According to research conducted by Statista, global e-commerce sales are projected to reach $6.38 trillion by 2024. This staggering figure highlights the immense potential of the e-commerce industry. However, to tap into this growth and succeed in the highly competitive market, businesses must prioritize their digital customer experience quality.

One of the primary challenges e-commerce platforms face is the ever-increasing customer expectations. Consumers demand seamless and personalized experiences across various touchpoints, including mobile apps. Any glitch or poor performance in an e-commerce app can lead to frustration, cart abandonment, and negative reviews, harming the brand’s reputation.

Embracing the Change: The Need for Digital Assurance:

To address the challenges the e-commerce industry faces and provide delightful customer experiences, businesses must embrace digital assurance. Digital assurance refers to testing and assuring the quality of digital systems, including mobile apps, web platforms, and other digital touchpoints. It encompasses various testing activities, such as functional testing, performance testing, usability testing, security testing, and compatibility testing.

Research by Gartner reveals that 83% of consumers are willing to share their data in exchange for a personalized shopping experience. This highlights the importance of delivering customized experiences through well-designed and thoroughly tested e-commerce apps. By adopting digital assurance practices, businesses can identify and resolve issues early in the development cycle, preventing costly and reputation-damaging incidents.

Digital assurance offers a proactive approach to ensure that e-commerce apps function as intended, meet customer expectations, and are reliable, secure, and user-friendly. By implementing robust testing strategies, businesses can gain confidence in their app’s performance, security, and usability. This instills trust in customers, who appreciate a seamless shopping experience.

The Value of Digital Assurance

Implementing digital assurance as a practice in e-commerce app development makes a lot of sense. Here’s why:

Enhancing Customer Experience: Digital assurance helps businesses deliver seamless and enjoyable customer experiences by identifying and eliminating bugs, performance bottlenecks, and usability issues. According to a study by Forrester, 66% of online adults agree that valuing their time is the most important thing a company can do to provide them with good online customer service. By thoroughly testing their apps, businesses can ensure that every interaction is intuitive, responsive, and error-free, increasing customer satisfaction and loyalty.

Maintaining Brand Reputation: In today’s digital age, a single negative experience can spread rapidly through social media and online reviews, tarnishing a brand’s reputation. Digital assurance is crucial in safeguarding brand equity by minimizing the risk of app failures, data breaches, or compromised security. Research from Trustpilot states that 86% of consumers hesitate to purchase from a business with negative online reviews. By prioritizing quality through rigorous testing, businesses can build customer trust and confidence.

Improving Conversion Rates: A well-tested e-commerce app that provides a seamless user experience can significantly impact conversion rates. By eliminating barriers to purchase, such as slow loading times, checkout errors, or broken functionalities, businesses can improve their conversion rates and drive more sales. According to Baymard Institute, the average documented online shopping cart abandonment rate is 69.57%. Effective app testing can help reduce this abandonment rate by ensuring a smooth and hassle-free buying process.

Optimizing Performance: Digital assurance enables businesses to identify and address performance bottlenecks, ensuring their e-commerce apps can handle high traffic volumes during peak periods. Businesses can avoid downtime, improve scalability, and provide a consistently excellent user experience by optimizing performance and load testing. Research from Akamai Technologies reveals that a one-second delay in website load time can lead to a 7% decrease in conversions. Therefore, ensuring optimal app performance maximizes customer satisfaction and revenue generation.

Staying Ahead of Competitors: In a fiercely competitive e-commerce landscape, businesses prioritizing digital assurance gain a competitive advantage. By delivering a flawless app experience, businesses can attract and retain customers, outperform competitors, and establish themselves as leaders in the industry. Research by Gartner indicates that by 2023, organizations prioritizing superior customer experience will outperform competitors by more than 25% in customer satisfaction metrics.

Conclusion

As the e-commerce industry evolves, businesses must adapt to the changing landscape and deliver exceptional digital customer experiences. By embracing digital assurance practices and investing in comprehensive app testing, businesses can enhance their e-commerce apps, improve customer satisfaction, and drive business growth.

With digital assurance, businesses can proactively address issues, optimize performance, and maintain a strong brand reputation in a highly competitive market. By prioritizing quality and continuously improving their e-commerce apps, businesses can seize the opportunities presented by the digital revolution and thrive in the ever-changing e-commerce landscape.

Remember, in the digital world, where customer expectations are soaring, a seamless and delightful app experience can make all the difference between success and failure. By investing in app testing and digital assurance, businesses can enhance their digital customer experience, build trust, and secure a competitive edge in the fast-paced world of e-commerce.

The post Optimizing Digital Customer Experience through Effective App Testing appeared first on Indium.

From IoT sensing to cloud

The IoT sensing architecture consists of three main components: sensors, gateways, and cloud platforms.

Sensors are physical devices that capture data from the environment. They can be wired or wireless and come in various forms, such as temperature sensors, humidity sensors, pressure sensors, and motion sensors. Sensors also can include cameras, microphones, and other types of sensors that capture data in different formats.

Gateways are the intermediate devices that receive data from sensors and transmit that data to a cloud platform for processing and analysis. Gateways can perform data filtering and aggregation, as well as provide security and connectivity to different types of networks.

Cloud platforms are the central systems that receive and process data from sensors and gateways. Cloud platforms can store data, run analytics, and provide dashboards and visualizations for end-users. Cloud platforms can also integrate with other systems, such as enterprise resource planning (ERP) or customer relationship management (CRM) systems

Here are a few examples of IoT sensing protocols

• MQTT – One commonly used protocol for IoT sensing is the MQTT (Message Queuing Telemetry Transport) protocol. MQTT is a lightweight, publish-subscribe messaging protocol designed for IoT applications with low bandwidth, high latency, or unreliable networks.

• CoAP – It can be used for IoT sensing CoAP (Constrained Application Protocol), which is designed for constrained devices and networks. CoAP uses a client-server model, where the client sends requests to the server and the server responds with data.

An example of using CoAP for IoT sensing could be in a smart agriculture system. Let’s say we have a soil moisture sensor in a field that sends data to an IoT platform using CoAP. The sensor would send a request to the CoAP server on the platform, asking for the current soil moisture level. The server would initially respond with the data, which the platform could use to determine if the crops need watering.

There are many other protocols that can be used for IoT sensing, including HTTP, WebSocket, and AMQP. The choice of protocol depends on the specific requirements of the application, such as the level of security needed, the amount of data being transmitted, and the network environment.

Also read: IoT Testing Approach on Devices

IoT Sensing Use Cases

IoT can be used in a variety of applications, such as Smart Homes and buildings, Industrial Automation and Healthcare.

• In smart homes and buildings, IoT sensing can be used to control heating, lighting, and ventilation system based on environmental conditions. For example, a temperature sensor can be used to adjust the heating system based on the current temperature, while a light sensor can be used to adjust the lighting system based on the amount of natural light.

• In healthcare, IoT sensing can be used to monitor patients and improve patient outcomes. For example, a wearable sensor can be used to monitor a patient’s heart rate and transmit that data to a cloud platform for analysis. The cloud platform can then alert healthcare professionals if the patient’s heart rate exceeds a certain threshold.

IoT Sensing and Predictive Maintenance

The Internet of Things (IoT) has revolutionized predictive maintenance by enabling real-time monitoring and analysis of equipment and systems. By deploying IoT sensors, organizations can collect data on various factors, including temperature, vibration, energy consumption, and more. This data is analyzed using machine learning algorithms to identify patterns and anomalies that can predict equipment failure.

Predictive maintenance in IoT sensing has numerous benefits, including

• Reduced Downtime: By predicting equipment failures before they occur, maintenance can be scheduled during planned downtime, and reduce the impact on operations.
• Lower Maintenance Costs: Predictive maintenance allows organizations to replace or repair equipment before it fails and reduce the need for emergency repairs and overall maintenance costs.
• Increased Efficiency: By monitoring equipment in real-time, organizations can identify inefficiencies and optimize operations to reduce energy consumption and increase efficiency.
• Improved Safety: Predictive maintenance can identify potential safety issues before they become a hazard, reducing the risk of accidents and injuries

For example, consider a fleet of trucks used for transporting goods. Each truck is equipped with IoT sensors that collect data on factors such as speed, fuel consumption, and engine performance. This data is analysed using predictive maintenance algorithms to identify when maintenance is required. For example, if the algorithm detects a decrease in fuel efficiency, it may predict that the engine needs to be serviced. The maintenance team can then schedule a service appointment before the engine fails, minimizing the risk of costly breakdowns and repairs.

Condition Monitoring and prognostics Algorithms

It is used in a variety of industries to evaluate the efficiency and state of machinery and systems. Condition monitoring is the process of keeping an eye on the health of machinery or other equipment to spot any changes that could indicate a potential problem. Prognostics, on the other hand, is the process of determining an equipment’s or system’s future health using the data gathered during condition monitoring.

Algorithms for condition monitoring and prognostics are generally required to maintain the effectiveness and functionality of equipment and systems. By utilising these algorithms, organisations can reduce the risk of equipment malfunction or damage, reduce downtime, and boost productivity.

Conclusion

IoT sensing, to sum up, is a rapidly expanding field that uses sensors and other smart devices to gather and transmit information about the physical world to the internet or other computing systems. Organisations and individuals can make better decisions and achieve better results by using the data collected by these sensors to provide insights into a variety of processes and environments

The post The Ultimate Guide to Understanding IoT Sensing: Everything You Need to Know appeared first on Indium.

Digital transformation is not just about adopting new technologies; it’s a holistic approach that involves rethinking business processes, customer experiences, and organizational culture. It’s about leveraging digital technologies to create new opportunities, optimize operations, and deliver value to customers in innovative ways.  

Using the lens of digital assurance and digital engineering, we hope to further illuminate the idea of digital transformation in this blog. The blog will specifically emphasize digital engineering and assurance while highlighting their role in digital transformation. 

Let’s begin! 

The Importance of Digital Transformation 

The importance of digital transformation in today’s business landscape cannot be overstated. Here are some key reasons why enterprises must prioritize digital transformation to stay relevant and competitive: 

• Resilience: The pandemic has highlighted the need for businesses to be resilient and adaptable to changing circumstances. Digital transformation enables enterprises to build agility into their operations, processes, and customer interactions, making them better equipped to navigate disruptions and uncertainties. Example: During the pandemic, many companies had to adapt their business models to survive. Restaurants that implemented online ordering and delivery services were more resilient than those that didn’t, as they were able to continue serving customers even during lockdowns. 

• Customer-centricity: Customers today demand seamless, personalized, and digital experiences. Digital transformation allows enterprises to leverage data, analytics, and automation to understand customer needs, preferences, and behaviors, and deliver hyper-personalized experiences that drive customer loyalty and retention. For Example: Amazon is known for its hyper-personalized customer experiences, with personalized recommendations based on purchase history and browsing behavior. This helps drive customer loyalty and retention, as customers feel understood and appreciated by the brand. 

• Innovation: Digital transformation fosters a culture of innovation within organizations, empowering employees to think creatively, experiment with new ideas, and drive continuous improvement. It enables enterprises to explore new business models, revenue streams, and markets, unlocking new growth opportunities. Tesla is known for disrupting the traditional automotive industry by introducing electric cars and self-driving technology. This innovation has enabled them to capture a significant share of the luxury car market and expand into other markets like energy storage.  

• Efficiency: Digital transformation streamlines operations, automates repetitive tasks, and eliminates manual errors, resulting in improved operational efficiency and cost savings. It enables enterprises to optimize their processes, reduce overheads, and enhance productivity, driving better business outcomes. Banks have embraced digital transformation to improve efficiency in their operations. For example, many banks now offer mobile banking apps that allow customers to deposit checks, transfer funds, and pay bills without visiting a physical branch, saving both time and money for both the bank and the customer.  

• Competitive Advantage: In today’s hyper-competitive business landscape, digital transformation is no longer optional; it’s a strategic imperative. Enterprises that embrace digital transformation gain a competitive edge by staying ahead of the curve, adapting to market changes faster, and delivering superior customer experiences. Example: Netflix disrupted the traditional TV and movie industry by introducing a subscription-based streaming service that offers personalized recommendations and original content. This has allowed them to gain a competitive advantage over traditional cable and satellite TV providers, as they are able to offer more value to their customers at a lower cost. 

Read our success story on Implementing Critical and Inclusive Testing Methods To Accelerate The App Development Lifecycle For Complex Retail Applications. 

Definition of Digital Assurance and its effects 

In layman’s words, digital assurance is a collection of QA (Quality Assurance) practices that guarantee seamless communication between various digital ecosystem components. Digital ecosystems include cloud computing, online analytical processing, and even social networking. 

Data management and data systems are also essential components of any digital ecosystem. Effective data management involves collecting, storing, processing, and analyzing data in a secure and organized manner. Digital ecosystems generate vast amounts of data, and having the right data systems in place ensures that businesses can effectively use this data to make informed decisions and drive growth.  

For example, online retailers like Amazon use data systems to track customer behavior and purchase history, allowing them to provide personalized recommendations and offers. Another example is healthcare organizations that use data management systems to store and analyze patient data to improve diagnoses and treatment plans. In both cases, effective data management and systems play a critical role in optimizing digital ecosystems and driving business outcomes. 

How Digital Assurance Helps in Digital Transformation? 

Digital Assurance plays a crucial role in supporting successful digital transformation initiatives for enterprises. As organizations strive to embrace new technologies, processes, and business models, Digital Assurance provides a comprehensive framework to ensure that the digital assets are reliable, secure, and aligned with the desired objectives. Here are some ways in which Digital Assurance helps in driving digital transformation: 

• Digital Assurance ensures the quality and reliability of digital solutions. Through comprehensive testing and validation, Digital Assurance identifies and addresses potential issues, bugs, or vulnerabilities in digital assets, thereby minimizing risks of system failures, security breaches, or customer dissatisfaction. By ensuring that digital solutions are functioning optimally, Digital Assurance enables organizations to deliver seamless, user-friendly experiences to customers, employees, and other stakeholders, fostering their adoption and engagement with digital technologies. 
• Digital Assurance promotes innovation and agility in the digital transformation journey. By continuously testing and validating digital assets, organizations can identify opportunities for improvement, innovation, and optimization.  
• Digital Assurance allows for rapid iterations, testing of new features or functionalities, and experimentation with emerging technologies, enabling organizations to stay agile and adaptive in the dynamic digital landscape. This helps organizations to respond quickly to changing customer needs, market trends, and business requirements, and stay ahead of the competition. 

Digital Engineering: Definition & Its Impact 

Digital engineering is a comprehensive approach to design that utilizes models and data instead of documentation. This technique involves integrating data across various models and transforming the culture of project teams. By doing so, digital engineering can significantly reduce the risk associated with building costs and timelines. 

How Digital Engineering Helps In Digital Transformation? 

It goes beyond traditional software development, focusing on building robust, scalable, and innovative digital assets that drive business outcomes. Here are some key reasons why Digital Engineering is vital to digital transformation: 

• Digital Engineering is a powerful tool that enables organizations to develop cutting-edge digital products and services that meet the constantly evolving demands of customers. For instance, a bank might use AI to create a chatbot that provides personalized financial advice to customers, while a retailer might use Big Data to analyze customer behavior and tailor their offerings accordingly. 

• By leveraging Digital Engineering, organizations can stay ahead of their competitors in the rapidly changing digital landscape. For example, a car manufacturer might use IoT technology to create a connected car that offers new features and services to customers, thereby differentiating itself from its competitors. 

• Digital Engineering fosters agility and flexibility in the development and deployment of digital solutions. For instance, an e-commerce company might use Agile methodology to develop its website and continuously improve its user experience based on customer feedback. 

• Digital Engineering methodologies such as DevOps and CI/CD enable organizations to rapidly design, develop, and deploy digital assets. For example, a software company might use DevOps to automate its software development and deployment processes, thereby reducing errors and accelerating time-to-market. This agility is essential for organizations looking to drive digital transformation and adapt to the constantly changing needs of customers and market conditions. 

To sum up, digital transformation is now a vital part of an enterprise’s ability to succeed in the modern world, particularly in the aftermath of the pandemic. Rather than a trendy phrase, it is a critical strategic element that organizations must adopt to remain pertinent, competitive, and adaptable to changing circumstances. 

The post Digital Assurance and Digital Engineering – The pillars of Digital Transformation appeared first on Indium.

What is Exploratory Testing?

Since exploratory testing is “investigative, experimental, or fact-finding,” as the name implies, anyone can perform it. Initial exploratory testing will be carried out by all new onboarding personnel to aid in their understanding of the application flow and other behaviour. Always recommend starting with exploratory testing.

Typically, exploratory testing is where testers, developers, and other team members begin to understand the product. Everyone will be able to comprehend the product’s behaviour and flow thanks to this. which facilitates the rapid collection of opinions and suggestions for more effective product improvement.

Exploratory testing is the best option to cover every aspect of the application.

When can you Use Exploratory Testing?

When the delivery of a requirement is urgent and the tester is without test cases and scripts to execute. In this case, exploratory testing is crucial to meeting the business needs and delivering high-quality results.

Types of Exploratory Testing

Freestyle Exploratory Testing

Freestyle exploratory testing is the most fundamental type of testing; instead of writing traditional test scripts, test cases, and test plans, we should come up with some test ideas that we can use to test the products. The products can be tested by users who provide random inputs. 

This test allows testers with limited product knowledge or testers without any product knowledge to learn about the Products as they test them.

This testing method helps in finding defects that are not able to find in traditionally.

Scenario-based Exploratory Testing

To create a product free of bugs, real user scenarios must be applied to the application. Before exploring the application/product in any way to match those scenarios, testers first prepare scenarios. The tester must run as many scenarios as they can in order to maximise test coverage.

This type of testing is more like a human approach toward the product, which helps to find real-time scenario-based defects.

Strategy-based Exploratory Testing

This testing is usually assigned to senior people on the team or who have good knowledge of software testing and are familiar with software testing techniques like risk-based technique, equivalence technique, and boundary value analysis technique to identify more unique and challenging bugs.

Strategy-based exploratory testing acts as a great complement to functional testing when it is difficult to write test cases for specific features or to assess the overall user experience.

Read this blog: chaos engineering and resilience testing as well.

The top few tools for exploratory testing are listed below.

Exploratory Testing Benefits

• No documentation is required before starting test activities.
• No Test design or Test case is required to start.
• We get Quick feedback on the application.
• It helps to dig in where automation cannot.
• Critical Bugs can be discovered.
• Users can learn in real-time.

Conclusion

Exploratory testing is not a conventional method of software testing, but it is a very effective way to test a product or piece of software. It permits testers, BAs, Scrum Masters, product owners, and other team members to think creatively, push the envelope, and develop real-time test scenarios for issue detection. Due to its amorphous nature, exploratory testing can be applied to any procedure that only requires minimal documentation, regardless of whether it uses agile methodology, the waterfall model, or another software development life cycle prototype.

The post Ultimate Guide for Exploratory Testing appeared first on Indium.

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.

However, though businesses are flooded with internal and external data, there is no guarantee for data quality. The data is fragmented and often duplicated across the organization. In some cases, it is also incomplete, inaccurate, and unreliable. A McKinsey report on data in the pharma industry shows that increasing data complexity will throw up several challenges such as lack of quality and inherent bias, limiting the application and usefulness of real-world evidence.

This is a challenge, as data needs to be reliable for making meaningful decisions and determining its effective usage. This makes testing of data for quality critical and imperative for data handling at every stage of the process- right from collecting data and analyzing it to prepare it for presenting the data to the stakeholders. The quality of data will influence the quality of decision-making, making data verification and validation crucial.

Data validation, however, is often skipped due to the common perception that it delays the pace of transformation. While in the short term, this approach may seem beneficial, in the long run, it leads to inaccuracies, repetitions, and other data-related issues, which will impact decision-making and render the transformation ineffective or of limited use.

Instead, businesses can adopt automated validation processes through which the pace of data validation can be quickened to test the data quality for faster digital adoption.

The Need for and Types of Data Validation

Data validation for accuracy, clarity, and integrity mitigates the risk of potential defects in the digital transformation project. But along with testing the quality of data, validating the data model is also important as any poorly structured data model can throw up unforeseen issues when software and apps access data files.

To prevent this, businesses must run a variety of tests to validate the data and the data model. Some of the foremost aspects to be validated are data consistency, integrity, format standardization, data type, uniqueness, and ensuring there are no null values.

Format standardization will help to improve the structure of the data models and ensure compatibility with the applications for which the data is being used. Another standardization is with reference to the file formats for easy mapping and access.

Data validation can be done using scripts where codes are written to compare data values and structure with defined rules to ensure that the information meets the quality parameters. However, this requires a knowledge of coding and can be time-consuming for large volumes of data with an element of complexity. Open-source and proprietary tools are available and can be selected based on the business objectives and how the features of the tools serve those needs.

The End-to-End Data Validation Process

McKinsey recommends a framework-based approach to data validation. Data quality testing must be incorporated at every stage of the data workflow and must include the three steps of determining the sample data, validating the database, and validating the data format.

Selecting the sample data is a critical step for large volumes of data. Getting the size and the error rate right will impact the project’s success. For validation of the database, determine the unique IDs and the number of records needed for comparing source and target data fields. Data format validation should check for incongruent and duplicate data, null field values, and mismatched formats.

A clearly defined set of rules will ensure that only the correct type of data, in the right format or a specified range is accepted. Where applicable, one should use a valid list of standardized values such as postal codes and industry codes for data validation. Businesses should also check for consistency and uniqueness to avoid duplication and ensure no fields are left blank.

A batch control audit is a commonly used in-built data quality testing process to check the correctness of batches of input data, especially in the data-preparation stage. Batch control can be of two types:

Sequence Control: The records in a batch are numbered sequentially to ensure the presence of each record during data validation.

Hash Totals: Selected field values in each record are totaled or the total number of records counted and checked at the time of data validation. These are also called hash totals.

Check out our Success Story. For A Leading Mobile Engagement Provider, Big Data Security Using Kerberos

Indium Digital Assurance with Data Quality Testing

Indium Software is a cutting-edge technology company that integrates digital engineering with quality assurance to provide businesses with end-to-end solutions for improving their business outcomes quickly and efficiently. We work closely with our customers to understand their business objectives, data sources, and the current infrastructure to create bespoke digital transformation solutions and meet the desired end-state.

Our digital assurance stack integrates quality assurance best practices with quality engineering, future tech testing, and advisory services. Data assurance and ETL testing form a crucial part of the overall solution with automation to accelerate the process and deliver quick and effective results.

Indium brings to the table more than 20 years of experience in QA and Testing and a team of more than 400 SDETs (Software Development Engineers for Testing).

Indium has a well-established digital and data engineering practice that is growing fast and has helped us be counted as a trusted partner for the world’s fastest-growing companies in digital assurance. Through increased automation, productivity, and quality, we offer a value-driven transformation from QA to QE.

Our data assurance capabilities include:

• Ensuring that the data is fit for purpose and the qualified data is extracted, transformed, and loaded from the source into each layer with data lake/data warehouse testing
• Ensuring data completeness for data validation including app modernization/digital transformation
• Ensuring data consistency, appearance, and performance
• Performing data validation of the underlying data models/data when migrating from legacy infrastructure to a cloud-based platform

FAQ’s

The post Getting Your Data Validation Right with Quality Testing for Digital Assurance appeared first on Indium.

The Challenges Faced by Users with Disabilities

Individuals with disabilities face a unique set of challenges when using technology. These challenges can range from physical disabilities, such as blindness or mobility impairments, to cognitive disabilities, such as dyslexia or ADHD. Software that is not designed with accessibility in mind can create significant barriers for these users, limiting their ability to access the same information and services as others.

Importance of Testing for Accessibility

Testing for accessibility is an essential part of the software development process. It ensures that software is designed with accessibility in mind and that users with disabilities can access and use it effectively. Accessibility testing involves identifying and addressing any barriers that may prevent users with disabilities from accessing or using the software. These barriers can include issues with colour contrast, font size, keyboard navigation, and screen readers.

Also Read:  Why QAOps has become an Inevitable process in Software Delivery

The Role of Digital Assurance in Accessibility and Inclusion

Digital Assurance plays a critical role in ensuring that software is accessible and inclusive for users with disabilities or special needs. Here are some techniques that can be used:

1. Screen reader testing: Screen readers are software applications that allow visually impaired users to access digital content. Testers can use screen readers to check the accessibility of a website or application.
2. Color contrast testing: Color contrast is an important factor in making digital content accessible to users with visual impairments. Testing the color contrast of text and images is crucial to ensure that the content is easily readable for everyone.
3. Keyboard-only testing: Some users with disabilities may not be able to use a mouse or touch screen. Testing with only a keyboard can help identify any barriers that might prevent keyboard-only users from accessing and using the digital product.
4. Focus testing: Testing the focus order of interactive elements such as links, buttons, and form fields is important to ensure that users with disabilities can navigate through the digital product using keyboard or screen reader.
5. Captioning and transcription testing: Captioning and transcription are important for making audio and video content accessible to users who are deaf or hard of hearing. Testing the accuracy and quality of captions and transcriptions is crucial for ensuring accessibility.
6. User testing with diverse participants: It is important to test the digital product with a diverse group of users, including people with disabilities. Observing and gathering feedback from these users can provide valuable insights into how accessible and inclusive the digital product is.
7. Automated accessibility testing: There are many tools available that can automatically test the accessibility of digital products. While automated testing is not a replacement for manual testing, it can help identify potential accessibility issues early in the development process. Some of the tools are:                                

• Axe: Axe is a free accessibility testing tool that integrates with popular web browsers, such as Chrome and Firefox. It provides detailed reports on accessibility issues and recommendations for fixing them.
• WAVE: WAVE is a web accessibility evaluation tool developed by WebAIM. It can be used to evaluate web pages for accessibility issues, and it provides visual feedback and suggestions for how to fix them.
• AChecker: AChecker is an online accessibility checker that can be used to scan web pages for accessibility issues. It provides detailed reports on accessibility issues and recommendations for fixing them.
• Tenon: Tenon is an automated accessibility testing tool that can be used to scan web pages and software applications for accessibility issues. It provides detailed reports on accessibility issues and recommendations for fixing them.
• Accessibility Insights: Accessibility Insights is a free accessibility testing tool from Microsoft that can be used to evaluate web pages and software applications for accessibility issues. It provides detailed reports on accessibility issues and recommendations for fixing them.

Overall, using a combination of these techniques can help ensure that digital products and services are accessible and inclusive for everyone.

Case Study

One great example is Apple, that has made significant strides in accessibility and inclusion testing for users with disabilities or special needs.

It has been said that Apple’s VoiceOver feature, a screen reader that enables users who are blind or visually impaired to use their Apple devices, has helped the company gain market share among those who are blind or visually impaired. In the United States, only 5% of people who are blind or visually impaired use Android phones, according to a 2012 report by the National Federation of the Blind.

Similarly, Apple’s Switch Control feature, which allows users with physical disabilities to control their device using external switches or a keyboard, has also been praised for its accessibility. In a 2017 study conducted by the research firm Fluent, 81% of disabled users reported that they were likely to purchase an Apple product because of its accessibility features.

Conclusion

Digital Assurance engineers can help in identifying and addressing accessibility barriers in software. Collaboration between Software testers, design and development teams is also essential to ensure that accessibility is integrated into the software’s design and functionality. By making software accessible and inclusive, we can ensure that all users can access the same information and services, promoting greater equality and inclusivity for all.

The post The Role of Digital Assurance in Accessibility and Inclusion appeared first on Indium.

To create policies and procedures that promote the greatest possible patient outcomes, quality assurance (QA) teams at healthcare facilities work across the system. That entails making sure that a wide range of rules, guidelines, and laws at the federal, state, and local levels are followed, as well as devising internal strategies to promote the provision of high-quality healthcare and the general wellbeing of the community the organization serves.

What is healthcare administration ecosystem?

The management and direction of healthcare organizations, such as hospitals, clinics, and other forms of healthcare institutions, are referred to as healthcare administration. To provide healthcare services to the public, a variety of various organizations and stakeholders collaborate within the healthcare administration ecosystem.

These stakeholders include the administrative employees who manage the daily operations of healthcare institutions as well as healthcare providers like doctors and nurses. The insurance industry, governmental entities, and regulatory bodies are all part of the healthcare administration ecosystem, and they all contribute to the development and smooth operation of the healthcare system.

How do organizations use healthcare administration ecosystem

The healthcare administration ecosystem is used by healthcare organizations to plan and control the provision of healthcare services. This involves overseeing the different divisions and jobs performed by a healthcare facility, including clinical care, accounting, human resources, and information technology. Healthcare administrators also make sure that the facility is meeting the needs of the community and patients, as well as all applicable laws, regulations, and standards.

Additionally, healthcare organizations can work with insurance firms, regulatory bodies, and other healthcare organizations to enhance the effectiveness and quality of care by utilizing the ecosystem for healthcare management. For instance, a hospital might collaborate with an insurance provider to create a patient payment schedule or with a regulatory body to make sure the institution complies with all relevant safety and quality standards.

Overall, the delivery of healthcare services depends heavily on the complex and interrelated healthcare administration environment. Healthcare providers can provide better care to their clients and the public at large by cooperating with one another.

What are the issues with healthcare administration ecosystem based on legacy technologies?

When healthcare companies rely on old technologies inside the healthcare administration ecosystem, several problems may occur.

One problem is that older technologies might not be able to keep up with the healthcare system’s evolving needs and rising demand. For instance, the volume of data and information produced by contemporary healthcare facilities may be too much for older systems to handle, causing bottlenecks and inefficiencies. Additionally, it may be challenging to interact with or exchange data with other businesses within the healthcare administration ecosystem if legacy technologies are incompatible with more modern systems or technology.

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The fact that outdated technologies might not be safe or in compliance with present laws and standards is another problem. Data breaches and cyberattacks are more likely as the healthcare sector grows more digital, endangering patient privacy and endangering the business. Legacy technologies might not be protected against these attacks by the proper security measures, which could result in regulatory penalties and reputational harm.

Finally, maintaining and upgrading legacy technologies can be costly because they may need specialized resources and expertise that are hard to come by. It may be difficult for the companies to invest in more cutting-edge and efficient technologies as a result of this drain on resources.

What are the needs of a modern-day healthcare administration ecosystem using QA and a data segregation framework?

Using a quality assurance (QA) programme with data segregation architecture can help meet several needs of the contemporary healthcare administration ecosystem, including the following:

A QA program can assist healthcare organizations in locating and addressing areas where patient care delivery can be improved. This could entail gathering and examining data on patient outcomes, running audits and reviews, and putting right any problems that are found. Sensitive patient data can be protected by healthcare organizations by being divided into several categories or “segments” according to the level of sensitivity. This can lessen the likelihood of data breaches and help prevent unauthorized access to sensitive information.

Healthcare organizations can comply with the requirements of numerous laws, regulations, and standards, such as the Health Insurance Portability and Accountability Act, by using both QA programmes and data segregation frameworks (HIPAA). These programmes can assist healthcare businesses in operating more effectively and cost-effectively by detecting and correcting inefficiencies and waste. 

Overall, the QA programmes and data segregation frameworks can assist contemporary healthcare administration ecosystems in providing patients with care that is higher-quality, more effective, and more cost-effective while also addressing the needs of all stakeholders in the healthcare ecosystem.

The Success Story

The client is a leading global provider of advisory solutions for health plans and a range of value-added services in the healthcare technology sector. Utilizing technology to provide solutions that ease business administration procedures, healthcare partner management, and automated medical care is the key to their services.

What were the issues with our client’s administration ecosystem?

• The client’s business-admin management portal was updated from the legacy systems, which had problems with data operations in siloed databases for distinct healthcare partners and their associated members.
• The system was upgraded with a multi-tenancy structure and segregated databases that demonstrate efficient querying and analysis.
• The Data Integrity and Security were in jeopardy during the system upgrade process and necessitated extensive Validations.

What are the business requirements?

• To confirm that the Data Segregation Framework is suitable for mapping access privileges and possible data breach escalations.
• Verify the data integrity of all aspects of the business flow, including on boarding, profiles, health plans, and the business rules that govern them.

How we did it

• To make sure there are no gaps in test coverage, a strong test strategy was defined and then carried out in accordance with the business flow.
• Developed a reliable test design requires in-depth knowledge of the Data Segregation Framework at the functional and database levels.
• In-depth business rules were included in the workflow’s on boarding process and customizations based on plan choices, including setting up benefit amounts and caps, restricting shipments, activating emergency plans, etc.
• Logical QA checks on the application of “security filters” for Partner group access. This QA criteria was fulfilled by performing user hierarchy-based tests; only the right partners and their members shall have access to information that is relevant to them.
• Information Look-up Integrity has been confirmed for the correct fetch from the data tables for all information request permutations.
• Conducted responsiveness and data-manipulation tests.

What are the results we have delivered?

• Since data segregation is a process-intensive exercise, knowing the data orchestration in and the numerous business rules in the end-to-end workflow were part of Indium’s test strategy.
• Beyond just data validations, Indium’s CoE evaluation of the entire project resulted in functional improvements that the client effectively adopted and applied.
• Demonstrated domain expertise and infused the right flavor of data security into the design of test cases.

If you are unable to find the perfect solution, we are happy to assist you in utilizing technology in your company. Contact us at (888) 207 5969 or (800) 123 1191 for more information or send an email to info@www.indiumsoftware.com.

The post How to Enhance Healthcare Applications with QA in Data Segregation Frameworks: Indiums’ Expertise 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.

The company’s reputation will almost certainly be ruined if it releases a product with numerous coding mistakes and performance concerns. That’s why it’s critical to keep track of, evaluate, and improve the tool’s performance based on real-world events.

The product goes online once all of the banking web applications testing steps have been completed successfully. A financial company manager will be more confident about the app’s performance in the real world after testing it. This is ensured when all bank application testing areas are touched upon. Let’s have a look at the major focus areas for a banking application tester.

• UX and usability & functionality — They ensure that modules work as expected (both individually and as part of a larger system). Banking professionals aren’t necessarily computer savvy. Not everyone has a solid understanding of the web when it comes to branch clients. As a result, application developers must create simple, user-friendly interfaces. Testers must check that using the product’s graphic attributes takes little time and that all of the primary services are only a few clicks away from the home page.

• Performance: On mobile devices, slow page loading and lack responsiveness are serious challenges that can negatively impact user retention rates. A tester must ensure that the product is speedy and consistent across multiple operating systems and browsers.

• Security: The majority of developed countries are tightening their web security controls. As a result, businesses run the danger of violating basic security rules. A firm owner puts themselves at danger of penalties or criminal prosecution if they do not follow security directives. The goal of security testing is to ensure that the application complies with industry-standard security rules and has additional safeguards in place to ensure that it is safe in the event of a hacker attack.

Banking applications handle sensitive data, which makes them more vulnerable than other projects. Financial software is a popular target for hackers, so any organisation working in the banking industry must be vigilant about security dangers.

Read this interesting piece: Fraud Analytics for the banking sector!

A specific case: Indium’s expertise

Our client is a private wholesale bank that facilitates community lenders in New Jersey, New York, Puerto Rico and the U.S. Virgin Islands advance housing and community growth with a secure online banking application.

The banking application is designed to set/update the parameters for the day to day business process and give customers a flexible and time-saving alternative for viewing, printing and downloading reports/data.

The firm’s initial requirement was to conduct gap analysis, set QA process and define QA Roadmap. After setting- up a QA process, they wanted us to help them automate their regression test cases. Also, they wanted a thorough functional vetting against the requirements to measure completeness and create & maintain reusable test artifacts (High level test scenarios / detailed test cases).

How did we do it?

Indium software, being an expert in the field of application testing solutions in the past decade took a sensible approach to solve the firm’s application testing issues. Here is our approach:

i. Determining the key testing goals as applicable to Mission Critical and Real time Application portfolio was our major task.

ii. Recommending of best Practices and methodologies.

iii. Creating check-lists to ensure the application’s functionality and prioritize test coverage

iv. Ensuring sufficient coverage and improve the quality of test design.

v. Creating test plan that includes test schedule along with milestones.

vi. Identifying test cases for Automation.

To ensure high quality code to UAT for all applications, our experts analyzed and suggested improvements to the QA delivery process. We had peer review on test cases and built a test suite. Our Well defined test strategy tracking. Our team had provided a seamless execution of regression test suite, enormous testing coverage and captured several relevant matrices to showcase testing progress and results. Deliverables in each phase of the process, including a Testing Plan, Gap Analysis Report, Test Cases, Incidents Report and Reports on Testing Progress were provided.

Our experienced test engineers used QTP / VB Scripting to automate the functional regression test suite. To ensure reduced cycle time for testing, we recommended a test automation framework for regression testing. They built re-usable test artifacts.

Our testing expertise indicated high level Test improvement opportunities including lowering the Total Cost of ownership of QA services. We provided channelized test data management and test scenario outlines. Our testing team assessed the requirements, tools, and processes involving Load/ stress testing for the Web based applications.

We understood the Client’s QA vision, Established QA process, conducted appropriate Gap Analysis, Laid Test Strategy, Setup Test Environment, Recommended Process Improvement and QA maturity Roadmap.

During the initial phase, we uncovered many lurking defects in production environment and over a period of time, we ensured zero defect leakage, managed test scripts for continuous execution.

i. Our test automation framework helped them reduce the regression cycle time by 40%.

ii. Productivity of resources was 110% (supported during weekends as per the client’s requisite).

iii. Manual efforts was reduced by 95%

iv. Test efficiency improved by 95%

Indium’s expertise in application testing services had been proven over the last 20+ years and we intend on providing cutting-edge customer centric solutions for your every software need.

The post Testing a bank application: A Success Story appeared first on Indium.