How Important Test Automation Is?

Test automation is the cornerstone of effective software development in the collaborative DevOps environment, where teams from development and testing converge in the pursuit of continuous integration and delivery. Beyond its function in quick issue discovery, it protects code quality by making sure that standards are obeyed without exception.

Understanding AccelQ

AccelQ is a cutting-edge platform for continuous testing and test automation. It provides a centralised environment for testing operations by seamlessly integrating test design, automation, and execution. Businesses may automate testing processes with AccelQ, leading to quicker product releases, cost savings, and improved software quality.

What Market Say

As of the latest reports, the global software testing market is projected to reach $60 billion by 2025, with North America accounting for a significant portion of the revenue. AccelQ’s innovative approach to testing positions it as a key player in this burgeoning market, offering businesses a strategic advantage in their development efforts.

The Challenges with Traditional Test Automation Tools

For years, traditional test automation tools have presented challenges that hindered the seamless adoption of test automation across industries. Complexity, coding requirements, flaky tests, high maintenance costs, and a lack of intuitiveness have plagued the effectiveness of many tools.

According to a recent survey, 70% of software testing professionals cite the high maintenance efforts required by traditional test automation tools as a major challenge.

Enter ACCELQ: A Paradigm Shift in Test Automation

ACCELQ emerges as a beacon of hope in the world of test automation. Powered by artificial intelligence and boasting a codeless approach, ACCELQ transforms the landscape of test automation in several profound ways.

1. AI-Powered Automation at Its Finest

ACCELQ leverages the power of AI to enable codeless test automation. This means that even testers without extensive coding skills can utilize the tool effectively. It simplifies the complexities of testing while ensuring robust and comprehensive coverage.

2. Cost Reduction: A Game-Changer

Imagine a world where you can achieve more while spending less. ACCELQ’s codeless nature and reduced maintenance efforts translate into significant cost savings for your organization. ACCELQ users have reported a staggering 50% reduction in testing costs.

3. Multi-Channel Automation

Whether it’s web, mobile, APIs, or desktop applications, ACCELQ offers seamless automation across your entire enterprise stack. It eliminates the need for multiple tools, streamlining your testing process.

4. Zero Coding: The Future of Automation

ACCELQ’s NLP-powered codeless approach revolutionizes automated testing. It harnesses Natural Language Processing (NLP) to enable testers to create and execute tests without traditional coding. This makes testing more intuitive and accessible. The approach handles real-world complexities, including intricate workflows, dynamic data inputs, and complex validation logic. It’s highly scalable, adapting seamlessly to projects of varying size and complexity.

Over 80% of ACCELQ users praise this zero-coding feature for simplifying testing efforts. By eliminating the need for traditional coding, testers can focus on designing comprehensive tests that ensure software quality.

ACCELQ’s NLP-powered codeless approach represents a significant leap forward in test automation, making it more accessible and efficient.

5. Packaged Apps Automation

ACCELQ LIVE, a part of the ACCELQ suite, is a transformative technology for cloud and packaged app testing and automation. It offers a seamless, defect-free, and agile testing experience that reduces costs and maintenance efforts.  ACCELQ LIVE has demonstrated a 60% reduction in defects and an agile testing experience.

6. Quality Lifecycle Management

ACCELQ doesn’t just automate testing; it revolutionizes how you manage your quality lifecycle. By unifying test design and execution, it streamlines your processes and accelerates the journey to high-quality products.

Use Cases of AccelQ

AccelQ’s versatility extends its usefulness across various industries and scenarios. Here are some notable use cases that highlight its effectiveness:

E-commerce Excellence

In the highly competitive e-commerce industry, rapid website and application updates are paramount. AccelQ enables e-commerce businesses to conduct seamless testing across platforms and devices, ensuring a seamless shopping experience for customers. Retail giants like Amazon have reaped the benefits of AccelQ’s automation capabilities, achieving faster rollouts of new features and heightened user satisfaction.

Banking and Finance

In the financial industry, accuracy and security are indisputable requirements. Financial organisations may make sure their software complies with legal requirements and is secure by using AccelQ’s thorough testing. In the era of digital banking, where customers want constant access to their accounts, this has proven extremely important. Leading banks have implemented AccelQ to improve their digital services and lower the risk of software errors.

AccelQ Unified: Seamless Integration of Web, API, Mobile, and Manual Testing Tools 

AccelQ Unified is a groundbreaking integration that brings together AccelQ’s versatile testing tools into a cohesive and powerful testing ecosystem. It seamlessly combines Web, API, Mobile, and Manual Testing capabilities, offering a comprehensive solution for testing teams. With AccelQ Unified, testing professionals can efficiently manage a wide range of testing requirements across different platforms and interfaces. Whether it’s web applications, APIs, mobile apps, or manual testing processes, AccelQ Unified streamlines the entire testing lifecycle.

This integrated approach ensures that testing efforts are synchronized, allowing for thorough and consistent testing across all aspects of your software application. It eliminates the need for managing separate tools or platforms, providing a unified interface for all your testing needs.

AccelQ Unified is designed to enhance collaboration and efficiency within testing teams. It enables seamless communication between different testing domains, ensuring that all testing efforts work in harmony towards achieving the highest level of software quality.

For more detailed information about AccelQ Unified and its individual components, you can refer to AccelQ’s official page on Test Automation Unified.

The Future of Software Testing

The field of software testing is constantly changing as a result of advancements in technology. Innovating and paving the way for the future of testing, AccelQ is at the forefront of this evolution:

• AI and Machine Learning Integration

AccelQ is actively exploring the integration of artificial intelligence (AI) and machine learning (ML) into its platform. This means predictive analytics, smarter test automation, and the ability to identify potential issues before they become critical. This proactive approach will revolutionize testing by minimizing the need for manual intervention.

• DevOps and Continuous Testing

The rise of DevOps practices and continuous integration/continuous deployment (CI/CD) pipelines demands faster and more agile testing. AccelQ is aligning itself with these trends, offering seamless integration with DevOps tools. This ensures that testing keeps pace with development, reducing bottlenecks and ensuring that only high-quality code reaches production.

• Cross-Platform Testing

As applications become more diverse, testing across various platforms and devices becomes increasingly complex. AccelQ is committed to simplifying this challenge by providing robust cross-platform testing capabilities. This will be pivotal as businesses strive to deliver consistent experiences across web, mobile, and emerging platforms.

Conclusion

Transforming Your Testing Landscape with ACCELQ

As software development continues its relentless pace, the need for test automation is more evident than ever. Test automation doesn’t just eliminate manual testing; it improves collaboration, communication, and feedback cycles, resulting in faster issue resolution.

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A testing team defining and implementing testing policies, strategies, and procedures throughout the organization typically makes up TCoE. Additionally, they offer project teams advice and assistance regarding testing best practices, resources, and methods. They may also encompass digital assurance services to ensure comprehensive quality assurance across digital platforms and technologies.

The approach of the Testing Center of Excellence (TCoE):

1. 1. Identify the need for a TCoE: Firstly, we need to identify and establish standardized testing processes and methodologies across the organization. Subsequently, we aim to provide a comprehensive set of best practices, tools, and templates to enhance testing quality.
2. 2. Define – Scope & Objectives: This step involves defining the specific goals and objectives of the TCoE, such as establishing a standard testing process across the organization, improving test coverage, reducing defects, or improving collaboration between testing and development teams. The output of this stage is to identify and document test processes, such as test planning, test design, test execution, and defects management.
3. 3. Hiring an Expert TCoE team: Create a dedicated testing team responsible for developing and implementing testing processes and best practices across the organization. The first step in building an effective test team is to hire the right people. You need to find individuals with the necessary technical skills and experience in software testing. Look for candidates with strong attention to detail, analytical thinking, and good communication skills.
4. 4. Setup – Tools & Technologies, Team & Environment: One crucial aspect of TCoE implementation is selecting and adopting appropriate tools and technologies. This blog post will explore the significance of tools and technologies in TCoE implementation and discuss some key considerations for their successful integration.

Enhancing Test Efficiency with Tools

The implementation of a TCoE requires the effective utilization of a variety of tools and technologies to maximize testing efficiency. These tools play a crucial role at different stages of the testing process, including test planning, execution, defect management, and reporting. By automating repetitive tasks, facilitating test case management, and enabling smooth collaboration, these tools empower testing teams to concentrate on important areas like exploratory testing and test strategy development. They contribute to speeding up test cycles, enhancing test coverage, and ultimately elevating the overall quality of software products.

Selecting the Right Tools

When choosing tools for TCoE implementation, organizations need to consider their specific testing requirements, project scope, and team capabilities. It is crucial to assess factors such as tool functionality, scalability, integration capabilities, vendor support, and cost-effectiveness. Popular commercial tools like TestRail, Zephyr, or HP ALM offer comprehensive test management features, while open-source tools like Selenium, JUnit, and Jenkins provide robust automation capabilities. The tool selection should align with the organization’s goals, project complexity, and the skill sets of the testing team.

Integration and Collaboration

An essential aspect of successful TCoE implementation is ensuring seamless integration and collaboration between various tools and technologies. Test management tools should integrate with defect tracking systems, continuous integration servers, and other relevant tools in the software development ecosystem. This integration enables real-time information sharing, traceability, and streamlined workflows, ensuring smooth coordination between development and testing teams. By fostering collaboration and communication, organizations can break down silos and establish a cohesive and efficient testing environment.

Continuous Improvement through Tool Evaluation

The implementation of tools and technologies within a TCoE is not a one-time activity. It requires continuous evaluation and improvement to stay abreast of the evolving testing landscape. Regular assessment of tool effectiveness, vendor updates, industry trends, and emerging technologies is vital to ensure that the chosen tools are still meeting the organization’s needs. Periodic tool reviews and benchmarking against industry standards enable organizations to identify areas of improvement, explore new tools, and make informed decisions regarding tool upgrades or replacements.

How to select the right tool:

1. 1. Test Management Tool: Test management tools offer several key benefits in the software testing process as below:
   1. (i) It provides a centralized platform to plan, organize, and manage all testing activities.
   2. (ii)The tool is used for enabling the creation, storage, and version control of test cases.
   3. (iii) Most test management tools integrate with defect tracking tools, enabling seamless defect logging, tracking, and resolution.
   4. (iv) Test management tools provide collaboration features like comments, attachments, and notifications.
   5. (v) This tool can integrate with test automation tools, allowing automated test scripts to be executed and managed within the same platform.

When assessing the feasibility of a test management tool for a project, consider the following factors based on project needs:

1. 2. Defect/Bug Tracking Tools: There are different Defect tracking tools options available such as JIRA, Bugzilla, or Trello which helps in logging, tracking, and managing defects throughout their lifecycle. These tools facilitate effective collaboration between testers, developers, and stakeholders, ensuring timely resolution of issues.
2. 3. Collaboration and Communication Tools: Some popular collaboration and communication tools like Confluence, Microsoft Teams, or Slack facilitate effective communication, knowledge sharing, and documentation within the TCoE team. During the pandemic, with many organizations shifting to remote work to maintain business continuity, Microsoft Teams has provided a platform for remote teams to stay connected, collaborate on projects, and communicate effectively. We can also use Microsoft Teams as a test management tool.
3. 4. Reporting and Analytics Tools: Reporting and analytics tools like Power BI, Tableau, or JIRA dashboards provide visualizations and analytics on test execution, defect trends, test coverage, and other key testing metrics. 

How to set your team and environment:

4. 5. Training – Testing team: Once you have hired your test team, providing them with the right training is essential. This includes technical training on software testing methodology and soft skills training on communication, teamwork, and time management.
5. 6. Monitoring – Team Performance: The efficient management of your test team is essential for the success of your project. PMs / Team Leaders must have clear objectives and expectations of their team members and provide regular feedback on their performance.
6. 7.Set up Framework and Environment: This phase includes establishing a standard framework and testing environment throughout the organization, including test scripts, data, and environments.

Roadmap of Testing Center of Excellence (TCoE):

The roadmap of a Software Testing Center of Excellence (TCoE) can vary depending on the organization’s specific goals and needs. However, here is a general outline of the key components that are commonly included in a TCoE roadmap:

Critical Levers of Testing Center of Excellence (TCoE):  

So let’s see how Quality Engineering Services can help. The primary objective of a TCoE is to improve the quality and efficiency of software testing activities. Here are some key levers or factors that contribute to the success of a Testing Center of Excellence:

In conclusion, a Testing Center of Excellence (TCoE) is essential for improving an organization’s software testing quality and efficiency. It establishes consistent processes, encourages teamwork, and continuously improves The main goals of a TCoE are to make testing easier, increase test coverage, speed up product release, satisfy customers, and foster innovation. Ultimately, a well-executed TCoE leads to better software, cost savings, higher productivity, and a competitive edge in the market.

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Grey box testing is a software testing technique that includes the elements of black box testing and white box testing. It involves testing a system or application with partial knowledge of its internal workings. In other words, the tester has some knowledge of the software’s internal structure, but not the complete knowledge that a white box tester has. This makes grey-box testing a more realistic and efficient approach to testing than black-box testing, while still providing some of the benefits of white-box testing.

You can use a technique called “grey box testing” to troubleshoot the software and identify its weaknesses. It can be used as an objective, non-obstructive form of penetration testing. In this kind of testing, the focus is on an application’s internal workings rather than how those workings interact with one another.

Outcome-based grey box testing

Efficient Testing: Grey box testing allows testers to focus their efforts on specific areas of the system that are likely to have defects, which helps to reduce testing time and costs.

Realistic Testing: Unlike black box testing, grey box testing provides insight into the system’s internal workings, making it more realistic and closer to how end-user interact with the software.

Comprehensive Testing: Grey box testing allows testers to access the system’s front-end and back-end, which helps identify defects that may be missed in black box testing.

Better Test Coverage: With access to some of the system’s internal workings, grey box testing can help identify more comprehensive test cases, leading to better test coverage.

Improved Quality: By identifying defects that may be missed in black box testing, grey box testing can help improve the overall quality of the software.

The Process

In grey box testing, test cases are generated based on algorithms that assess internal states, application architecture knowledge, and programme behavior rather than by testers having to design test cases from scratch.

 Steps to follow, while performing Grey Box testing:

• Identifying and choosing Inputs from white and black box testing approaches.
• Identify possible outputs from these inputs.
• Identify key paths for the testing level.
• Identify sub-functions for deep-level analysis.
• Identify responses for sub-functions.
• Detect probable outputs from sub-functions.
• Perform sub-function test cases.
• Assess and verify results.

Also read:  IoT Testing Challenges and Solutions: Overcoming the Unique Obstacles of IoT Testing.

Layers of Grey box

Grey box testing involves examining each layer with greater depth.

Database Testing: During grey box testing of a database application, the tester may have access to the database schema but not the source code. As a result, they can more effectively and realistically test the database’s features.

API Testing: In API testing, a tester may have knowledge of the API’s internal structure and data flow, but not the complete code. This allows them to identify any issues in the API functions or data handling.

Security Testing: In security testing, a tester may know the system architecture and security protocols but not have entire knowledge of the source code. This helps them to identify any security vulnerabilities that may be missed in black box testing.

Performance Testing: The tester may know the system architecture and data flow, which allows them to identify any bottleneck or performance issues in the system.

Inference

By reducing bugs at an earlier stage of the life cycle, grey box testing can help to improve quality by identifying defects in all the layers.

It offers some insights into how the system functions internally, making it more realistic and thorough. The transition to Grey Box testing is inevitable given how quickly digital technologies are developing. Higher quality and less expensive results will result from moving left and towards the earlier testing phase.

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Some of the key technologies that are transforming digital technologies are:

Artificial Intelligence/Machine Learning

Artificial Intelligence (AI)/Machine Learning is at the heart of digital transformation and quite pervasive, powering many solutions, improving personalization, and enhancing user experiences. AI/ML systems run on natural language processing algorithms that convert unstructured data to structured data. This brings in an element of uncertainty and ensuring that they function as expected can be a big challenge as the algorithms are trained to learn based on user activity and evolve to enhance user experience, supporting human intelligence to improve outcomes.

Internet of Things

The Internet of Things devices are also becoming ubiquitous, empowering humans with smart devices that improve their efficiency and effectiveness. IoT devices contain embedded systems, controlled by an application, and connected to a network that allows data to be gathered and uploaded to the cloud. This data is further used for analytics and developing ML models.

NFT

Non-fungible tokens or NFTs have become popular for the easy money, or cryptocurrency, that people are making by selling art on the net. But that’s not the only use for NFTs. It is, in fact, a highly secure solution for digital assets and is also used for creating smart contracts based on blockchain technology. It leaves an audit trail of transactions, preventing fraud and improving traceability.

These and many such technologies have disrupted the way businesses run and enhance user experience. However, the extent of digital transformation through automation and resource reallocation is limited by the ability of different components to interact smoothly with each other. The digital ecosystem is continually evolving, and people and processes are becoming more interconnected through multiple channels like mobile, social, analytics, and cloud stack. This is creating a complex environment. And any lapse or lag in this environment will cause interruptions and disruptions. Performance can become an issue if the underlying engine or the data is not performing as expected or of the right quality. Managing these complexities associated with digital transformation is where digital assurance and quality testing come in.

Also read: The Role of IoT in Metaverse

Factors Driving the Need for Digital Assurance of Future Tech Applications

Complex Digital Ecosystem

The various components of digital transformation, including mobile, social, analytics, and cloud, need to be synchronized to deliver the desired objectives. This is very complex due to the varying levels of maturity of the solutions and the organizations embracing these technologies.

Greater Security Threats

As systems become more intelligent, so do the hackers and malicious elements. IoT devices also leave enterprise systems vulnerable due to the interconnection of applications across devices, especially at the edge. This has increased the need for tighter security solutions that are properly configured, constantly tested, and revised to meet evolving challenges.

Hybrid Environments

Often, businesses with legacy systems continue to retain on-premises systems for a part of their operations. This requires digital solutions to be able to access legacy IT infrastructure without impacting core functionalities.

Enhancing Customer Experience

There is no predictability as to how a customer will access the services. Therefore, ensuring a uniform experience across the four pillars of the digital ecosystem becomes essential. Each component must deliver optimum performance throughout the business journey and enhance customer delight.

Agility and Shorter Time-to-Market

Shorter delivery cycles have become very critical to ensure agility in a highly dynamic digital environment. It requires not only faster development cycles but also quality assurance to keep pace.

A digital-first approach in the modern business world requires business models and customer strategies to match the fast-paced transition at every stage of the journey. Traditional testing and automation are not equipped to meet these challenges and ensure the required quality assurance, security, performance, and functionality needs are met. The traditional QA processes and infrastructure also need to match the new need and be powered by modern technologies. They require an AI-based testing framework to enable machine learning and continuous improvement of testing for new technologies such as AI and IoT.

Automated Quality Assurance with Modern Technologies

Test automation and quality assurance help meet several of the development goals. They accelerate testing, improve outcomes, and shorten the time to market.

Some of the key emerging technologies in digital quality assurance and testing are:

Shift-Left Approach to Testing

In the (traditional) waterfall approach, apps would be tested after the development is complete and before the production stage. But today, organizations use a Shift Left approach where the testing team gets involved right at the beginning of the software development process to identify and prevent defects early.

Codeless Test Automation

Codeless test automation involves no code. It enables the democratization of testing by empowering citizen testers to build test use cases with object, data, model, and keyword methods. User-friendly interfaces allow even the uninitiated to easily understand the scripted testing tools and use them effectively to debug at every stage of the development process.

Machine Learning/AI

Artificial intelligence and machine learning enable the prediction of the test script and write test cases appropriate to the requirements. These technologies help create multiple frameworks such as linear scripting, data-driven hybrid testing, modular testing and such. In keyword-driven codeless frameworks, a table format is used to define keywords or action words that execute the function. Behavior-driven framework allows tests to be written in plain, descriptive English. With NLP-driven frameworks, Natural Language can be used to write the test steps.

NLP and keyword-driven frameworks have gained popularity as they are easy to use, and allow test cases to be written seamlessly in a simple, natural language that can be easily interpreted and understood. They also help draw insights from unstructured data for improved decision-making.

Data Quality Assurance

Since data is at the core of all modern technologies, the quality of the data also becomes crucial. The data should be complete, deduplicated, have integrity, and be relevant. This requires data to be screened and cleansed of anomalies for achieving expected outcomes.

Indium for Digital Assurance

Indium Software has all-round experience in development, quality assurance, testing, and DevOps. This lethal combination enables it to leverage the latest technologies to create frameworks that can be easily customized to meet the varying digital assurance requirements of different enterprises. Indium has more than 22 years of QA experience and provides end-to-end assurance services that shorten delivery cycles and improve customer experience.

Its quality assurance stack includes:

Quality Assurance

Covers functional QA, performance assurance, test data management, smart test automation, and business value chain testing.

Quality Engineering

Covers CX testing, testing of API/microservices to QAOps, low-code platform testing, data assurance, and Smart Assisted Testing

Future Tech Digital Assurance

Covers QAOps and digital assurance for IoT-enabled apps, Non-Fungible Tokens (NFT), and Blockchain, and testing of AI engines and models

Test Advisory & Consulting

Helps clients with QA Maturity Assessment and develop the overall test and test automation strategy for DevOps and Agile environments.

FAQs

The post AI and IP-led Digital Assurance with a Test Automation Platform for Testing AI, IoT, and other Future-Tech Applications appeared first on Indium.

Data lakes are storage systems that are designed to hold large quantities of raw, unstructured, or semi-structured data in its native format until it is demanded. The term “lake” is used to reflect the idea of a vast body of data, just like a lake with a vast body of water in its natural state. This allows organizations to store data in various formats such as files, objects, logs, sensor data, social media feeds, etc and can include data from various sources, including IoT devices, social media platforms, enterprise applications, and more.

Data lakes are designed to support various types of data analytics, including exploratory, descriptive, and predictive analytics, and can provide organizations with a more comprehensive view of their data, enabling better decision-making, and improved data insights. The raw form of data allows more flexible and agile data processing. Data lakes are usually implemented on Hadoop-based technologies like HDFS, cloud storage like Amazon S3, and use NoSQL databases like Apache Cassandra, or Apache HBase.

Pic (1) ref from: What is the Dark Web? (soscanhelp.com)

Data Warehouses are storage systems that are designed to hold structured data, such as data from transactional systems, CRM systems, or ERP systems. Data warehouses usually use a relational database management system (RDBMS), which means that data is organized into tables and can be queried using SQL. Data warehouses are typically implemented on SQL-based technologies like Oracle, Microsoft SQL Server, or Amazon Redshift. It is designed to support business intelligence (BI) activities, such as reporting, analysis, and data mining.

Thus, Data lakes are optimized for storage and batch processing whereas data warehouses are optimized for fast querying and analysis. In terms of cost, Data lakes are often less expensive to implement and maintain than data warehouses, because they use open-source technologies like Hadoop and NoSQL databases, and they do not require as much data processing and transformation. Data warehouses, on the other hand, can be more expensive to implement and maintain because they require specialized hardware and software, and they often require more data transformation and processing. Data Warehouses are well-suited for structured data and traditional data processing, Data Lakes are better suited for handling large volumes of unstructured data and more flexible data processing.

Check out this informative blog post on the ETL Testing – A Key to Connecting the dots

Let’s now deep dive into its testing practice in Digital Assurance. Data lake testing and data warehouse testing differ in several key aspects. These differences can affect the testing approach, methodologies, and tools used for each of these systems.

1. Data volume and variety: To check the accuracy and completeness of the data stored; Data lakes are designed to store a wide range of data types, including structured, semi-structured, and unstructured data from multiple sources. In comparison, data warehouses typically store only structured data that has been pre-processed and transformed to meet specific business requirements.
2. Data ingestion and processing: To verify that the data is properly loaded into both storage systems from various sources. Data lakes allow flexible data ingestion and can handle data from a variety of sources in real-time. It follows the ELT method as such: Extract, Load, and then Transform, which means that data transformation, cleaning, and processing are typically done after the data is ingested into the lake. In contrast, Data Warehouses are typically designed to store cleaned and processed data, which means that data transformation happens before the data is loaded into the warehouse. It follows our traditional ETL ( Extract, Transform, Load) processes, and may not be able to handle real-time data.
3. Testing scope: The scope of data lake testing is broader than data warehouse testing as it involves testing data ingestion, processing, storage, and retrieval from multiple sources. Data warehouse testing is typically focused on ensuring data accuracy, completeness, and consistency.
4. Testing tools: Data Lake testing may require a different set of tools than data warehouse testing. For example, data lake testing may require big data testing tools that can handle large volumes of data, while data warehouse testing may require more traditional testing tools such as SQL scripts or data quality tools.

5. Data Security: Data lakes often require a different set of security measures than data warehouses, as they store a wide range of data that may include sensitive information. Data lake testing should ensure that data is protected against unauthorized access, tampering, and theft.
6. Data Access: Data in data lakes can be accessed by a wider range of users and applications, which can make it more challenging to manage and monitor data access. Data lake testing should ensure that data access is secure, efficient, and auditable. Data warehouses are often designed to support a specific set of business intelligence and analytics applications.
7. Performance: Data retrieval in a data lake can be slower than in a data warehouse, as the data must be processed and organized before it can be analysed. Along with that, Data lakes can store petabytes of data, making performance a critical concern. Data lake testing should include performance testing to ensure that data retrieval, processing, and analysis can be performed in a timely manner.
8. Scalability: As data lakes grow, the ability to scale the system to handle larger amounts of data becomes increasingly important. Data lake testing should include scalability testing to ensure that the system can handle growing data volumes and processing needs.
9. Integration with other systems: Data lakes are often integrated with other systems, such as data warehouses, cloud services, and big data analytics tools. Data lake testing should include integration testing to ensure that data can be effectively shared and utilized by these systems.

In conclusion, data lake testing and data warehouse testing are both important for ensuring data quality and accuracy, but they have different requirements and testing needs due to the differences in the nature of the data and systems involved. We are aware that both Data Warehouse testing and Data Lake testing are emerging now in this new digital era. Data Lake testing is important because it helps to ensure that the data lake is functioning as expected, that the data is of high quality, and that the data lake is secure and compliant. By performing data lake testing, organizations can build trust in the data and use it with confidence in decision-making and analysis. With the help of the iDAF (Indium Data Assurance Framework) framework and other widely used tools on the market, Indium Software is successfully conducting Data Lake testing.

The post How does Data Lakes Testing differ from Data Warehouses Testing? appeared first on Indium.

The quality of the project or product is managed by a QA professional. They are expected to try their hardest to test the system/application and find as many bugs as they can to ensure that the system functions as desired/expected in the environment it was designed for. From the beginning of the process until the release of the application, it is heavily involved. At a high level, QA is primarily concerned with making sure that the system correctly implements the requirements or specifications with the least amount of troubleshooting required.

Career Advancement brings QA to a decision point where they must choose which path they want to advance in their career.

Business analysis is a natural progression and a great career move for QA professionals involved in many overlapping activities like BA work. Business analytics is certainly a high-profile, high-payingcareer and it is one of the fastest growing professions with an all-time high growth rate of 14%. In short, a career change to business analytics for QA is much more promising and lucrative in today’s scenario.

Business-certain behavioural skills are essential for anyone wishing to transition to this analyst role. As a QA professional, if you have excellent verbal and written communication skills, are used to working with customers, and have good analytical skills, the business analyst role is perfect for you.

Good QA professionals who pay attention to detail are more likely to succeed as business analysts.

Here are some reasons why a career change from QA to BA makes so much sense.

Fair Understanding of Requirements

QA professionals read, analyse, and review requirement specifications based on their job profile to ultimately understand better testing. While doing this, the Quality Analyst improves their requirement/domain knowledge day by day. By putting extra effort into requirements analysis and business domain understanding, QA professionals can better plan their career change to the business analytics profession.

Intelligent at Documentation

QA specialists are involved in documentation like test planning, test case development, and error reporting at every stage of the project lifecycle.

Documentation competence is another essential skill for a business analyst. By learning new tools such as diagramming/modelling tools and effective communication skills, QA professionals can acquire the skills they need to become effective BAs.

Learning and Refining Requirements Documents

Write or document the best requirements over time. Critical and unconventional thinking. A good QA Professional is someone who has the ability to think critically and out of the box while solving drawn gaps in business problems and requirements.

Understand User Expectations Better

When testing an application for different roles/privileges, the QA expert can know what users expect from the application.

The question that then arises is whether these qualities are sufficient for a QA professional to smoothly transition to the role of business analyst. While these inherent qualities as a QA will aid in the transition, QA professionals need some degree of career development and planning, learning new skills, and maintaining existing need to improve the skills.

So here are some pointers to help the QA professional advance her chosen career as a business analyst.

1. Understand Business Fundamentals

The various organisational functions and how they directly or indirectly serve internal and external customers should be well-understood by BAs. It is significant. A QA professional must first comprehend how a business runs, how its revenue stream’s function, and what it does. We advise reading through the APQC-provided common process classification framework before getting started. I also advise reading introductory books on marketing, operations, finance, and business strategy.

2. BA Learn Processes, Tools, and Techniques

Like other activities, business analysis follows a process. The best free resource available is IIBA’s Business Analysis Core Standard. This is a short document, about 50 pages, but a very informative overview of the BA process. It gives you a good idea of how business analytics are actually done in your organization.

BAs use a variety of tools throughout their work.

They frequently employ the business process modelling, state modelling, and use case modelling tools.

Most organisations use Microsoft Visio, Lucid Chart, and BizAgi Business Process Modeller, which are all very well-liked tools.

3. Fully understand your field and industry

The Internet offers numerous resources on almost every subject increase. Search business term repositories (domains) available on a project or client/web. You can also get the manual for the domain you are on. If you work in retail, look for the Handbook of Retail. Reading the manual will give you a pretty good idea of ​​how a retail domain works. More insight and knowledge about your domain and organization will improve stakeholder acceptance. And stakeholder acceptance is key to business analyst success.

4. Actively participating in requirements gathering

Gathering serves as a foundational study for the requirements generation phase. Requirements research is one of the most important tasks for any BA. As one scholarly article puts it: „Collection errors are often the leading cause of system failures and crashes, making the overall loss or error correction cost very high. “

Participate actively in the requirements gathering process or contribute.

To better comprehend the project and its value to its users, it is imperative to become familiar with the various requirements gathering techniques.

Frequently acting as a liaison between clients and other stakeholders. They collect requirements, serve as a liaison between developers and testers, and resolve conflicts on both sides. Improved behavioural skills are needed for this. Communication, stakeholder dialogue, active listening, facilitation, conflict resolution, and creative thinking are essential business analysis skills.

5. Training & Certification – The Ultimate Key to Success

BA Certification & Training enhances professional skills and business knowledge. A Business Analytics certification expands your horizons on BA techniques and approaches. Accreditation can improve overall performance, eliminate uncertainty and expand market opportunities.

IIBA’s ECBA certification is the right place to enter the field of business analytics. ECBA not only provides a solid foundation for starting the BA profession, but also helps with global accreditation. ECBA accreditation does not require aptitude standards related to training and covers aspects related to requirements extraction, requirements analysis and management, stakeholder management, technologies used by BAs, etc.

Experience Abundant BA trainers. This increases your chances of passing quickly on your first attempt.

6. Participating in the Implementation Process

During implementation, there are many important tasks that the business analyst must perform. These tasks contribute to the success of the project and ensure that business goals are met. Most prioritizations and workarounds are resolved during this phase.

Future BAs should participate in the implementation process and learn all the processes, techniques and nuances involved.

7. Seek Internal BA Role Changes upon completion

BA training and certification, you will be able to leverage your newly gained knowledge and experience in your industry/domain by seeking opportunities within your current organization. The first thing to do is look for opportunities for internal transfers. Discuss your career goals with your manager and look for opportunities to practice your bachelor’s degree skills/techniques.

It is often said that in order to be successful, you need to be proactive and not wait for life to come your way.

Accompanying business analysts on specific projects within the organization helps them understand and apply the concepts they learn. Put yourself in the shoes of a business analyst and evaluate and learn from what you observe.

Conclusion

Moving from Quality Assurance to Business Analytics seems possible. In fact, QA professionals are the perfect people to transition into the field of business analytics.

As you move forward toward your goals, there are„ what if “moments and stumbling blocks that will test your resolve. All it takes is dedication and a willingness to learn and succeed no matter what. Remember that a good business analyst is one of the greatest assets an organization can have. Having a good and capable bachelor’s degree in an organization is like finding the pot of gold at the end of the rainbow. 

The post Is switching from QA to BA a good career move? appeared first on Indium.

As predicted, this year saw the advent of several novel technologies that can help organisations transform their IT workforces across all major verticals. However, the quality engineering domain has several major service lines that are geared to accommodate and expedite comprehensive technology transformation.

Before we dive deep into two of the most sought-after emerging technologies, let’s briefly discuss digital quality engineering.

Digital Assurance and Quality Engineering: The What and Why?

The long-held definition of quality engineering is: “the discipline of engineering concerned with the principles and practise of product and service quality assurance and control.” (Source: Wikipedia) The field of digital quality engineering extends beyond software testing. 

Stress detection through stress testing is an essential component of quality assurance. Initially, manual quality assurance was used to support the mainframe era, which was followed by the introduction of automation through the World Wide Web (WWW), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and so on. Businesses have evolved into an “Intelligent Digital Mesh” (IDM), and the Cloud Computing revolution has ushered in the “digital quality assurance” method.

The three components of speed, quality, and intelligence combine in digital QA to establish a unified approach to accelerated Continuous Quality Improvement (CQI). As organisations scramble to attain a digital makeover, digital quality checkpoints shine through with enhanced risk mitigation and holistic customer-centric solutions. Many aspects of digital QA, such as cognitive QA, IoT testing, cloud testing, chatbot testing, and voice bot testing, aid in digital transformation.

As companies mature in addressing this new IT environment, Digital QA will evolve to address the end-to-end automation of test ecosystems, improve the velocity of quality delivery, and enable Agile and DevOps frameworks and AI (AI). One such strategy that incorporates AI and ML (Machine Learning) into automation is autonomous testing.

Best Read: Testing a bank application: A Success Story

Autonomous Testing: The Next Big Thing in Automation

Many sectors, including software, strive for flawless user experiences. However, to deliver swiftly, organisations must reduce delivery cycles from months or weeks to hours or less. This is where automation comes in. Before automation, test cycles were frequently lengthy. Handwritten tests have a set of stages and checks that require involvement. 

When the first wave of test automation arrived, it completely transformed the test execution process. Automation allowed tests to run more quickly and constantly. However, the disadvantages of manual triggers and maintenance issues quickly became apparent. The CI/CD methods further blurred the line between developers and testers. At this moment, the automation sector appears to be at a standstill.

Challenges of Automation Testing

Some experts argue that automation testing, when faced with fast-paced new-age practices, failed to fully deliver on its promises. Some of the main challenges with automation are listed below:

Challenge to build

A high degree of skill and domain expertise is essential to creating high-quality automation scripts. These include proficiency in programming languages, mastery of the tools, and thoroughness with client requirements. Manual creation of tests remains the major bottleneck for automation.

Costly debugging and maintenance

It would not be incorrect to say that automation is a maintenance nightmare in and of itself. It incurs high costs and is time-consuming. Sometimes a minor update to the GUI would result in re-recording an entire script. The fact that debugging a script is equally troublesome adds to the difficulty.

Stability concerns 

Considering the most widely used Selenium automation tool, most scripts rely on XPath. Any change in it, and the scripts become useless. Also, the scripts don’t consider that the infrastructure is inherently unstable, so tests “fail” when they time out or crash.

Automation vs. Autonomous Testing

We read out both to test in automation, considering all forms of testing as verification of interaction. What if we could automate dictation entirely? To do this, autonomous testing makes use of AI and ML.

Autonomous testing is the automated creation and maintenance of tests and the analysis of results using an application blueprint. Using machine learning algorithms, these tools cut through redundant data in testing and spot irregularities.

Benefits of Autonomous Testing

• Early bug detection through impact and root-cause analysis lowers the risk of failure.
• Autonomy in test infrastructure facilitates faster test cycles. 
• Ability to self-heal the scripts while encountering unexpected application changes.
• Reduction in maintenance costs through automatic identification of scenarios and test data
• Better allocation of resource productivity by eliminating human dependencies and skillset requirements 
• Increased productivity with faster and improved release cycles

The transition from testing automation to fully autonomous testing is in and of itself a challenge. Most businesses’ current automation approaches can be summarized as follows:

• Assisted Automation: Using an automation tool, QA creates and executes tests, but manually manages and maintains scripts.
• Partial Automation: This tool helps create test cases and execute them, and the tester is responsible for test monitoring.
• Accelerated Automation and Unassisted Automation: The tool manages every component of the test, reducing the tester’s responsibility to assess the impact of a change.
• Autonomous Testing: The automation tool is redefined as autonomous and learns from failed tests to make the best judgments. Human-machine collaboration via seamless AI-powered systems is critical to embracing total autonomy. 

Chaos Testing: A Method for TiP 

While autonomous testing seeks to reduce test cycle times, chaos testing employs “Testing in Production” (TiP) to increase the fault tolerance of a built system.

What is TiP? 

Testing in production is based on the premise that only real-time input can successfully supplement the quality assurance process. Every version release is, in theory, a production test with high value-to-cost risk.

Principles and Steps in Chaos Testing 

Chaos testing assesses the resilience of a system to unexpected events by inducing failure. Experts explain this by stating that it is preferable to break a system intentionally at the optimal time than to have it crash unexpectedly. With the rising importance of “mean time to repair” (MTTR), it is vital to minimise the recovery time of a business.

Chaos testing is based on the following established plans and experiments

• Create a hypothesis: Here, we define the steady state, which is the system response to unstable variables. Metrics such as the total throughput, error rates, and system latency help make assumptions about its status. 
• Design and run experiments: Identify, design, and run all probable failure scenarios in a controlled manner. Doing this ensures you have a backup plan for even the worst-case scenario. if the backup strategy is unclear, the system recovery pathways are documented.
• Analyse the impact: Compare and contrast the outcomes with the initial hypothesis. The error difference in the metrics aids in deciding whether to improve or limit the blast radius and reveals additional issues. 

Benefits of Chaos Testing 

• Prepare for the unforeseen: By simulating all potential failures, you get the information necessary to fortify the system against surprises. 
• Uncover the unknowns: It illustrates the system failure reaction and facilitates the development of associated recovery routes by utilizing several experiment-specific indicators. 
• Reduce system downtime: With a good grasp of the common failures encountered by a system, we can reduce the MTTR by making continuous fault injections and regularly targeted improvements. 
• Improve customer satisfaction: Chaos testing empowers organizations to gain confidence in their systems’ resilience and increase customer satisfaction.

Chaos Test Tools 

The origin of chaos testing dates to when Netflix transitioned to the cloud via AWS. The unexpected AWS outage caused the Netflix platform to be unavailable for several hours, inducing them to implement the concept of chaos engineering. Today, many tools are available to perform Chaos testing, such as: 

• Chaos Monkey: The first open-source tool via which Netflix assembles an entire suite of fault injection tools called Simian Army. It attacks by discontinuing the VM instances. 
• Chaos Blade: Built for Alibaba, to support wide range of platforms and has dozens of attack modes.
• Chaos Mesh: This tool possesses a fully featured web UI encompassing 17 unique attacks.
• Litmus: This is a Kubernetes-native tool that utilizes Litmus probes to monitor the system’s health statistics. 
• AWS-FIS: This works only with AWS services through a fault injection simulator. 
• Gremlin: Offered as a SaaS technology, Gremlin can be integrated with CI/CD to test entire systems. 
• ChaosNative: It is a SaaS platform that hosts LitmusChaos to perform end-to-end chaos testing. 

Chaos testing is thus a proactive approach to testing, which can begin with small trials in controlled environments. It can then be effectively implemented and scaled up through proper rollback planning and appropriate tools. 

Indium’s Approach to Autonomy in Testing 

Through the uphoriX Test-Accelerator, Indium has taken a step towards autonomy by providing low code automation, intelligent scripting, Script once NFT, a native test data generator, and swift compatibility. Indium considers smart test automation as an emerging vertical that ‘addresses one of the fundamental aspects of cloud-native initiatives – the velocity of code deployment’ and a prerequisite to the ongoing digital transformation.

The post Chaos Testing and Autonomous Testing: Triumphs of Digital Quality Engineering in 2022 appeared first on Indium.

And with that in mind, we have created a list of incredible test automation features and trends that are changing the way software testing services are carried out. If you want to make the most out of software testing, adapting to these is pretty much mandatory, as it can give you some incredible results.

IoT Testing

The Internet of Things is making waves especially in the business world right now. Most IoT devices and apps need testing for security, performance and how easy they are to use.

Not many people are aware of IoT technology and how it works, hence organizations rely on proper testing in order to make the IoT application work properly.

With that in mind, the improvements provided with software testing are pushing QA Services and testers, which means it’s important to always have an up to date approach to software testing.

Adopting IoT testing is pretty much a must, especially with test automation becoming so important right now.

Obviously, this has to go through a lot of trial and error, but the payoff itself can be amazing and that’s what matters the most here.

Combining DevOps with Agile

It is very important to implement all the industry changes while also bringing in something new to the table.

Methodologies like Agile and DevOps are already working very well for most businesses.

But as you prepare for test automation you will need to add some sort of accountability features, as that matters a lot.

And in this case, combining the two methodologies is what you will need to do.

You need to plan and also execute the software testing, but then again you must have a continual response regarding the product quality.

That means training your team on DevOps and test automation for faster deployment.

The focus is to always provide top of the line services and value, ensuring that everything is working properly and without any worries. That certainly helps a lot, and it brings resounding benefits no matter the situation.

Robotic Process Automation

With emerging technologies like AI, Machine Learning, IoT and cognitive computing already transforming industries. 

Robotic Process Automation is the latest in the list of technologies which can re-invent the business process management.

Both RPA and test automation may sound similar, since both involve automation. However, they are quite different.

RPA can be helpful in software testing if you already have an RPA tool in place, it could be of help in your test automation process.

AI implementation

Nowadays the business world is constantly focusing on automation and on using the power of AI.

This is very significant, and it has the potential to do wonders if everything is handled correctly and with the right set of ideas.

Manual testing is still required, but test automation is becoming more prevalent and more powerful, which is a good thing as it accelerates the process and it focuses on value and quality at the same time.

The attention to detail is staggering but knowing how to implement AI testing features is what will make the difference.

You still need to test manually because even AI might fail to spot some issues. But in order to prepare for the future of testing automation, you have to ensure that there are dedicated AI features included in here.

Codeless automation

Why do you need codeless test automation? Codeless Test automation is introduced to increase the scalability of test automation.

It makes it easy for the business users and the testers themselves to easily automate test cases. It helps the software testers to automate test cases without having to worry about coding.

You don’t have to understand the code in order to test properly, and that will lead to much better results and an astonishing, very powerful experience for you to explore and enjoy.

Other things to consider

Machine learning will be just as important as you try to automate and fully customize everything.

Doing this properly can make all the difference, and it has the potential to bring in some major changes. 

Machine learning is designed to offer accurate results without putting in too much stress on the people that do software testing in the first place.

We still have to understand the way AI works and how we can use it correctly, which is why it can be a good idea to wait and see how everything adapts in a proper manner.

We can also expect to see a large increase in the amount of test automation which is being implemented right now.

There are some great opportunities in fields like health monitoring and checks. Things can still go wrong in production, so targeting specific features is what might eventually make all the difference.

But we can expect that in the future, products will be able to test themselves automatically.

People will still have to do some minor checks here and there, but the focus has to be on quality, value and offering tremendous and very impressive attention to detail all the time.

To Conclude

We believe that continuous deployment and integration are the most important aspects when it comes to the future of test automation.

The tests still need to be comprehensive, they have to run virtually and they need to be up to date.

All of this has the potential to bring in incredible changes, and the results can be staggering if everything is up to par and it works very well.

The future of software testing is bright, and both AI and machine learning are key components in its entire evolution.

Software testers need not worry though, as there is still lot of time for testing to be fully automated. After all a human is required to check if the product is usable to humans.

The post Top Trends Shaping the Future of Software Testing appeared first on Indium.

“Hope for the best, plan for the worst”

I do not know of another quote which applies in a better manner to the Software testing industry. As part of the Quality Assurance teams spread across the world, we strive for 1 goal and 1 goal only. The best product. Period

Take a look at the human evolution. What do you see?

Starting out as hunter-gatherers, we made camp in areas teeming with game, nuts and fruits. We moved on when the supply went down.

When we started farming, we used trial and error at times, or simply fell on better/stronger yielding seeds.

Once found, we weeded out the chinks (read weaker strains) and cultivated the better ones.

As time came to pass from one age to another, we got introduced to science and technology. The end result of these advances: better quality of life.

Today in the 21^st century, Quality of life is an industry of its own. No longer an ideology, it has evolved from mere thoughts to physical tools, processes and practices. 

Everything, (notice the emphasis) produced on an assembly line, on a tree, by a plant or handcrafted; has to stand the test of proving its Quality in the terms of durability/efficiency or taste.

When out in the market, we pay anywhere from low to exorbitant price to buy a product purely based on the product quality. Take for e.g. those apples in your refrigerator.

You didn’t pick up the first apples you laid your hands on, did you? You inspected them, the color, any damage, smell etc. The same goes when you are out buying anything; clothes, electronics, automobiles.

You inspect the fabric, read online user-generated reviews etc. to verify the investment you are about to make will actually be worth it.

As luck would have it, the same is applicable a million times manifold of a software development cycle.  

If you have read until here, you are reading this off Indium Software’s blog. Now why would yet another software testing company write/publish an article along these lines when there are thousands, if not millions already existing.

Simply because, we understand Quality Assurance like nobody’s business.

Quality is the key reason why customers/End-users are being loyal to you for the billionth time.

It may be software for the masses, or a tool to manage employees, or a video game. Without ensuring the quality, your application isn’t selling.

Quality Assurance is not just another cog in the development cycle. It’s the last push; the final stand. Good Quality Assurance = Success.

1.Why Indium you ask?

Conor McGregor once said, ‘There is no talent here. This is hard work. This is an obsession.’

Indium is an exclusive focused Software Testing Services Company. We do Quality Assurance for a living.

We don’t just tell you where your application’s functionality breaks. Besides, with our experience, we will be able to tell you how user friendly your app is?

We suggest variations to the design after comparative analysis to other likely applications in the market.

If there isn’t any, then you are in luck. We will help you with making it user friendly enough to make your end user keep coming back for more.

2. How do we do what we do?

To quote from Lee Child’s newest addition to the Reacher series, “Let me hazard a wild-ass guess. It’s cover for something. Given your current batting average, it’s a high level something. Which means you’ll get whatever you ask for. Especially staff. So you’ll be calling me in the morning anyway. Why not tell me twelve hours early?”

We come, ‘Battle Ready’. Which means, the moment we chance upon a project, we get down to work.

Break it down to the essentials, chart out requirements, phase out testing cycles, coincide the testing cycles to tentative development cycles and create milestone based test plans.

Depending on the requirements, we can start right along with the Development cycle.

Ready to pitch in when a QA ready build is available. Or, if your product is live already, we can come onboard to help optimize the test strategy to follow the change in development cycle.

We oversee all aspects of product development (hardware, software, chemistry) and ensure that nothing is released if it doesn’t adhere to the company’s quality system.

To plagiarize an advertisement, “We eat, drink and sleep Quality Assurance”.

“We don’t just count the sheep. We count how many, what species, variations in development based on feed quality, difference in jump height.” Comparison Bridge between the “counting sheep” idea and Test Requirements breakdown.

Quality Assurance is a way of life. It has caused evolution. It has caused social and economic cycles. And we understand it like nobody’s business.

The post Quality Assurance: A Way of Life appeared first on Indium.

I wanted to give the readers of iBlog – customers, prospective customers, employees, investors and several key stakeholders – my perspective of Indium’s positioning in the overall global market.

I looked through our corporate deck, reminisced about conversations I’ve had with our customers and started thinking, deeply, about what makes Indium tick?

While, what I am about to say has been a part of our corporate positioning for over a decade, I certainly thought penning it down and formalizing it would make a difference.

The Need for High Quality Software

Before I begin, let me start with the obvious. Technology and software have certainly become an integral part of every sector, and every process, in this world.

And, therefore, the demand for high quality software, which has gone through a robust testing process, has never been greater.

Over the course of the Indium journey, I have seen the software testing industry go from a pure application level software testing to the more complex system level testing it is today. Test Automation, Load Testing, Functional Testing, Unit Testing have all been around for a while. So what has changed since then? Today, we have applications being delivered over the Cloud and Mobile, and we’re also seeing multi-tier architectures being developed using Agile methodologies. This has necessitated a change in the processes, tools and expertise required to ensure high quality software.

Automation is now the most talked about topic in the IT industry, but, I’d say, this is also nothing new.

We have had several tool vendors such as HP, IBM, Microfocus and other open source tools such as Selenium & JMeter for over 15 years now.

What has changed is enterprises and technology vendors are rapidly releasing software, and therefore test cycle time has significantly shrunk wherein merely throwing extra hands at the problem won’t suffice with omni-channel technologies to be tested. 

From our perspective, we are seeing test automation gain increasing significance over the last 3 to 4 years.

Research conducted by IDC forecasts that the market for discreet worldwide testing services is expected to touch US $39 billion by 2019, with a CAGR of about 12.5 percent. 

More importantly, the IDC report also emphasises on the importance of picking reliable testing vendors, to ensure high quality of software.

Of course, I am using these data points from IDC, to reiterate what we at Indium already have a pulse of from various customers.

Over the last couple years, I’ve had numerous interactions with some of the world’s leading brands and the emphasis on software testing has become a key focus area.

I believe the core of our approach to servicing our global clients can be explained with this simple line: “We’re small enough to care, large enough to deliver.”

Let me also add here – this statement is not simply a tagline. It is what we believe in, very deeply, at Indium.

For us, client satisfaction is the primary metric of success and everything we do is geared towards this key metric.

But, what is important to note here is that, over the last 17 years, we’ve steadily grown to become a significant player in the software testing market.

Our team has grown to 450+, led by an experienced management team with strong understanding of software testing, coupled with global sales presence.

Most importantly, we’ve successfully executed several top-notch, long-term projects across industries including banking & financial services, technology, gaming, media, education, healthcare, life sciences and retail.

We are servicing customers ranging from large enterprises to start-ups and our ability to be nimble enough to deliver value to this spectrum of customers is why customers continue to engage with Indium.

Irrespective of the size of the customer or the project, we always have only an A-team handling the customer. This is what gives credence to our motto ‘we’re small enough to care, large enough to deliver.’

In line with this philosophy, let me use this space to explain Our Four Key Pillars that allow us to deliver with top-notch quality.

Pillar #1: Our IP Test Automation frameworks

Indium’s ‘iAccelerate’ suite of IP-led testing frameworks and tools include iSAFE, iFACT, iMobi, iAVA and iScoute.

These proprietary test accelerators integrate seamlessly with all testing tools and support all scripting languages.

Our frameworks are highly customizable and portable.

More importantly, it helps our clients reap the benefits of test automation from an ROI standpoint.

All our frameworks are capable of enhancing test coverage, running target-based parallel execution and efficiently analysing the root-cause. The frameworks also enhance the bug reporting processes, effectively reducing overall time to market.

Over the next few years, we’ll have several areas, where we’ll develop deep expertise and build scale.

Pillar #2: Market Leadership in Niche Areas

One key trait, we believe, is our ability to specialize in niche areas and build expertise.

To substantiate my statement, we’re among the Top 4 global players in Game Testing Services. Our game testing lab in Bangalore is well-equipped with over 250 devices and an army of experienced game testers.

It is complete with systems, processes and high-quality program management to serve the largest gaming companies in the world.

Moreover, our comprehensive QA solution for eCommerce business garners significant interest globally.

We are excited with the software testing capabilities that we have built on Supply Chain Management (SCM), Point of Sale (PoS) and Promotion Systems.

Lastly, our testing solution around Cloud, Mobility and Big Data technologies is a critical aspect of our growth strategy.

We believe that we are well positioned with our ‘IP led QA solutions’ to create a strong value chain for our customers in this space.

Needless to say, we’re constantly investing to scale these practices further.

Pillar #3: Testing is What We Love & What We Do

Another one of our key differentiators is our laser-like focus on testing and nothing else.

We strive to deliver excellence, bring in the best practices to ensure our customer’s software quality initiatives are completely met and overall, build a strong brand in the software testing industry.

Over the past 17 years, we’ve successfully served over 250 clients, with utmost satisfaction.

Pillar #4: Stability & Capability

Stability is one of the key factors that has enabled us to build our brand image.

We established a robust organic growth strategy.  Our team has grown to 450+, led by an experienced management team with a strong software testing background coupled with global sales presence.

Our multiple delivery centres enable us to work closely with our customers across the world, on terms suitable to them, thus enhancing the comfort of working with us.

Also, from a capability perspective, we’ve a unique consultative and solution based approach to serving our customers with

• A strong program management model
• Best-in-class Quality Management Processes & tailored solutions
• Dedicated CoE to deliver managed services round the clock

The post Small Enough to Care, Large Enough to Deliver appeared first on Indium.