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Choosing the Right Automation Testing Tool: A Guide

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Testing is an essential step in any Software Development Life Cycle (SDLC) aimed at confirming the product’s quality and eliminating errors to ensure the final product is free from defects. There are two testing methods: Manual testing and Automation testing. Manual testing involves testers meticulously following a step-by-step process to identify defects in the developed product. 

However, this approach often leaves little room for exploratory testing and reporting, particularly when Agile processes show no sign of decline. In contrast, Automation testing relies on predefined actions, produces consistent results, and generates intelligent reports, offering greater efficiency for both the organization and testers.

In this blog, we will delve into the critical decision of choosing the right automation tool. With a plethora of options available in the market, selecting the most suitable tool can be a challenging yet crucial task. We’ll explore the factors to consider and provide insights into some of the best automation tools that cater to web and app testing needs. Whether you’re a seasoned QA professional or a newcomer to the world of test automation, this blog will help you make informed choices, enhancing the effectiveness of your testing efforts. But before we go ahead, let us first understand the basics of automation tools. 

Why do we need automation tools?

Automation testing improves the efficiency of testing, ensures accurate results, and expedites the execution speed of time-consuming and repetitive manual tasks. The testing process in the SDLC gets completed with minimal human intervention which leads to faster sharing of results when the build is shared.

What are the benefits of automation?

Automated testing has benefits like higher reliability, availability, productivity, reduced operating costs, and increased performance.

How to choose the right automation tool?

There are many automation testing tools in the market and the new tool gets launched every year, some are open source and some are charged. How to choose the right tool for your project? Below are some tips that can help you make informed decisions:

  • Determine the scenarios to be automated.
  • Evaluate and investigate the automation options that suit you the best as per your budget and requirements.
  • Choose a couple of tools that suit your requirements.
  • Conduct test trials and choose the best one.
  • Share the information with the stakeholders and get their consent.
  • The selection and utilization of the most suitable tool depend on the project’s specific requirements, as there is no universal “best” tool for all projects.

The below-listed tools are some of the best automation tools that can be used for both web and app testing.

Popular tools in the market
  • Appium

Appium is a common open-source framework and a cross-platform tool that executes the test scripts in multiple platforms like Android, iOS, and Windows using the same API. Additionally, Appium provides support for multiple programming languages and automation testing frameworks, such as Java, JavaScript, Python, PHP, C#, and more.

Using Appium, you can conduct app test automation for the following :

  • Native mobile apps: These apps are written using Android, iOS, and Windows SDKs installed on the device.
  • Mobile web apps: Web applications can be opened in mobile web browsers such as Chrome and Safari. Additionally, you can access mobile web applications by utilizing in-app browsers on both Android and iOS platforms.
  • Hybrid apps: These applications are constructed with a wrapper around a “webview,” which is a native control enabling interaction with web content. You have the option to access hybrid apps either through a physical device or via a browser URL.

KATALON

Katalon Platform is an automation testing software developed by Katalon, Inc. This software is built on top of the open-source automation frameworks Selenium, and Appium with a specialized IDE interface for web, API, mobile, and desktop application testing.

Katalon Studio is a low-code platform that has all-in-one for automation testing for web, API, mobile, and desktop (Windows). This tool has the following features that can help in speeding up the QA: 

  • Powerful Record and Playback: This is the simplest method for generating test cases in automation testing. It records the actions that you perform and converts them into an executable code in the back end. This functionality is particularly useful when the process of identifying objects is laborious and challenging.
  • Behavior-Driven Development (BDD): BDD is a testing methodology widely used in many agile teams. Katalon Studio supports Cucumber Runner for BDD and seamless integration with Jira.
  • Smart XPath: When there is an update in the application, it is difficult to fix the element location. Katalon Smart XPath leverages AI to automatically locate web and app elements, making it easier for you to adapt to the frequent changes that accompany each new release.
  • Smart Wait: This is a newly introduced feature in Katalon Studio 7 that handles the Wait issues in Selenium. With this feature, Katalon Studio will automatically pause until all front-end processes of a web page have been completed before proceeding to the next steps. This ensures uniform execution outcomes and helps QA engineers save considerable time by avoiding unnecessary investigations into false alarms.
  • Image-based Recognition: This is considered a breakthrough feature in test automation and is under development. In its initial release, end-users will be able to engage with application elements using keywords, eliminating the need to write scripts for element location.
  • Dual-editor Interface: Katalon Studio offers manual and scripting options allowing automation beginners to commence automation immediately, while also providing automation experts with the capability to create advanced test scripts.

Selenium 

Selenium is an open-source framework for a range of tools and libraries that aims at supporting browser automation. It also provides a playback tool for authoring functional tests across most modern web browsers, without the need to learn a test scripting language.

Benefits of Using Selenium:

  • The Selenium test script can be written in different programming languages like Java, C#, Python, Ruby, PHP, Perl, and JavaScript.
  • Selenium offers record and playback features with its browser add-on called Selenium IDE
  • The powerful Selenium WebDriver helps you create the most complex and advanced automation scripts.

TestComplete 

TestComplete is a GUI-based test automation tool that can be utilized for most desktop, web, and mobile applications. This tool can be used by both technical and non-technical users.

Features:

  • Code or codeless test cases are created using record & playback, or script in JavaScript, Python, and VBScript.
  • Utilizes ML and AI for object recognition.
  • Supports testing enterprise applications such as SAP, Oracle EBS, and Salesforce.
  • Parallel execution of UI tests in the cloud or local machine. 
  • Integrates completely with commonly used tools such as CI/CD, test management, issue tracking, and version control, giving you a complete testing lifecycle.

Ranorex Studio

Ranorex Studio is used by many enterprises worldwide as an all-in-one tool for testing desktop, online, and mobile applications. 

Features:

  • This code is easy to use for beginners but very productive for automation QA with a complete IDE and a codeless click-and-go interface. 
  • Reliable Object Identification is possible for web items with modifying IDs. It supports test maintenance by sharing the object repository and code modules.
  • Customized test reports can be created with the built-in Selenium WebDriver for parallel or distributed test case execution.
  • The test execution is video recorded which helps to debug the failure test cases without re-running. Also supports commonly used tools like Jenkins, Jira, TestRail, Git, Travis CI, etc.

Kobiton

Kobiton is an AI-driven scriptless automation tool that ensures comprehensive quality and release at speed. this tool easily automates Functional, Performance, Visual, and Compatibility tests.

  • This testing tool offers codeless capture and runnable automation.
  • It creates scriptless Appium code 
  • AI-assisted correction
  • Automated debug detection
  • Performance Testing
  • Visual Validation & UX Testing
  • In-depth session exploration and reporting
  • Seamless CI/CD integrations
  • Unlimited user policy with Access to Real devices in the cloud or via on-premises set-ups.

ACCELQ

ACCELQ is also one of the popular AI-powered Codeless Test Automation platforms that runs on the cloud which seamlessly automates Web UI, API, Mobile, and Desktop. Many customers have adopted this tool to speed up their automation as it does 3 times faster automation development and reduces 70% of their maintenance and 50% of cost savings which enables continuous delivery.

  • Codeless, No vendor lock, Built-in framework
  • Any browser & OS, cloud-based, actionable reporting
  • Business process, Automated, Visual, plug & play
  • Visual test, comprehensive taxonomy, requirement mapping
  • In-sprint automation, Web-Mobile-API & Desktop

Lamda test

  • This tool is the most famous automated tool for desktop and web applications. This tool assists the manual and automation cross-browser testing in languages like Java, Python, or Javascript, on a combination of 2000+ desktop and mobile browsers.
  • Online Browser Compatibility Testing
  • Test Responsiveness on all Screen Sizes
  • Inbuilt Issue Tracker
  • Parallel execution of tests reduces your testing span.
  • It has remarkable testing for Geo-Targeting, Geo-Blocking, and Geo-Location in over 27 countries, including India, Japan, Canada, the United States, Germany, Australia, and the United Kingdom
Conclusion

Automation testing has become a cornerstone for delivering high-quality products efficiently. It offers the promise of precision, repeatability, and speed that manual testing often struggles to match. However, the success of your automation efforts hinges on selecting the right tool for the job.

As we’ve explored, the process of choosing the appropriate automation tool is multifaceted. It demands a keen understanding of your project’s specific requirements, a consideration of budget constraints, and a careful evaluation of available options. Your choice should ultimately empower your team to efficiently identify defects, enhance software quality, and expedite the delivery of robust, error-free applications. In the end, the right automation tool can be the catalyst for achieving excellence in your testing efforts.

About the Author:

Saranya N S is heading Quality Assurance in Mantra Labs. She has in-depth knowledge of testing automation frameworks and has worked in automation teams in various domains across the IT industry.

Further Reading: How is Automation Testing Effective in Defect-Free Delivery?

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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