What does 2020 has in store for Software Automated Testing

The quality of software has become the single most important element in ushering success for any enterprise. However, ensuring quality can be a big challenge and involves following the latest software automated testing trends. Also, with the advent of new technologies and apps having SMAC (Social Media, Analytics, and Cloud) interface, testing is not the same anymore.

The objectives of testing are guided by a slew of considerations. These include achieving faster time-to-market, delivering products based on customers’ feedback, and generating better ROI. Given the criticality of automated testing services, more and more enterprises are embracing the end-to-end shift-left testing methodology. Further, the emergence of Industry 4.0, driven by IoT and other technologies, has prefaced the role of software automated testing.

In the last few years, trends like Agile/DevOps have dominated the testing landscape. The year 2020 is likely to be the continuation (and consolidation) of the earlier trends, but with more sophisticated technologies, wider adoption, and better innovative solutions. Let us check them out.

The software automated testing trends to expect in 2020

Automation has continued to dominate the testing industry for the past few years. In fact, it has allowed the validation of various testing methodologies by using virtual users. Further, it can be extrapolated to test software incorporating new technologies such as AI and ML.

Artificial Intelligence and Machine Learning (AI and ML): With intelligent devices generating humongous quantum of data, artificial intelligence and machine learning can be leveraged to draw business intelligence. These technologies make testing faster, smarter, and effective.

If earlier, AI and ML were leveraged to prioritize test cases, predict the quality of tests, or classify defects, among others, 2020 will see the inclusion of more testing areas. In fact, the forecast of investments in AI and ML testing is likely to reach a whopping $200 billion by 2025.

Rise in Cyber Security Testing: The omnipresent and ever-growing security challenges concerning digital revolution have brought automated testing services w.r.t security to the fore. In fact, business stakeholders like the CIOs, CEOs, and CFOs have understood the gravity of the challenge. The thrust, therefore, in any test automation strategy is to offer total protection to the critical data. This can safeguard systems, databases, and the overall business from suffering losses or losing customer trust.

In the year 2020, DevSecOps will attract the traction of business stakeholders. In such a case, everyone in the organization shall be expected to be accountable in upholding security. Thus, the software application coming out of the SDLC will be more resilient to cyber threats. No wonder, the efforts to uphold cybersecurity will move a few notches higher.

Performance Testing and Performance Engineering: If product testing had a strong element of performance testing in 2019, then 2020 will see a greater move towards performance engineering. In the latter, there is a collaboration among various processes and elements to deliver the highest level of quality. These include software, hardware, security, performance, usability, and configuration, among others. Since delivering superior customer experiences has become critical for the success of a product, then a move towards performance engineering is likely to deliver customer delight.

IoT Testing: With automation becoming the leitmotif of the techno-driven world of ours, the use of IoT devices has been on the rise. According to an estimate by Gartner, the number of IoT devices with embedded software at their core may rise to 20.5 billion by 2020. Since smart systems are likely to drive the world of tomorrow, the embedded software inside systems needs to be validated for quality. For example, unless the sensors are validated for their quality in a self-driven automobile, the consequences can be catastrophic.

IoT testing involves the testing of devices with embedded software. This is about ensuring device authenticity, security, data integrity, compatibility, performance, scalability, and usability. The challenges faced by IoT test engineers is monitoring communication between sets of software running on various device platforms and operating systems. The challenges in forming a test automation strategy for IoT mainly revolve around the lack of expertise in testing IoT functionality.

Big Data Testing: The increasing use of digital devices is giving rise to big data. These are mostly needed to be processed in real-time to derive suitable insights including business intelligence. The usage of big data is seen across sectors like healthcare, banking, technology, retail, telecom, media, and many others. The processing of such data leads to the optimization of workflows and better decision making. Big data testing deals with a humongous quantum of disparate sets of data. As more companies embrace digital transformation, the possibility of data generated from various areas will increase. Thus, implementing an automated testing strategy around big data is likely to dominate the testing landscape in the year 2020.

Conclusion

As software becomes more complicated and interfaces with myriad third-party applications, software testing needs to follow suit. In the year 2020, automated testing will harness existing technologies as mentioned above to deliver high-quality products. To create new-gen products, enterprises should use the next level of testing trends in the year 2020.

This article is originally published on readdive.com

Why the Media Industry needs QA Automation Services?

Like other industry segments, the media industry is transforming to improve the quality of its services. In a fast-changing world, disseminating information in real-time can be a challenge. The media has to be at the forefront of everything as an entire global ecosystem related to the world of economics, politics, foreign policy, and many others is dependent on it. So, unless the media dishes out accurate information seamlessly in double-quick time, the implications for the ecosystem could be immense.

Importantly, media companies are facing serious cost escalation in delivering services. This is due to the rising competition from Over-The-Top (OTT) players and other overhead costs. However, these challenges can be addressed by identifying and leveraging the value trapped across organizations and reinvesting them to fuel new growth avenues.

The media of today uses a plethora of technologies to collect, process, and transmit information in real-time. This leaves no scope for errors as the implications can be huge. Media too is grappling with challenges that others are facing. These include working with legacy systems, which do not lend themselves to quick real-time processing. Further, the digitally savvy consumers of today use a range of devices to access information. This means the news distributed over multiple channels should be accurate, secure, and responsive. So, how to overcome such challenges that a globalized world has brought about? The answer is digital transformation induced QA automation services that make the final product dynamic, richer, responsive, and secure.

The media industry encompasses a host of techno-driven scenarios, activities, products, and services. Some of them include OTT, IPTV, set-top boxes, video transcoding, buffering, streaming, closed captioning, and video editing. To ensure their quality, software QA automation should be made part of the workflow. However, care must be taken to ensure software test automation services include the below-mentioned conditions:

·         Be technology and tool agnostic

·         Capability to run tests across devices and platforms

·         Provide an expansive test coverage area

·         Ensure quality in the quickest possible time

·         The automated QA testing tool should accommodate various file formats, payment processing methods, security protocols, or streaming scenarios.

How QA automation services can help the media industry?

The media industry is increasingly driven by changing customer behavior. It includes rising expectations among the younger generation demanding quick access to quality content in real-time, anytime and anywhere. Also, the media industry is facing various other types of challenges, which include:

·         Consumers becoming savvy to identify marketing campaigns disguised as editorial content.

·         Consumers becoming aware of the information about them being monetized by third parties. In fact, complicated privacy policies are turning consumers to media players who provide better data privacy and transparency.

The digital transformation initiatives comprising software QA automation can help media companies in addressing the above-mentioned challenges and deliver the following benefits:

·         Simplifying Operations: There is a lot of trapped value in silo-driven departments and processes. These increase the Opex budget of media companies as they end up duplicating efforts across traditional and digital channels. Media companies can become efficient, focused, and better performing by centralizing their processes. This shall simplify operations and release value for the companies to drive growth. Developing an ERP solution customized to the specific needs of the media industry can achieve the above-mentioned outcomes. And by running automation testing services, each and every feature and functionality of the solution can be validated.

·         Real-time Content Management using Data Analytics: Media companies are increasingly delivering services built around content. In doing so, they are expected to provide meaningful customer experiences. Data analytics can derive meaningful inferences about consumer preferences from a variety of channels and devices. And automated QA testing can check the data for any hidden glitch or bug and turn it glitch-free. It is only when structured and glitch-free data is analyzed that proper business intelligence can be derived. This can help media organizations to plan suitable strategies and stay up the winning curve.

·         Contextualization and Personalization: Content creators and marketers need to generate personalized content to garner consumer attention. This can be a challenge given the information overload consumers are subjected to. However, while doing this, issues of data privacy and security ought to be tackled in a transparent manner. A robust QA process can ensure if the sensitive consumer information is secure or the delivery process adheres to the existing security protocols or regulations.

Conclusion

The media industry is churning due to the advent of new technologies, growing competition, and changing customer preferences. Moreover, since disseminating content over multiple channels needs to be assured of quality and security, media companies can engage QA automation services.

How Digital Transformation can be enabled by Quality Engineering

Organizations are embracing digital technologies to give customers omnichannel experiences and scale up their business volumes. The disruptive nature of these technologies has allowed companies to shore up their capabilities for storage, processing, and analytics. Developments such as social media, mobility, analytics, IoT, and smart devices are enabling digital transformation and determining how users engage with businesses. The production demand in companies has gone through the roof thanks to the changing market dynamics and growing competition. This demand for scale has necessitated the expansion of the legacy systems and inclusion of the user ecosystem. The latter encompassing social media, personal devices, and cloud, among others expects a superior customer experience.

With shortening time to market and the need to ensure Continuous Integration and Continuous Delivery (CI and CD), the role of QA has changed. It has moved from the earlier waterfall model of ‘testing after development’ to ‘testing alongside development’ of Agile/DevOps. As customer experience has become the determining factor in the adoption of digital products, the role of QA has become critical. This is more so due to the need to validate software across interfaces, platforms, devices, browsers, and networks. With the demand for continuous builds gaining ground, the development and QA teams should go beyond the status quo. They should aim at preventing defects rather than taking the usual reactive approach. Thus, QA assumes the role of digital quality engineering. This involves the management, maintenance, and development of IT systems with enhanced quality standard.

Today, quality assurance goes beyond functional testing and covers non-functional parameters as well. These include security, usability, performance, accessibility, and compatibility. Thus, digital quality engineering is a nimble model underpinned on parameters such as being agile, intelligent, automated, and on cloud. It goes into assessing, optimizing, and ensuring customer experience (CX), every time.

How quality engineering services impact digital transformation

As digital transformation helps enterprises to reorient strategies, streamline workflows, optimize the cost of operations, and enhance quality, the software quality engineering services make the below-mentioned impact.

·         Analyzing user behavior to optimize CX quality: Analytics solutions run through user data patterns to derive business intelligence. The use of AI through automated algorithms helps in analyzing the user data on bounce rates, average time spent, or user inflow, to determine the trends. For example, webpages with high bounce rates or loading speeds can mean performance issues. Also, AI-driven QE services can analyze the user behaviour pattern and give fresh insights to developers about incorporating new features or functionalities.

·         Test Driven Development (TDD) and Behaviour Driven Development (BDD): The two popular approaches to software development require unit testing of the code before conducting other tests. The enterprise quality engineering team needs to ensure the testing process is quick. It does so by pitting quality via API validation versus UI-driven test cases. The BDD approach is geared towards ensuring acceptance outcomes. Here, the software quality engineering services implement the QA steps using automation scripts as defined by the user story. These two development approaches require better collaboration among various stakeholders - teams representing digital quality engineering, development, and management.

·         Enable ‘on demand’ usage with ‘on cloud’ model: Delivering customer experience can be a costly affair with upfront investments on infrastructure, devices, and tools. Further, add maintenance cost to the updation of browser or device variants, and you are staring at huge CapEx. This calls for moving to the cloud platform where tools and infrastructure can be dynamically provisioned based on demand. For example, the experts at any quality engineering company can execute compatibility testing by hosting automated scripts on the cloud. Thereafter, virtual machines can be provisioned with myriad OS-browser combinations to conduct the tests. Thus, QA teams can maintain a dynamic testing ecosystem that can address the changing requirements. The ecosystem can eliminate the need for infrastructure setup or license procurement.

Conclusion

Software applications are primed to provide enhanced customer experiences. They do so by interacting with the accompanying digital ecosystem - devices, browsers, operating systems, and networks, among others. The applications need to be glitch-free and overcome hurdles like evolving technologies, diverse market demands, and cost pressures, among others. QE services are increasingly being sought to deliver great customer experiences by harnessing agile, intelligent, and automated processes. They can help enterprises to stay up the competitive curve, deliver value for money, and optimize the customer experience.

This article is originally published on it.toolbox.com.

What is Software Integration Testing all about?

The software applications driving the modern digital ecosystem, in conjunction with the hardware systems, are dependent on various third-party applications and platforms. The omnichannel footprint of software means each module (and the interface between modules) needs to function smoothly to deliver the expected outcomes. This is ensured by conducting software integration testing.

One of the important characteristics of a software application are the seamless flow of information between its two ‘units’ or ‘modules’. However, the flow of information could be interrupted by the presence of glitches, which if not identified and corrected in time can make the application faulty. Thus, software integration testing helps to expose faults that lie at the interface between two integrated units. Once the individual units or modules are tested, the integration of interfaces gets validated.

To draw an analogy to this type of testing, let us consider two groups of friends who have been invited to a party. To find out if they can get along, they should be subjected to an ‘integration test.’ This is done by bringing them to a single room and observe how they interact. In a similar vein, to check if each unit of software functions seamlessly, they need to be integrated and tested. Thus, integration testing, as part of the software testing services, checks if all unit’s function in harmony. It ensures if the modules developed by different developers are working towards a singular objective.

Various types of software integration testing

The various ways to test the integration of modules are as following:

Big Bang: As one of the most common ways to test the integration of software modules, the big bang involves smashing and testing all the units together. This may keep the tester in good stead if all the tests are completed or the software project is relatively small. However, it can have its cons as well. For example, in case a glitch is identified, it would be difficult for testers to figure out the right module or unit responsible for it. To find the erring module, testers have to detach a few of them and repeat the testing till they identify the glitch. Since this approach requires the modules to be ready before testing, it can extend the turnaround time for product release.

Incremental: Here, two or more logically aligned units are tested as part of a single batch. Thereafter, other similarly aligned units are checked eventually ensuring the interface of every single unit with another is validated. It combines both the bottom-up or top-down approaches.

Hybrid, Mixed, or Sandwich: This approach combines both bottom-up and top-down type of integration testing. Here, the top and lower modules are tested simultaneously for integration thereby deriving the best results. This approach can come in handy for large projects.

Best practices for integration testing

Since most software development processes are moving towards Agile or DevOps, it needs to be seen how integration testing can fit into a CI/CD environment. The software testing services for integration should have the following best practices.

Execute integration testing before unit testing: The waterfall model of software product testing has led us to believe that fixing a glitch later in the SDLC can be costly. This is due to the fact that one doesn’t move to the next stage until the completion of the present phase. This approach, however, can be turned on its head in an Agile environment. This is because Agile offers the flexibility to change the business logic in an SDLC.

Do not confuse unit testing with integration testing: Unit testing targets the basic code and needs to be run frequently to detect bugs. On the other hand, integration testing is much more time consuming and should not be part of every build cycle. However, it may be included in the daily build.

Extensive logging of processes: Identifying and mitigating bugs in a unit test are easy. However, given the scope and complexity of integration tests spanning a number of modules, doing the same is difficult. The need is to keep a record of processes to better analyze the reasons for failure.

Conclusion

Integration testing may be expensive and time-consuming but is essential to deliver quality products in the DevOps and Agile-driven environments.

This article is originally published on dev.to.

Why Agile Testing needs to follow a new approach?

The global digital transformation journey requires the quality of processes, models, products, and services to be top-notch. To ensure accelerated growth, enterprises are embracing the Agile model and moving away from the traditional ones - waterfall, spiral, and iterative. The reason being the slowness and inadequacies of these models. Enterprises have realized the need to adopt innovative ways to offer customer delight, the final outcome to stay competitive. The agile testing approach is mainly embraced by enterprises that need their development pipeline to create continuous builds. This calls for both development and testing to be conducted simultaneously instead of the earlier silo-based waterfall method.

The need for adopting the agile testing approach has been necessitated due to the growing complexity of software applications. Today, the performance of the software is determined by the quality of its interfaces with third-party applications, browsers, devices, and operating systems. The more complex a software is, the more comprehensive testing it demands. Agile testing is about shift-left testing where planning, designing, writing, and testing code becomes part of the sprint. It is a collaborative approach instead of the waterfall’s silo-driven approach and drives better outcomes. Agile testing involves taking continual feedback from the customers and stakeholders.

Why is agile software testing advantageous?

The complexity of software applications has shifted the focus on testing. This is to ensure the applications live up to the expectations of both businesses and customers. The agile testing approach focuses on smart testing through automation rather than using the time-consuming manual means. Here, the testers and developers follow a collaborative approach with the former offering timely feedback to the latter. This makes the final product fully aligned to the customers’ requirements or expectations.

Principles of Agile Testing

Agile application testing follows a set of principles to deliver glitch-free products to the end-customers:

Continuous Testing: Agile testing experts conduct continuous testing to identify glitches at various stages of development and integration.

Continuous Feedback: One of the main reasons why products often fail to meet customers’ expectations is the apparent disconnect that exists between the end customers and other stakeholders. The agile testing strategy ensures the development team receives continuous feedback on the quality of builds. This helps the product to meet the business as well as customer needs.

Test-Driven: The agile testing approach includes testing at the time of development itself rather than later. This saves time and costs as mitigating a glitch after development can be expensive and time-consuming.

Less Documentation: The reusable checklist used by the agile testing specialists ensures the focus is on testing rather than on keeping the incidental details.

Simplified and Clean Coding: Since the glitches are identified and remedied within the sprint, the final code remains simplified and clean.

Accountability is shared by all: In the traditional system of testing, only the testing team is held responsible for the presence of glitches. However, in agile testing methodology, the development, testing, and business analyst teams share equal responsibility for the outcome.

Latest trends in Agile Testing

The spiraling demand for software testing and the advent of new technologies like IoT, AI, big data, and analytics emphasize the need to follow the latest trends. These include:

Artificial Intelligence (AI): The objective of agile in identifying and mitigating errors early on in the development cycle can be met effectively by using AI. For example, AI can analyze the database of past codes and check for patterns that contained glitches. These patterns can then be flagged for the developers and testers to gain necessary insights. Artificial intelligence can analyze the code under development to find if it deviated from its intended objective. This way it can help the agile-driven team to align the build with the business objectives and customer expectations.

Move towards Quality Engineering: The growing technological complexity of software applications means more chances for the ingress of bugs. So, along with continuous testing to identify errors in real-time, the focus should be on eliminating the ingress of errors in the first place. This calls for moving towards quality engineering where products and services are designed and built to meet or exceed customers’ expectations. It also involves the development, management, operation, and maintenance of IT systems displaying a high-quality standard.

Big Data Testing: With applications interfacing with other IT ecosystems (SMAC for example), a huge quantum of data gets generated. These need to be tested for errors using big data testing. Here, rather than testing the individual features of the software application, activities like data creation, storage, retrieval, and processing are validated.

Continuous Improvement, Integration, and Delivery: Agile is giving way to DevOps where apart from development and testing, the operations team is also involved. Here, any software build is tested continuously and enhanced based on the customers’ feedback. The entire DevOps process is geared towards ensuring the integration and delivery of glitch-free software quickly.

Conclusion

Is agile testing meeting the rising expectations of software applications? With delivering better customer experiences becoming critical for companies to stay competitive, the agile way of testing needs to follow the latest trends. Capturing and mitigating glitches earlier in the SDLC or even preempting them can significantly increase the acceptance of software in the market.

This article is originally published on medium.com.

What is the compliance perspective of Medical Devices testing

The role of medical devices in screening, diagnostics, and treatment has become critical. Modern medical devices have sophisticated components with digital interfaces. These help to derive meaningful inferences from the data emanating from patients’ vital stats. Since speed and quality lie at the core of the functioning of medical devices, they need to comply with quality standards and regulations. Also, since the functioning of medical devices is regulated by the built-in software, the same should adhere to basic safety guidelines or protocols.

Further, the rate of failure of medical devices has been found to be increasing by the day leading to the establishment of IEC 62304.

What is IEC 62304?

It is the international regulatory standard defining the SDLC requirements of software driving medical devices. The standard was established as it was felt that product testing alone will not ensure the safety of patients, especially when there is a software present. The standard requires every aspect of the SDLC to be scrutinized. These include development, configuration, risk management, maintenance, security, and problem resolution.

IEC 62304 offers a standard framework for the manufacturers of medical devices to design software. By conforming to this standard, manufacturers can fulfill the requirements of medical devices testing thereby generating trust. Also, since the standard is harmonized with the medical device directive practiced in the European Union, it has been acknowledged as a benchmark. The devices adhering to IEC 62304 include

•     Diagnosing, monitoring, or treating patients under medical supervision
•     Contacting the patient - physical or electrical
•     Transferring energy to/from the patient
•     Monitoring or detecting such energy transfer

Digitization in healthcare can have many dimensions. These include monitoring the performance of applications, leveraging the digital ecosystems for the stakeholders, and calculating the quantum of investments to drive digital transformation. The healthcare sector is being transformed with the infusion of new technologies (wearables included) and treatment/diagnostics methodologies. Since devices incorporating new technologies need to deliver results with precision, they should undergo rigorous medical devices testing. Let us discuss the reasons for validating medical devices through comprehensive Quality Assurance.

• Security: Medical devices contain sensitive data about patients’ health, which if breached, can lead to severe consequences. It is only through rigorous healthcare software testing that devices can be made hack-proof. The measures may include identifying vulnerabilities, validating and authenticating user log-ins, performing penetrating testing against firewalls, or encrypting data. Also, medical devices need to adhere to stringent quality standards such as the Health Insurance Portability and Accountability Act (HIPAA). This ensures the protection of patients’ health-related data and information.
• Usability: This is a crucial testing requirement as devices are handled by healthcare professionals while discharging their duties. Since many professionals may find it difficult to handle the features and functionalities of devices, the same should be made simpler. This is where usability testing using automation can help to simplify and enhance the user experience.
• Big data: The healthcare sector deals with a humongous quantum of data based on which inferences are drawn about the health condition of patients. These inferences are further leveraged to plan the right treatment strategy or develop a product. Big data analytics can help to derive the right inferences from the data quickly and accurately. It can help professionals to make informed decisions related to research and development, drug inventions, or curing ailments.
• Device interoperability: Medical devices need to connect and interoperate to deliver the required outcome and user experience. Since the healthcare sector needs to ensure data privacy, security, and regulatory compliance, the role of medical devices testing specialists becomes crucial. So, healthcare testing services need to apply technical expertise, resources, and time to ensure quality, compliance, and business profitability.

The testing of medical devices impinges on meeting the regulatory requirement. Moreover, navigating the regulatory ecosystem is crucial for the successful launch of medical devices or products. It ensures the adherence of devices to attributes like performance, safety, and security. Also, with the increased complexity of treatment protocols, the standards of quality and safety of medical devices should be enhanced. These include adherence to Electromagnetic Compatibility (EMC) testing for devices with power supply and electronic components. The quicker these standards are complied with, the faster companies can expect their products to become market-ready.

Conclusion

The healthcare sector deals with patients’ data and information on a daily basis. To ensure the security of these, the medical devices testing experts ought to ensure interoperability and flawless performance of such devices. This is where adherence to regulatory protocols becomes important to deliver better customer experience and market adoption.

This article is already published on dev.to.