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What is Sanity Testing

What is Sanity Testing 

by Samiksha chhallani Blog

What is Sanity Testing ? 

Introduction

Software testing ensures the quality and reliability of applications. Among various testing methods, Sanity Testing is essential for verifying recent code changes before deeper testing. This article covers its purpose, benefits, and differences from other testing types.

What is Sanity Testing?

Sanity Testing is a software testing method that checks specific functionalities after minor updates or bug fixes. The goal is to confirm that recent changes work correctly and haven’t caused new issues. It is usually performed manually before extensive regression testing.

This type of testing is focused, quick, and unscripted, targeting affected areas without a full system check.

Key Characteristics

  1. Targeted Approach – Tests only modified or impacted software components.
  2. Quick Validation – Ensures basic functionality remains intact in a short time.
  3. Minimal Documentation – No need for extensive test case preparation.
  4. Post-Fix Testing – Conducted after resolving specific issues.
  5. Decision-Making Tool – Helps determine if deeper testing is necessary.

Why is Sanity Testing Important?

 

This testing process plays a crucial role by:

  • Preventing Unnecessary Effort – Identifies major issues early.
  • Ensuring Stability – Confirms that updates don’t introduce new problems.
  • Speeding Up Testing – Saves time by focusing only on affected features.
  • Improving Software Quality – Provides a quick assessment of key functions.
  • Supporting Agile Development – Fits well in fast-paced CI/CD workflows.

 

How to Perform Sanity Testing?

Step 1: Identify Affected Areas

Determine which features were changed due to updates or bug fixes.

Step 2: Verify Key Functionalities

Test impacted sections to ensure proper functionality.

Step 3: Check Integration Points

Ensure related modules function correctly with the changes.

Step 4: Validate Fixes

Confirm that reported defects are successfully addressed.

Step 5: Decide Next Steps

If the testing passes, proceed with full regression testing. Otherwise, return for debugging.

Real-World Examples of Sanity Testing

Example 1: E-Commerce Website

A popular e-commerce platform fixes a bug in its checkout process where discount codes were not applying correctly. After the fix, the QA team conducts sanity testing to ensure:

  • Discount codes now apply as expected.

  • The total price calculation remains accurate.

  • No new issues appear in payment processing.

If sanity testing confirms these fixes, the team proceeds with full regression testing.

Example 2: Mobile Banking App

A mobile banking app updates its login security to support biometric authentication. Before running full-scale tests, the QA team performs sanity testing to:

  • Verify that biometric login works correctly.

  • Ensure traditional username/password login is unaffected.

  • Confirm the login process doesn’t introduce new crashes.

Example 3: SaaS Application

A cloud-based SaaS company fixes a reported issue where users couldn’t upload files in certain formats. Sanity testing is performed to:

  • Check that the file upload feature works for all supported formats.

  • Ensure no new issues arise in file processing.

  • Verify that files are stored and accessible as expected.

Sanity Testing vs. Smoke Testing

These two testing types often get confused. Here’s how they differ:

FeatureSanity TestingSmoke Testing
PurposeValidates specific changesEnsures application stability
ScopeLimited to modified areasCovers core functionalities
Execution TimeShortLonger
Test CasesUnstructuredMay include predefined cases
Performed ByTesters or developersTesters

Both are essential and serve distinct roles in software testing.

Benefits of Sanity Testing

  1. Fast Issue Detection – Identifies problems quickly.
  2. Efficient Resource Use – Saves effort by focusing on critical areas.
  3. Prevents Regression Failures – Avoids unnecessary deep testing if issues exist.
  4. Enhances Software Stability – Ensures recent changes don’t break functionality.
  5. Supports Agile and CI/CD – Facilitates rapid testing cycles in modern development.

Conclusion

Sanity testing is a crucial technique ensuring that recent software changes do not introduce new issues. It is quick, targeted, and helps maintain software reliability. By incorporating it into your development and QA workflow, you can improve efficiency and enhance software quality.

Adding this step to your testing strategy is particularly useful in agile environments, helping teams deliver stable and high-performing applications.

what is neural network

What is Neural Network

by Samiksha chhallani Blog

WHAT IS NEURAL NETWORK

what is neural network

What is a Neural Network?

In today’s digital age, artificial intelligence (AI) is transforming industries, and one of the key technologies behind this revolution is neural networks. From self-driving cars to voice assistants and recommendation systems, neural networks play a crucial role in enabling machines to mimic human intelligence. But what exactly is a neural network, and how does it work? This article provides an easy-to-understand introduction to neural networks, their structure, types, and applications.

 

Understanding AI Neural System

A AI Neural System is a computational model inspired by the human brain. It consists of layers of interconnected nodes, or neurons, that process information. These networks are a subset of machine learning and are widely used in deep learning, a branch of AI focused on analyzing large datasets to make predictions and decisions.

The fundamental goal of a neural network is to recognize patterns and relationships in data. By doing so, it can perform tasks such as image and speech recognition, natural language processing, and even playing complex games like chess and Go.

 

Structure of AI Neural System

A neural network is typically composed of three main layers:

  1. Input Layer: This layer receives raw data in the form of numbers, images, or text. Each neuron in this layer represents a feature of the input data.
  2. Hidden Layers: These layers process and analyze the input data. The neurons in hidden layers apply mathematical functions to identify patterns and relationships.
  3. Output Layer: This layer produces the final result, such as classifying an image, predicting a value, or generating text.

Each neuron in a neural network is connected to others through weights, which determine the importance of a connection. These weights are adjusted during training to improve accuracy.

 

How Does a AI Neural System Work?

The working of a neural network can be broken down into three key steps:

  1. Forward Propagation: Data flows from the input layer through the hidden layers to generate an output. Each neuron applies an activation function (like ReLU or Sigmoid) to determine if it should pass information forward.
  2. Loss Calculation: The predicted output is compared with the actual output, and an error (loss) is calculated using a loss function.
  3. Backpropagation & Optimization: The network adjusts the weights using an optimization algorithm (such as Gradient Descent) to minimize the loss and improve accuracy.

This process is repeated multiple times until the neural network learns to make accurate predictions.

 

Types of Neural Networks

Neural networks come in different architectures, each suited for specific tasks:

1. Feedforward Neural Network (FNN)
  • The simplest type of neural network where information moves in one direction (from input to output).
  • Used in tasks like image recognition and fraud detection.
2. Convolutional Neural Network (CNN)
  • Specialized for processing image and video data.
  • Uses convolutional layers to detect patterns such as edges, textures, and shapes.
  • Applied in facial recognition, medical image analysis, and autonomous vehicles.
3. Recurrent Neural Network (RNN)
  • Designed for sequential data like text, speech, and time-series analysis.
  • Uses memory cells (such as Long Short-Term Memory – LSTM) to remember past inputs.
  • Used in chatbots, speech recognition, and stock market predictions.
4. Generative Adversarial Network (GAN)
  • Consists of two neural networks: a generator (creates data) and a discriminator (evaluates data).
  • Used in generating realistic images, deepfake videos, and AI art.
5. Radial Basis Function Network (RBFN)
  • Used in function approximation and classification problems.
  • Employs radial basis functions for decision making.
 

Applications of AI Neural System

Neural networks are transforming various industries with real-world applications, including:

  • Healthcare: Disease diagnosis, medical imaging, and drug discovery.
  • Finance: Fraud detection, algorithmic trading, and credit risk assessment.
  • E-commerce: Personalized recommendations, chatbots, and sentiment analysis.
  • Automotive: Autonomous driving, traffic prediction, and vehicle safety systems.
  • Gaming: AI-powered opponents, game development, and real-time rendering.
  • Natural Language Processing (NLP): Voice assistants like Alexa and Siri, language translation, and text summarization.
 

Advantages of AI Neural System

  • High Accuracy: Capable of learning complex patterns from large datasets.
  • Automation: Reduces human intervention in tasks like image recognition and speech processing.
  • Scalability: Can handle massive amounts of data efficiently.
  • Self-learning: Improves performance over time through training.
 

Challenges and Limitations

Despite their advantages, neural networks have some challenges:

  • Data Requirements: Require large datasets to achieve high accuracy.
  • Computational Power: Need powerful GPUs or cloud computing for training.
  • Black Box Nature: Difficult to interpret how decisions are made.
  • Overfitting: May memorize data instead of generalizing well to new inputs.
 

Future of AI Neural System

The future of neural networks looks promising with advancements in AI research. Innovations like transformers, neuromorphic computing, and quantum AI are pushing the boundaries of what neural networks can achieve. As neural networks continue to evolve, they will drive breakthroughs in robotics, personalized medicine, and real-time AI interactions.


What is data normalization

by Samiksha chhallani Blog

What is Data Normalization

What is data normalization

Introduction

In the world of data management and database design, data normalization plays a crucial role in ensuring efficiency, consistency, and accuracy. Whether you are a database administrator, data analyst, or software developer, understanding data normalization is essential for optimizing data storage and improving database performance. In this article, we will explore what data normalization is, why it is important, its benefits, and the various normalization forms used in database design.

What is Data Normalization?

Data normalization is the process of organizing data within a database to minimize redundancy and improve data integrity. It involves structuring a relational database in a way that eliminates duplicate data and ensures that data dependencies are logical. By applying normalization techniques, databases become more efficient, scalable, and easier to maintain.

Normalization is achieved through a series of rules called normal forms. Each normal form builds upon the previous one, progressively refining the database structure to improve its efficiency and eliminate anomalies such as insertion, update, and deletion inconsistencies.

Why is Data Normalization Important?

Data normalization is essential for several reasons, including:

  1. Reducing Data Redundancy – Normalization eliminates duplicate data by ensuring that information is stored only once, thereby reducing storage costs and improving data consistency.
  2. Enhancing Data Integrity – By maintaining proper relationships between data elements, normalization minimizes the risk of inconsistent or conflicting data.
  3. Improving Database Performance – Well-structured databases enable faster query execution, as data is stored in a more organized manner.
  4. Simplifying Data Management – Normalized databases are easier to update and maintain, reducing the likelihood of data anomalies.
  5. Facilitating Scalability – A normalized database structure makes it easier to expand and adapt to changing business needs.

The Different Normal Forms

Normalization is implemented through a series of normal forms, each aimed at improving the structure of the database. The most commonly used normal forms are:

1. First Normal Form (1NF)

A table is in First Normal Form (1NF) if:

  • Each column contains atomic (indivisible) values.
  • Each row has a unique identifier (primary key).
  • There are no duplicate columns.
  • Each column contains values of a single type.

Example: Before 1NF:

StudentIDStudentNameCourses
101AliceMath, Science
102BobHistory, English

After 1NF:

StudentIDStudentNameCourse
101AliceMath
101AliceScience
102BobHistory
102BobEnglish

 

2. Second Normal Form (2NF)

A table is in Second Normal Form (2NF) if:

  • It is already in 1NF.
  • All non-key attributes are fully dependent on the primary key.

Example: Before 2NF:

OrderIDProductIDProductNameCustomerID
201P001LaptopC101
202P002MouseC102

In the above table, ProductName depends only on ProductID, not on OrderID. To achieve 2NF, we separate product details into another table.

After 2NF: Orders Table:

OrderIDProductIDCustomerID
201P001C101
202P002C102

Products Table:

ProductIDProductName
P001Laptop
P002Mouse

3. Third Normal Form (3NF)

A table is in Third Normal Form (3NF) if:

  • It is in 2NF.
  • There are no transitive dependencies (i.e., non-key attributes should not depend on other non-key attributes).

Example: Before 3NF:

EmployeeIDEmployeeNameDepartmentDepartmentLocation
501JohnHRNew York
502SarahITSan Francisco

Here, DepartmentLocation depends on Department, not directly on EmployeeID. To achieve 3NF, we split the table:

Employees Table:

EmployeeIDEmployeeNameDepartment
501JohnHR
502SarahIT

Departments Table:

DepartmentDepartmentLocation
HRNew York
ITSan Francisco

Higher Normal Forms

Beyond 3NF, there are additional normal forms such as:

  • Boyce-Codd Normal Form (BCNF) – A stricter version of 3NF, eliminating cases where a candidate key is still dependent on another non-prime attribute.
  • Fourth Normal Form (4NF) – Removes multi-valued dependencies.
  • Fifth Normal Form (5NF) – Addresses join dependencies and ensures data reconstruction without anomalies.

Conclusion

Data normalization is a fundamental concept in database design that enhances data integrity, reduces redundancy, and improves overall database efficiency. By applying normalization techniques, organizations can ensure accurate data storage, improve system performance, and streamline data management. Understanding and implementing the right level of normalization is key to designing an optimized and scalable database system.

What is Boosting in Machine Learning

by Samiksha chhallani Blog

What is Boosting in Machine Learning ?

In machine learning, achieving high accuracy and model performance is crucial. While there are many ways to improve the performance of machine learning models, one of the most effective techniques is boosting. Boosting is an ensemble learning technique that combines multiple weak learners into a strong learner to improve predictive accuracy. But what exactly does boosting mean in the context of machine learning? Let’s explore this powerful technique and how it can help you create better machine learning models.

 

What-is-boosting-in-machine-learning

Boosting is an ensemble learning technique that combines the predictions of several models, called weak learners, to create a single, strong predictive model. The primary objective of boosting is to convert weak learners, which are typically simple models like decision trees, into a highly accurate predictive model by combining their outputs. Unlike other ensemble methods such as bagging (which trains multiple models independently), boosting builds models sequentially. Each subsequent model attempts to correct the errors made by the previous models, allowing the overall model to focus on the most challenging instances.

 

Key Features of Boosting

Before diving into the process of how boosting works, let’s review some key features that define this technique:

  1. Weak Learners: A weak learner is any model that performs slightly better than random guessing. In boosting, decision trees with limited depth (often referred to as decision stumps) are commonly used as weak learners. Despite being weak individually, when combined, these models can make accurate predictions.

  2. Sequential Learning: Boosting algorithms build models one after another in a sequential manner. Each new model corrects the mistakes of the previous model. This is in contrast to bagging algorithms (like Random Forest) where all models are built in parallel.

  3. Weighting Misclassified Instances: In boosting, the instances that are misclassified by previous models are given higher weights, meaning that the next model in the sequence will focus more on those harder-to-classify instances. This helps improve the overall performance of the model.

  4. Final Prediction: After all models have been trained, they are combined to make a final prediction. Depending on the boosting algorithm, this could involve a weighted average of the predictions (for regression tasks) or a majority vote (for classification tasks).

How Does Boosting Work?

The boosting process involves several iterations where weak learners are trained and combined to improve model accuracy. Let’s go through the process step by step:

  1. Start with a Simple Model: The first model (often a weak learner, like a shallow decision tree) is trained on the dataset. This model will likely make several mistakes, as it is a simple model.

  2. Focus on Mistakes: After the first model makes predictions, boosting algorithms will focus on the data points that were misclassified or have large prediction errors. These points will be given higher weights in the next model’s training process, signaling to the new model that these instances need more attention.

  3. Train the Next Model: The second model is trained to correct the errors of the first model, focusing on the misclassified points. By doing this, the model is iteratively refining the predictions and focusing on the difficult examples.

  4. Repeat the Process: This process of training models to correct the errors of previous ones continues for several iterations. Each model adds value by improving the overall predictions made by the ensemble.

  5. Combine the Models: After all models have been trained, their predictions are combined to make the final prediction. In classification tasks, the final prediction may be determined by a majority vote (the most frequent prediction across all models), while in regression tasks, it could be a weighted average of the predictions from all models.

Common Boosting Algorithms

Several boosting algorithms have been developed over the years. Here are some of the most widely used ones:

1. AdaBoost (Adaptive Boosting)

AdaBoost is one of the earliest and most popular boosting algorithms. It works by adjusting the weights of misclassified instances, so that the next model in the sequence pays more attention to them. AdaBoost is typically used with decision trees as weak learners, but it can also work with other types of models. The key features of AdaBoost are:

  • It starts with equal weights for all training instances.
  • After each iteration, the weights of misclassified instances are increased, forcing the next model to focus on those harder-to-classify points.
  • The final prediction is a weighted sum of the individual model predictions.

Pros: AdaBoost is simple to implement and effective, even for large datasets. It is also less prone to overfitting than some other models.

Cons: AdaBoost can be sensitive to noisy data and outliers, as these can heavily influence the final model.

 

2. Gradient Boosting

Gradient Boosting is another popular boosting algorithm that works by optimizing a loss function through a series of iterations. Unlike AdaBoost, which uses reweighted instances to focus on misclassified data, Gradient Boosting builds each new model to minimize the residual error (the difference between the predicted and actual values). This is done through gradient descent.

In Gradient Boosting:

  • The algorithm calculates the gradient of the loss function (i.e., the error) and uses this to train the next model.
  • Models are added iteratively to minimize the residual errors of previous models.
  • Final predictions are made by combining the predictions of all models.

Pros: Gradient Boosting can handle complex relationships and produce high-quality models with high accuracy. It’s effective for both regression and classification tasks.

Cons: Gradient Boosting can be slow to train and may be prone to overfitting if not properly tuned.

 

3. XGBoost (Extreme Gradient Boosting)

XGBoost is an optimized implementation of Gradient Boosting, designed to be faster and more efficient. It is highly popular in machine learning competitions due to its speed and accuracy.

Key features of XGBoost include:

  • Regularization: XGBoost incorporates regularization techniques to avoid overfitting, making it more robust.
  • Parallelization: XGBoost can train models much faster than traditional Gradient Boosting by parallelizing the process.
  • Handling Missing Data: XGBoost can handle missing data, making it more flexible in real-world applications.

Pros: XGBoost is highly efficient, performs well on structured datasets, and has a range of hyperparameters to fine-tune for optimal performance.

Cons: XGBoost requires careful hyperparameter tuning and can be computationally expensive for large datasets.

 

Why is Boosting Important?

Boosting is an essential technique in machine learning because it significantly enhances the performance of weak models. Here are some reasons why boosting is widely used:

  • Increased Accuracy: By combining multiple weak models, boosting creates a stronger model that can make more accurate predictions, especially on difficult datasets.
  • Better Handling of Imbalanced Datasets: Boosting can focus on harder-to-classify instances, which helps improve accuracy when dealing with imbalanced datasets.
  • Effective for Complex Problems: Boosting is effective at learning complex patterns and relationships in the data, making it ideal for challenging problems.
Data-Lake

What is Data Lake? 6 Powerful Benefits & Best Practices

by Samiksha chhallani Blog

What is Data Lake?

A Data Lake is a centralized storage system that holds structured, semi-structured, and unstructured data at any scale. Unlike traditional databases, Data Lakes allow raw data to be stored without the need for prior organization.

 

🔹 Why it is Important?

✔️ Handles structured, semi-structured, and unstructured data
✔️ Supports advanced analytics, AI, and ML
✔️ Scalable and cost-effective storage solution
✔️ Enables real-time data processing

Key Components of a Cloud-based Data Lake Architecture

A Data Lake is built using multiple components to ensure efficient data storage, processing, and analysis.

1️⃣ Data Ingestion Layer 🏗️

This layer is responsible for importing data from various sources, including:
✅ Databases (SQL, NoSQL)
✅ APIs & Web Services
✅ Streaming Data (Kafka, Apache Flink)
✅ IoT & Sensor Data

2️⃣ Storage Layer 💾

The storage layer is where data is stored in its raw form. Popular storage options include:
✅ Cloud Storage – AWS S3, Azure Data Lake, Google Cloud Storage
✅ On-Premises Storage – Hadoop Distributed File System (HDFS)

3️⃣ Processing & Analytics Layer 📊

This layer enables data transformation and analysis through:
✅ Big Data Processing (Apache Spark, Hadoop, Presto)
✅ Machine Learning & AI (TensorFlow, PyTorch, AWS SageMaker)
✅ SQL Queries & BI Tools (Power BI, Tableau, Looker)

4️⃣ Security & Governance Layer 🔒

This layer ensures data security, compliance, and governance using:
✅ Role-Based Access Control (RBAC)
✅ Data Encryption & Masking
✅ Data Cataloging & Metadata Management

5️⃣ Consumption Layer 🔍

This layer allows users to access and utilize data through:
✅ APIs & SDKs for developers
✅ Business Intelligence (BI) dashboards
✅ Machine Learning models for predictions

 Data Lake vs. Data Warehouse: What’s the Difference?

FeatureData Lake 🏞️Data Warehouse 🏛️
Data TypeRaw, unstructured, semi-structuredProcessed, structured
ProcessingAI, ML, real-time & batch analyticsBusiness Intelligence (BI), reporting
SchemaSchema-on-read (defined at query time)Schema-on-write (structured before storage)
Storage CostLower (uses scalable cloud storage)Higher (structured storage requires indexing)
Best ForBig data, AI, machine learning, IoTFinancial reports, KPI tracking, business dashboards
stp

 Top Benefits of a Enterprise Data Lake

✅ Stores All Data Types – Structured, semi-structured, and unstructured.
✅ Scalability – Can handle petabytes of data efficiently.
✅ Flexibility – No need to structure data before storage.
✅ Cost-Effective – Uses low-cost cloud storage (AWS S3, Azure Blob Storage).
✅ Advanced Analytics – AI, ML, and Big Data processing capabilities.
✅ Real-Time & Batch Processing – Supports fast decision-making.

Common Challenges in Managing a Big Data Lake

🚨 Data Swamp Problem – If not properly managed, a Data Lake can become a “data swamp” (unorganized and unusable).
✔ Solution: Implement metadata tagging and data governance policies.

🚨 Security Risks – Storing raw data without security measures can lead to breaches and compliance violations.
✔ Solution: Use role-based access control (RBAC), encryption, and logging.

🚨 Slow Query Performance – Large volumes of raw data can slow down analytics.
✔ Solution: Use indexing, caching, and data partitioning for optimization.

 Popular Data Lake Platforms & Tools

🌐 Cloud-Based Data Lakes

✅ AWS Data Lake (Amazon S3 + AWS Glue) – Scalable, AI-ready.
✅ Azure Data Lake Storage (ADLS) – Microsoft ecosystem integration.
✅ Google Cloud Storage (GCS) + BigQuery – Fast SQL-based analytics.

💻 Open-Source Data Lake Solutions

✅ Apache Hadoop & Spark – Distributed storage & big data processing.
✅ Delta Lake – Optimized data lakehouse architecture.

 Real-World Use Cases of Data Lakes

💡 E-Commerce – Customer behavior analysis, recommendation systems.
💡 Healthcare – Medical imaging, genomics research, AI-driven diagnostics.
💡 Finance – Fraud detection, real-time transaction monitoring.
💡 Manufacturing – IoT-based predictive maintenance.
💡 Retail & Supply Chain – Demand forecasting, inventory optimization.

 Best Practices for Managing a Data Lake Storage

✔ Define Data Governance Policies – Helps prevent data swamps.
✔ Implement Data Security – Use encryption & role-based access control.
✔ Optimize Query Performance – Use indexing, caching, and partitioning.
✔ Ensure Data Quality – Maintain metadata tagging and validation rules.
✔ Use Cost Optimization Strategies – Store rarely accessed data in lower-cost tiers.

It’s Future: What’s Next?

🔮 Data Lakehouses – A hybrid model combining Data Lake & Data Warehouse capabilities.
🔮 AI-Powered Data Lakes – Using machine learning for automatic data classification.
🔮 Real-Time Data Lakes – Enabling instant data processing & decision-making.
🔮 Edge Data Lakes – Storing & processing IoT data closer to the source.