cannot write SQL or handle messy data, they cannot perform their core job, no matter how many secondary tools they know.
2) Data Scientist
Primary skills usually cover:
Python or R: Writing scripts, working with libraries like pandas, NumPy, scikit‑learn, etc.
Statistics and probability: Hypothesis testing, distributions, confidence intervals, experimental design.
Machine learning: Regression, classification, clustering, model evaluation metrics, basic feature engineering.
Problem formulation: Converting a business question into a data/ML problem and designing a solution approach.
Take away these skills, and the person is no longer functioning as a data scientist, regardless of how many visualisation tools they know.
3) Backend Developer
Primary domain skills might be:
One core programming language (Java, Python, Node.js, Go, etc.) with strong fundamentals.
Backend frameworks (Spring Boot, Django, Express, etc.).
REST APIs, integration patterns and handling authentication, authorisation, etc.
Databases: Designing schemas, writing queries, understanding transactions and indexing.
Without these, it is almost impossible to deliver backend features reliably.
4) Cloud / DevOps Engineer
Primary skills often include:
Deep knowledge of at least one cloud provider (AWS, Azure or GCP).
CI/CD tools (Jenkins, GitLab CI, GitHub Actions, etc.).
Containers and orchestration (Docker, Kubernetes).
Infrastructure as Code (Terraform, CloudFormation) and environment automation.
You can see a pattern: primary skills are “non‑negotiable”. They are the skills companies are directly paying you for.
2. What are secondary technical domain skills?
Secondary technical domain skills are the “supporting cast”. They do not define your title, but they make your primary skills far more powerful. These skills:
Help you work better with other teams (product, business, design, infra).
Allow you to handle end‑to‑end tasks instead of only one narrow piece.
Make you more adaptable when technologies, tools or roles change.
Individually, each secondary skill may not land you a job. Together, they can significantly boost your performance, promotions and long‑term growth.
Examples by role
1) Data Analyst – secondary skills
Basic Python: Automating repetitive reports, cleaning data more efficiently, connecting to APIs.
Data visualisation tools: Power BI, Tableau, Looker, etc., to tell stories with dashboards.
Domain knowledge: Understanding of finance, marketing, operations or HR, so your insights are business‑relevant.
Communication and storytelling: Presenting results in a simple, clear way to non‑technical stakeholders.
A data analyst without SQL struggles to survive; a data analyst without Power BI can still work, but will be less independent and slower.
2) Data Scientist – secondary skills
Big data tools: Knowledge of Spark, Hadoop, or distributed processing platforms.
Data engineering basics: Pipelines, ETL concepts and integration with data warehouses or lakes.
Visualisation: Building simple dashboards so stakeholders can monitor models and metrics.
Business/domain understanding: Knowing how logistics, e‑commerce, banking, healthcare or telecom really work in practice.
These skills help a data scientist move from “I can train models” to “I can build solutions that actually get used in production”.
3) Backend Developer – secondary skills
Basic front‑end: HTML, CSS and a bit of JavaScript/React, so they can understand the full flow.
Cloud basics: How their services run on AWS/Azure/GCP, logging, scaling, monitoring.
API documentation tools: Swagger/OpenAPI, Postman collections, etc.
Security and performance basics: OWASP concepts, caching, rate limiting, etc.
These secondary skills make them a stronger part of a cross‑functional product team.
4) Cloud / DevOps Engineer – secondary skills
Scripting skills (Python/Bash): Writing automation scripts, custom integrations and quick tools.
Monitoring & observability tools: Prometheus, Grafana, ELK stack, CloudWatch, etc.
Basic networking and security: VPCs, subnets, firewalls, IAM, encryption, etc.
Collaboration with developers: Understanding application architecture so infra changes are aligned with product needs.
Again, these are not the core definition of the role, but they make the engineer far more effective.
3. How to clearly separate primary vs secondary skills (with simple examples)
A simple way to think about this distinction is to ask two questions:
“If I remove this skill, can I still do the job at a basic level?”
“Is this the skill recruiters filter for when they search for my role?”
If the answer to both is yes, you are probably looking at a primary skill. If the answer is “it helps, but I’d survive”, that is likely a secondary skill.
Example 1: Early‑career Data Analyst
Primary skills:
SQL
Excel / Google Sheets
Data cleaning and basic statistics
Secondary skills:
Power BI / Tableau
Basic Python
Good slide‑making and presentation skills
Without SQL, they cannot function. Without Power BI, they can still use Excel charts or basic reports, though less efficiently.
Example 2: Mid‑level Data Scientist
Primary skills:
Python
Statistics and ML algorithms
Model evaluation and experimentation (A/B tests)
Secondary skills:
Spark or big data tools
Domain knowledge (e.g., lending risk, fraud detection, supply‑chain optimisation)
Stakeholder communication and storytelling
If you remove Python and ML, the job collapses. If you remove Spark knowledge, they can still work on smaller‑scale problems.
Example 3: DevOps Engineer
Primary skills:
CI/CD pipelines
Containers (Docker)
Kubernetes or another orchestrator
Cloud infrastructure management
Secondary skills:
Basic programming (Python/Go)
Monitoring tools (Prometheus/Grafana)
Security and cost‑optimisation awareness
Here too, the primary skills directly relate to keeping systems running and deployments smooth. Secondary skills help them optimise and collaborate better.
4. Why this distinction matters for your career
Understanding the difference between primary and secondary technical domain skills is not just theory. It can change how you plan your learning, how you position yourself in the market and how you negotiate roles.
a) Focus your learning roadmap
When you are early in your journey, your first priority should be to build and solidify primary skills. That is your entry ticket. Once those are strong enough to clear interviews and handle real projects, you can gradually layer on secondary skills to widen your scope.
For example:
Aspiring data analyst: nail SQL and Excel first, then add Power BI and Python.
Aspiring data scientist: focus on Python, statistics and ML fundamentals first, then learn cloud, MLOps and big data tools.
b) Build a clear, sharp resume
A good resume separates:
“Key technical skills” (primary) – highlighted at the top.
“Additional tools & technologies” (secondary) – shown as breadth.
This helps recruiters quickly see your fit. If your primary skills are buried inside a long list of buzzwords, your profile looks unfocused.
c) Plan your long‑term niche
Over time, many professionals develop a combination like:
2–3 very strong primary skills (deep).
5–8 secondary skills (broad).
That mix makes you both specialised and adaptable, which is exactly what modern tech roles demand.
5. How this relates to data and tech careers in general
Most modern tech roles – especially in data, AI and software – require a blend of both types of skills. Reports from universities, industry blogs and hiring platforms consistently emphasise:
Strong technical foundations (coding, statistics, core tools) as the primary layer.
Complementary skills like visualisation, domain understanding, cloud and communication as the secondary layer that drives real‑world impact.
So, when someone asks, “What are primary and secondary technical domain skills?”, a simple answer is:
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See lessPrimary skills = what you are hired for and what you do most of the time.
Secondary skills = what makes you better at your primary work and more valuable to your team, but not the core definition of your job.
Can anyone help me with genuine UpGrad reviews of there data science course ?
If you are looking for only genuine UpGrad reviews of their data science course (no promotion), the best approach is to look at long, unfiltered student experiences that include both positives and negatives, not just testimonials. Where to find genuine UpGrad reviews On Analytics Jobs, there is a deRead more
If you are looking for only genuine UpGrad reviews of their data science course (no promotion), the best approach is to look at long, unfiltered student experiences that include both positives and negatives, not just testimonials.
Where to find genuine UpGrad reviews
On Analytics Jobs, there is a dedicated UpGrad data science review page that compiles multiple learner stories – from people who got good value to those who felt misled about outcomes and support:
https://analyticsjobs.in/question/is-a-data-science-course-from-upgrad-worth-it/
These reviews talk about:
Structured PG‑style learning with IIIT‑B branding, multiple projects and decent conceptual coverage.
Salary jumps for some learners, including reports of significant percentage hikes after course completion.
Serious complaints from others around aggressive sales, high fees (often in the multiple‑lakh range), batch sizes, mentor quality, and especially disappointment with “placement support” versus what was promised.
Reading those long threads will give you a far more honest picture than any ad: you will see recurring themes, like some learners appreciating the structured content and others feeling the ROI is weak for the price and that job assistance is mostly limited to generic support.
What these reviews suggest in simple words
From genuine learner feedback across platforms (including student posts and independent comments):
UpGrad’s data science programme with IIIT‑B generally scores well on:
Course organisation, recorded + live content, and project‑based learning for beginners and early‑career professionals.
Brand value of IIIT‑B and a formal PG‑style credential on the resume.
Common red flags raised by unhappy learners include:
Very high fees relative to what they felt they received in terms of actual interview calls and final outcomes.
Over‑promising by sales teams versus what the career services team actually delivers after you join.
Feeling “stuck” after paying, with limited flexibility and difficulty getting refunds or escalation.
So, the genuine picture is mixed: UpGrad is not a scam for everyone, but it is also not a magic ticket. It works reasonably well for disciplined learners who mainly want a structured PG‑type journey and are okay treating placements as “support” rather than a guarantee; it is often a poor fit for those expecting assured jobs just because of the fee and branding.
How to use these reviews before deciding
If someone is evaluating UpGrad’s data science course, a practical, no‑promotion checklist would be:
Go through multiple full reviews on the Analytics Jobs UpGrad page and note:
What satisfied learners are praising (structure, projects, mentorship).
What dissatisfied learners are repeatedly complaining about (fees, support, expectations vs reality).
Reach out to a few recent alumni on LinkedIn, specifically those who finished in the last 1–2 years, and ask:
What job role and CTC they are at now.
Whether UpGrad directly contributed to that outcome or they mostly self‑navigated.
Only enrol if:
The fee fits your risk appetite even in a worst‑case scenario (no placement).
You are ready to put in consistent effort on projects, networking and self‑applications alongside whatever UpGrad provides.
Also compare with other top data science options
Finally, UpGrad is just one player in a very crowded market. To make a truly informed choice, it helps to compare its offering with other leading programmes on parameters like curriculum, delivery model, fees and verified reviews.
Analytics Jobs maintains an updated “Best Data Science Course 2025” list where you can explore multiple providers (Learnbay, Great Learning, Scaler, Intellipaat, Imarticus, etc.) along with short overviews and links to detailed review pages:
Best Data Science Course (curated list + links to in‑depth reviews):
https://analyticsjobs.in/best-data-science-course/
Checking this page alongside the UpGrad review thread will give a much clearer sense of whether UpGrad genuinely fits your goals, or whether another programme offers better value for your specific background and budget.
See lessWhat is the most suitable alternative for online data science, Simplilearn (IBM certification), Upgrad (IIITB certification), or Intellipaat IIT Madras data science?
All three options you mentioned – Simplilearn (IBM), UpGrad (IIIT‑B) and Intellipaat (IIT Madras) – can look similar from the outside, but they actually suit slightly different learner profiles. Instead of a one‑word “best”, it is more useful to match each course to the learner’s goals, budget and rRead more
All three options you mentioned – Simplilearn (IBM), UpGrad (IIIT‑B) and Intellipaat (IIT Madras) – can look similar from the outside, but they actually suit slightly different learner profiles. Instead of a one‑word “best”, it is more useful to match each course to the learner’s goals, budget and risk appetite using real reviews.
1. Start with real learner reviews
Before choosing any of the three, it is strongly advised that learners go through genuine, long‑form student experiences rather than just marketing pages. On Analytics Jobs, there are detailed review threads for each of these providers:
Simplilearn Data Science reviews (course structure, IBM branding, pros/cons, complaints and success stories):
https://analyticsjobs.in/question/simplilearn-reviews-data-science-course/
UpGrad Data Science (IIIT‑B) reviews (is it worth the fee, how the PG structure feels, and what students are saying about placements and support):
https://analyticsjobs.in/question/is-a-data-science-course-from-upgrad-worth-it/
Intellipaat + IIT Madras data science reviews (curriculum breakdown, fees, mentorship quality, and mixed feedback on career services):
https://analyticsjobs.in/question/intellipaat-reviews-data-science-course/
These pages compile both positive and negative experiences, so learners can see patterns instead of relying on a few random comments.
2. How each option is positioned
In simple terms, the three programmes are positioned like this:
Simplilearn (IBM‑linked course)
Strong focus on industry‑oriented certificates and co‑branding with IBM for data science and analytics tracks.
Suits learners who want a globally recognised corporate brand tag (IBM) plus a structured, but relatively modular, online programme.
UpGrad (IIIT‑B PG‑style programme)
Usually offered as a long‑form Executive PG / Diploma in Data Science or AI with IIIT Bangalore, with graded assignments, projects and term‑wise structure.
Better fit for working professionals who are ready for a heavier time commitment and higher fees in exchange for a more “PG degree–like” academic signalling on their CV.
Intellipaat – IIT Madras (IITM Pravartak)
Designed as an advanced certification in Data Science / Business Analytics with IIT Madras, with 7–11 months of structured learning, multiple projects and IIT branding.
Targets learners who specifically want the IIT stamp plus a bootcamp‑style journey (projects, case studies, tools) at a comparatively moderate fee versus a full university degree.
So the “most suitable” alternative depends on whether the learner values an IIT/IIIT label, an IBM corporate tag, or a long‑duration PG structure the most.
3. Practical way to choose between them
Instead of asking “Which is best?”, it helps if the learner answers a few questions:
What is my primary goal?
Career switch to data science / analytics (needs strong projects + career support).
Skill up within current role (content quality and flexibility may matter more than brand).
How much can I realistically invest (time + money)?
UpGrad and similar PG‑style programmes usually sit at the higher end of the fee and time spectrum.
Intellipaat IIT Madras and some Simplilearn tracks can be relatively shorter and sometimes more cost‑effective for learners who do not want a full PG journey.
How critical is brand vs actual skills?
IIT Madras / IIIT‑B branding can help with signalling, but outcomes finally depend on projects, networking and how actively the learner uses the career services.
IBM‑branded certificates are useful for demonstrating exposure to certain tools and frameworks but should be combined with a strong project portfolio.
As the Analytics Jobs team, the recommendation to the learner can be:
4. Also check other top data science options
Finally, these three are not the only options in the market. Analytics Jobs maintains an updated list of some of the best data science courses in India for 2025, where learners can compare multiple institutes on curriculum depth, delivery model, fees and verified student feedback in one place:
Best Data Science Course 2025 list (curated and regularly updated by Analytics Jobs):
https://analyticsjobs.in/best-data-science-course/
For anyone still confused between Simplilearn, UpGrad and Intellipaat, it is sensible to keep these three in the shortlist, but also cross‑check 2–3 more options from the “Best Data Science Course 2025” page before enrolling. This broader comparison often gives much better clarity on value for money, pedagogy and placement reality
See lessWhat are the standout features of leading web application performance testing tools, and how can businesses select the most suitable tool for their projects?
Top Performance Testing Tools Performance testing tools help to perform performance testing on the software to check the speed, reliability, response time, resource usage, and other performance metrics under the expected workload. These tools help to validate the software's basic features. We have rRead more
Top Performance Testing Tools
Performance testing tools help to perform performance testing on the software to check the speed, reliability, response time, resource usage, and other performance metrics under the expected workload. These tools help to validate the software’s basic features.
We have researched for you. Here is a list of the best web application performance and load testing tools, with a detailed comparison:
#1) WebLOAD
Enterprise-grade load and performance testing tool for web applications. WebLOAD is a tool of choice for enterprises with heavy user load and complex testing requirements. It allows you to perform load and stress testing on any internet application by generating load from the cloud and on-premises machines.
WebLOAD’s strengths are its flexibility and ease of use – enabling you to quickly define the tests you need with features like DOM-based recording/playback, automatic correlation, and JavaScript scripting language.
The tool provides a clear analysis of your web application performance, pinpointing issues and bottlenecks that may stand in the way of achieving your load and response requirements.
WebLOAD supports hundreds of technologies – from web protocols to enterprise applications and has built-in integration with Jenkins, Selenium and many other tools to enable continuous load testing for DevOps.
#2) LoadNinja
LoadNinja by SmartBear allows you to quickly create scriptless sophisticated load tests, reduces testing time by 50%, replaces load emulators with real browsers, and get actionable, browser-based metrics, all at ninja speed.
You can easily capture client-side interactions, debug in real-time, and identify performance problems immediately. LoadNinja empowers teams to increase their test coverage without sacrificing quality by removing the tedious efforts of dynamic correlation, script translation, and script scrubbing.
With LoadNinja, engineers, testers and product teams can focus more on building apps that scale and focus less on building load testing scripts.
Features:
#3) HeadSpin
HeadSpin offers the industry’s best performance testing capabilities for its users. Users can optimize their digital experience with the performance testing capabilities of the HeadSpin Platform by identifying and resolving performance issues across applications, devices, and networks.
Features:
#4) ReadyAPI Performance
SmartBear offers an all-in-one automated API Testing Platform called ReadyAPI. It contains various tools like Swagger & SwaggerHub, SoapUI NG, ReadyAPI Performance, Secure Pro, ServiceV, and AlertSite.
ReadyAPI Performance is an API tool for load testing. This API testing tool will assure you that your APIs can perform anywhere. It will let you install load agents on any server or cloud as well as on-premise. It provides advanced performance metrics for load test runs.
SoapUI NG is a tool for functional testing and you can use these functional testing use cases designed in the SOAPUI for performance testing.
This load testing tool will help you with testing the speed, scalability, and performance of the APIs, Servers, and Network Resources. It has features of flexible load generation, parallel API load tests, server monitoring, and pre-built load templates.
#5) LoadView
LoadView is a fully managed, on-demand load testing tool that allows complete hassle-free load and stress testing.
Unlike many other load testing tools, LoadView performs testing in real browsers (not headless phantom browsers), which provides extremely accurate data, closely emulating real users. You only pay for what you use and no contracts are required. LoadView is 100% cloud-based, scalable, and can be deployed in minutes.
Advanced Load Testing Features include Point and Click Scripting, Global Cloud-Based Infrastructure, Real Browser Testing
#6) Keysight’s Eggplant
Keysight’s Eggplant Software is an open, extensible, and multi-protocol performance testing solution. It is designed for new challenges. It performs end-to-end testing and can test anything and everything. It addresses technology glitches.
Eggplant Software provides the benefits of testing faster & efficiently, reducing IT costs, automating repetitive tasks, performing test maintenance at a scale, and reducing time-to-market.
Features:
#7) Apache JMeter
Open source load testing tool: It is a Java platform application. It is mainly considered as a performance testing tool and it can also be integrated with the test plan. In addition to the load test plan, you can also create a functional test plan.
This tool has the capacity to be loaded into a server or network so as to check on its performance and analyze its working under different conditions. Initially, it was introduced to test web applications, but later its scope had widened.
It is of great use in testing the functional performance of resources such as Servlets, Perl Scripts and JAVA objects. Need JVM 1.4 or higher to run.
System Requirements: It works under Unix and Windows OS
Official Website: Apache JMeter
#8) Micro Focus LoadRunner
This is a Micro Focus product which can be used as a Performance Testing tool. This can be bought as a Micro Focus product from its Micro Focus software division. Also, it is very much useful in understanding and determining the performance and outcome of the system when there is an actual load.
One of the key attractive features of this testing tool is that it can create and handle thousands of users at the same time.
This tool enables you to gather all the required information with respect to the performance and is also based on the infrastructure. LoadRunner comprises of different tools – namely, Virtual User Generator, Controller, Load Generator and Analysis.
System Requirements: Microsoft Windows and Linux are the favorable OS for this measuring tool.
#9) Rational Performance Tester
Rational performance tester is an automated performance testing tool that can be used for a web application or a server-based application where the process of input and output is involved. This tool creates a demo of the original transaction process between the user and the web service.
By the end of it, all the statistical information is gathered and they are analyzed to increase efficiency. Any leakage on the website or the server can be identified and rectified immediately with the help of this tool.
This tool can be the best option for building an effective and error-free cloud computing service. This Rational Performance tester was developed by IBM (Rational software division). They have come up with many versions of this automated testing tool.
System Requirement: Microsoft Windows and Linux AIX are good enough for this performance testing tool.
#10) NeoLoad
NeoLoad is the most automated performance testing platform for enterprise organizations that continuously test applications and APIs. NeoLoad provides testers and developers automatic test design and maintenance, the most realistic simulation of user behavior, fast root cause analysis, and built-in integrations with the entire SDLC toolchain.
NeoLoad lets you reuse and share test assets and results from functional testing tools to analytics and metrics from APM tools. NeoLoad supports a full range of mobile, web, and packaged applications, like SAP, to cover all testing needs.
Continuously schedule, manage and share test resources and results across the organization to ensure application performance.
System Requirements: This tool is compatible with operating systems like Microsoft Windows, Linux, and Solaris.
#11) LoadComplete
Easy and affordable performance testing tool. LoadComplete enables you to create and execute realistic load tests for websites and web apps. It automates creating realistic load tests by recording user interactions and simulating these actions with hundreds of virtual users either from your local computers or from the cloud.
LoadComplete helps you check your web server’s performance under a massive load, determine its robustness and estimate its scalability. It also provides detailed metrics and reports that help you gain in-depth insights into infrastructure performance, application behavior, and end-user experience.
System requirements: This tool works on 64-bit operating systems such as Windows XP Professional and Windows 7 or later.
#12) WAPT
Performance Testing tool for websites and intranet applications: WAPT refers to the Web Application Performance tool. These are the scales or analyzing tools for measuring the performance and output of any web application or web related interfaces.
These tools help us to measure the performance of any web services, web applications or any other web interfaces. With this tool, you have the advantage of testing the web application performance under different environments and different load conditions.
WAPT provides detailed information about virtual users and their output to its users during load testing. This is considered to be the most cost-effective tool for analyzing the performance of web services.
The WAPT tool can test the web application on its compatibility with the browser and operating system. It is also used for testing the compatibility with the windows application in certain cases.
WAPT System Requirement: Windows OS is required for this testing tool.
#13) Loadster
Loadster is a desktop-based advanced HTTP load testing tool. The web browser can be used to record the scripts which are easy to use and record. Using the GUI you can modify the basic script with dynamic variables to validate the response.
With control over network bandwidth, you can simulate a large virtual user base for your application stress tests.
After the test, an executed HTML report is generated for analysis. This tool is the best way to identify the performance bottlenecks in your application.
Loadster System Requirements: Windows 7/Vista/XP
#14) k6
k6 is a modern open-source load testing tool that provides an outstanding developer experience to test the performance of APIs and websites. It is a feature-rich and easy to use CLI tool with test cases written in ES5.1 JavaScript and support for HTTP/1.1, HTTP/2, and WebSocket protocols.
“Like Unit testing, for Performance” – is the motto of k6. It provides native Pass/Fail behavior for easy automation and integration into CI pipelines. Additionally, the community has built a browser recorder and converters (JMeter, Postman, Swagger/OpenAPI) to facilitate the test creation process.
k6 runs on Windows, Linux, and Mac OS.
#15) Testing Anywhere
Testing Anywhere is an automated testing tool that can be employed for testing the performance of any website, web application or any other objects. Many developers and testers make use of this tool to find out the bottlenecks in their web applications and rectify them accordingly.
It is a powerful tool that can test any application automatically. This testing tool comes along with a built-in editor which allows the users to edit the testing criteria according to their needs.
Testing Anywhere tool involves 5 simple steps to create a test. They are object recorder, advanced web recorder, SMART test recorder, Image recognition, and Editor with 385+ comments. This testing software was originally developed by San Jose-based Automation Anywhere Inc. Today, there are more than 25000 users for this product.
Conclusion:
In conclusion, when it comes to performance testing tools for web applications, there are several top contenders to consider. Each tool offers unique features and benefits, ranging from enterprise-grade solutions like WebLOAD and LoadNinja’s scriptless testing to innovative platforms like HeadSpin and ReadyAPI Performance. Additionally, open-source options like Apache JMeter and modern tools like k6 provide flexible and developer-friendly alternatives. Ultimately, the choice depends on specific project requirements and preferences, ensuring optimal performance and user satisfaction.
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