Artificial Intelligence (AI) was just a buzzword a few years back, but now it has become an indispensable part of human lives. AI is present almost everywhere, from smartphones, smart devices (Alexa, Siri, etc.), smart apps (Cortana, Google Assistant, etc.) to self-driving cars. With AI used across all fields such as Healthcare, Banking, FinTech, Insurance, etc., undoubtedly, this technology has its demanding presence even in the software testing field, which is on the rise today. Next-Gen technology is used in test automation to make the testing process smoother, faster, and more reliable.

Content 1. An overview of AI-based software testing 2. How can AI help overcome test automation challenges? 3. How is AI revolutionizing software test automation? 4. AI-based software test automation methods 5. Leading AI tools for automation testing 6. Conclusion

An overview of AI-based software testing

The software testing market has slowly changed from the initial manual testing to semi-automation, and then towards automation testing using tools. Further codeless automation, automation using bots leveraging AI & ML technologies, and specifically AI-based software test automation is in more demand in recent years.

AI-based software testing is an advanced software testing technique in which next-gen technologies like AI, Machine Learning (ML), and Deep Learning (DL) are used to test the software effectively. AI and ML apply problem-solving and reasoning techniques to automate and improve the testing process. AI-based testing can be performed effectively by leveraging tools that use data and algorithms to design and perform the tests without any human intervention. This advanced testing ensures better test coverage, test accuracy, faster time to market, and provides a significant reduction in overall testing costs.

AI is embedded into automated software testing processes making tests smarter, more reliable, and efficient. However, businesses continue to face many challenges associated with conventional automation testing methods and specifically, AI can be leveraged to overcome such challenges.

How can AI help overcome test automation challenges?

1. Finding the right set of people:

One major challenge associated with automation testing is finding the right team with the right skill set for scripting.

Solution: Businesses can leverage AI-based test automation tools that provide testers with a semi or fully codeless scripting platform, and can thus overcome the challenge of finding the right team and skill set.

2. Time consumed in performing repetitive tasks:

Every time a new test automation project comes up, no matter how reusable the components are, teams end up writing a lot of similar code again, which consumes a lot of time.

Solution: To overcome this challenge, AI can be utilized to generate test scripts quickly and automatically. AI tools can be trained based on the previous project inputs and outputs to create test scripts automatically for similar projects.

3. Flaky tests:

Testing teams spend hours determining if the failed test is due to application issues or poorly written test cases. These sorts of test failures are known as flaky tests, due to which a release is unnecessarily kept on hold, resulting in the delay of the software delivery.

Solution: AI can help teams overcome the challenge of flaky tests by creating more robust test cases and finding patterns in random test failures to speed up the process.

4. Frequent script updates due to UI changes:

In order to provide a seamless User Experience (UX), businesses tend to modify the app User Interface (UI). Even though the change may be minor or unnoticeable, it could fail the test scripts while performing some actions on the page.

Solution: AI and ML algorithm-based tools can be trained to determine minor changes in the code or bugs in the application. These tools can then take further actions accordingly and save human efforts in script updates for such minor changes.

5. Determining appropriate test size and test cases:

Every time a change is made in the application, regression tests are to be conducted. But, in a fast-paced environment, it is logically not feasible to run the entire regression suite of test cases for every change. Thus, determining the right size and set of test cases to be run for a particular change has always been challenging for the testing teams.

Solution: AI can help testers build algorithms that predict the right size and set the regression test suite for the given change based on different parameters.

6. Maintaining the test suites/test scripts:

As the size of an application becomes larger, it becomes challenging to maintain a large number of test scripts.

Solution: AI can be used to overcome this challenge as AI tools can be used to maintain and extract the required test scripts as per testing requirements.

Thus, AI helps overcome the challenges of traditional test automation and is said to bring a revolution in the field of test automation.

How is AI revolutionizing software test automation?

1. Helps automate the unit testing process:

AI can be used for static analysis of the software to decide which code area is not covered under unit tests. The AI-based software testing tools can use this information to generate unit tests for uncovered code. These tools can generate unit tests and even update the tests whenever the source code changes.

2. Enables visual validation of UI testing process:

AI can be used for UI testing as it involves the usage of image recognition techniques to navigate through the application. It can verify UI objects and elements visually to create UI tests. AI tools also use exploratory testing techniques to find bugs in the application’s UI. The AI tool generates the screenshot for later verification by QA engineers. It also verifies the other visual aspects of the system under test like color, size, layout, etc.

3. Eases regression testing significantly:

The AI-powered tools and AI-powered engines help to create tests faster, run thousands of tests in one go, and significantly reduce overall test maintenance. These tests work seamlessly across browsers and even mobile browsers.

4. Supports self-healing:

Many self-healing automation tools use AI and ML to automatically update and adjust changes in the UI or environment of the application. Typically, the AI tools detect the issues and remediate them without any human intervention through the self-healing method.

5. Delivers early defect predictions:

AI in testing helps in early and fast bug identification, reduces defects, and makes the product bug-free and reliable for end-users.

6. Smoothens the continuous testing process:

AI smoothens the continuous testing process by eliminating the chances of manual intervention. It can detect issues or defects in the testing process and trigger remedial measures to ensure continuity of the testing process. This ensures that only the bug-free code progresses from one stage to the next stage and helps to improve the quality across the software development lifecycle.

7. Ensures seamless execution of API testing process:

AI algorithms understand the patterns and relationships in different API calls and group them effectively based on the scenario. It understands the existing tests to learn about the relationship between APIs, and then uses this information to understand changes in APIs and finally creates new scenario-based tests.

8. Improves the security testing process:

AI can improve the security testing process by quickly identifying the cyber-security issues present in the software. It can extract information from the previously recorded data and analyze the system’s weaknesses in real-time. This way, AI can help businesses strengthen the security and privacy of their systems, application, data, and networks. It also allows testers to create more robust penetration tests.

Thus, it is evident that AI is used in all spheres of the automation testing process and ensures faster testing outcomes more efficiently without any human intervention.

AI-based software test automation methods

AI-based testing:

This testing method is an advanced software testing technique in which AI, ML, and DL technologies are integrated with the automation testing process to improve the software’s quality and speed up software delivery. Some of the common methods are:

Regression suite automation:

Regression testing consumes a lot of time and the tester’s efforts. AI-enabled regression suite automation intelligently runs automated tests based on the changes in the code each time. The main aim of this test is to reduce the regression test cycle time by identifying and executing the right set of test cases that must be executed.

Defect analysis and prediction:

It involves the usage of ML and NLP algorithms that helps with the effective identification of software defects. The main aim of this test is to ensure the early identification of defects and help businesses go to market early.

Self-healing automation:

AI-enabled self-healing process involves automatic healing of test automation script breakages that could occur due to object or other property changes. The main aim of this test is to ensure lesser manual intervention and a faster self-healing process.

Leading AI tools for automation testing

UiPath Test Suite:

UiPath Test Suite combines the world’s leading Robotic Process Automation (RPA) technology with best-of-breed testing capabilities to accelerate scaling through proactive testing, cross-enterprise collaboration, and a consistent approach to create and deploy automation. UiPath Test Suite benefits practitioners who create automation and stakeholders who depend on automation, including transformation leaders, business users, customers, and partners. UiPath Test Suite consists of Test Manager, Studio Pro, Orchestrator, and Robots, and other Test Studio Components. Moreover, it fosters collaboration through reusable artifacts and has a user-friendly interface.

Sofy.Ai:

It is an AI-powered, no-code quality automation platform that leverages the power of AI to test mobile apps on a variety of android and iOS devices. It can be used to perform manual, automated, and RPA testing of apps. Uniquely, this no-code testing platform simplifies testing, saves testing time, increases productivity, and shortens the testing life cycle. This intelligent automation testing tool simplifies manual, automated, and robotic testing in a significant way.

Tricentis Tosca:

It is an enterprise software testing tool used for end-to-end automation testing of software applications. This tool comes with amazing features like Vision AI, model-based test automation, risk-based test optimization, etc. The Vision AI feature of this tool leverages the power of AI to automate and customize tests. It uses deep convolutional neural networks combined with advanced heuristics to deliver stable, self-healing, platform-agnostic UI automation. Vision AI, together with Tricentis Tosca, can be used to automate even the hardest-to-access UI systems.

Appvance IQ:

It is an AI-driven autonomous continuous testing system that can be used for test creation and execution. This tool allows the generation of autonomous tests and codeless test creation and can be used to execute functional, performance, and security testing. This tool supports continuous testing in a DevOps environment and can be easily integrated with Jenkins, TeamCity, CircleCI, Jira, and other CI/CD tools.

Functionize:

It is a cloud-based automation testing tool that is used for functional and non-functional testing (load and performance testing). This tool uses AI and ML algorithms to speed up test creation and maintenance. This AI tool uses a plain English language and NLP to create a functional test. Also, this tool can execute thousands of tests in a minute from all desktops and mobile browsers.

Parasoft:

It is an automated testing tool that can be used to automate the tests at every stage of the software development cycle, starting from code analysis to UI Testing. It is a test suite with various components, and it has recently introduced AI and ML into software test automation to help organizations adopt static analysis.  Parasoft’s AI solution can be used to detect defects early and to ensure compliance with a variety of functional safety, security, and coding standards. It reduces the burden of teams and ensures serious design issues are addressed first.

Applitools:

It is an AI-powered visual testing and monitoring platform that is easy to set up and integrates with all modern test frameworks. This tool supports all major browser versions and allows to check the functionality and appearance of the app on various devices like mobile phones, tablets, laptops, or external monitors.

Conclusion

AI in software test automation has become an important trend and has a tangible reality as AI holds the potential to take software testing to the next level. Undoubtedly it helps testers to generate more tests and ensures the speed and reliability of automated tests. But integrating AI into software test automation needs professional assistance as it is a complex process. Businesses can leverage AI testing from a Next-Gen QA and independent software testing services provider for high-quality software and faster time-to-market.

The post How is AI Revolutionizing Software Test Automation? first appeared on TestingXperts.

Enterprises across the globe continue to foray into the challenging and turbulent market space with a need for quality products to gain their market place. Software development of applications and products demands effective software testing that has a shift-left approach today. Software testing is taken up along with the software development life cycle (SDLC) to enable faster releases and deliver quicker ROI. Hence, looking at the criticality of software testing in the SDLC, more and more enterprises are looking out for end-to-end testing cycles and hence continue to invest in next-gen software testing services.

Contents 1. Top 16 Software Testing Trends to Look Out in 2020 2. Test Automation to Ramp Up Quality for Agile & DevOps 3. User Experience to take a Leap with Usability Testing 4. Big Data Testing to Empower Enterprises 5. IoT Testing to Boost Well-Connected Smart Devices Market 6. AI & ML in Testing goes Mainstream 7. Surge in Blockchain Testing 8. Demand for Cyber Security Testing on the Rise 9. RPA Testing is Even Bigger than Automation Testing 10. Performance Testing to Shift towards Performance Engineering 11. QAOps 12. Mobile Test Automation 13. Testing to become effective with Behavior-driven 14. Use of Scriptless Automation Tools 15. Multi-device Testing 16. Integration of tools 17. App penetration testing 18. Conclusion

A recent Nelson Halls’ report states Next-gen testing accounts for 24% of the software testing services spending. It is the fastest growth offering with a 12.8% CAGR for 2018-2023. Growth is driven by mobile testing, which still accounts for ~75% of all next-gen testing spending.

Evidently, software testing continues to play a key role in the emergence of software development methodologies like Agile and DevOps. In addition, with the industry 4.0 in place, the trend indicates a shift towards automation, cyber-physical systems, the internet of things (IoT) and the industrial internet of things (IIoT), cloud computing all around and the booming up of Artificial intelligence with Machine Learning trends. Testing of these technologies demands effective test strategies and testing methodologies to be in place. Interestingly, enterprises should ponder some of the major software testing trends to take the complete benefit of the market segment to derive products of their requirement.

Top 16 Software Testing Trends to Look Out in 2020:

1. Test Automation to Ramp Up Quality for Agile & DevOps

With the latest agile and DevOps processes, faster and quality releases as the underlying motto, most enterprises continue to adopt test automation. Undoubtedly, test automation has already penetrated into the industry in testing repetitive tasks quickly and efficiently. With DevOps substantiating proper collaboration between departments, test automation tools continue to lead the software testing space. The proper usage of test automation tools helps to achieve faster release cycles, better quality, and quicker ROI.

2. User Experience to take a Leap with Usability Testing

With the rapid influx of mobile and e-commerce applications, the significance of software testing has been witnessed to test the operating systems, platforms, and devices. The mobile apps continue to be the major business enabler for businesses today; an effective and great user interface with a streamlined usability flow is the need of the hour. Along with a quick app loading time, users continue to show preference to apps that have a great Usability embedded in them. Poor usability can affect customer and brand loyalty; hence usability testing identifies all the bugs before the application is released to your users.

3. Big Data Testing to Empower Enterprises

Enterprises across industries continue to deal with huge data volumes and diverse data types. The mining of any amount of structured or unstructured data defined as Big data needs effective testing. Big data testing helps to make improved decisions with accurate data validations, and helps improve market targeting and strategizing.

4. IoT Testing to Boost Well-Connected Smart Devices Market

It is expected that the number of connected devices will be more than 20 billion by 2020 when compared to the figure of just 6.4 billion during 2016. These figures represent the massive expansion and the need for an effective IoT testing strategy. It includes the testing of Operating systems, communication protocols, along with software and hardware of the IoT devices. Most enterprises have already started identifying the need for an effective IoT testing strategy to enable efficient and well-connected smart devices.

Especially, testing for vulnerabilities in IoT devices is an emerging business need as IoT typically encompasses all products that are connected to the internet in one way or the other. IoT systems collect data while in usage from various interconnected devices and share information with their manufacturers without the users being aware of it. Further, manufacturers in their haste to get new design features for smart products continue to overlook the complications arising out of security concerns of system’s software and hardware.

There is a possibility of vulnerabilities seen in the hardware (chipset) of many new IoT products which is susceptible for multiple threats that needs to be effectively tested. Even the software that is included in the IoT devices usually does not get any sort of security testing done while at the manufacturers end. Thus, numerous IoT devices continue to get hacked due to the susceptibility affecting the entire network of users. Hence, it is essential to get all the IoT products and devices security tested to avoid threats and vulnerabilities.

5. AI &ML in Testing goes Mainstream

According to a leading Research analyst, Artificial Intelligence(AI) will be omnipresent in all spheres of technological innovations. It will become the top investment priority of CIOs by 2020. The market for AI is expected to be around $6-7 billion in North America alone. Machine Learning (ML) and user interfaces such as Speech recognition and gesture recognition will advance in the future. The prediction of various tasks based on complex neural networks and algorithms has literally changed the outlook of technology and these applications also need rigorous testing and validations.

With the world completely moved towards digital transformation, there is much pressure to balance market requirements and build a system which is predictive and scalable to cater future needs of software. Testing needs to embed AI which perfectly imitates human behavior using machine learning and predictive analytics. Going forward for the upcoming latest applications in connected world evidently needs testing to use AI and ML to automate.

6. Surge in Blockchain Testing

There is a rapid expansion of the virtual currency Bitcoin usage. A recent report by McKinsey states that Blockchain is a nascent technology with the potential to bring about step-function improvements in financial transactions. Blockchain testing helps to enable smart contracts and ensures fraud protection.

Undoubtedly, the Blockchain technology has revolutionized the way businesses are dealing with digital currencies such as Bitcoin. These Blockchain applications are not limited to the financial world and its smart contracts are being used in every field of business from energy sector to governmental services. This wide range of applications support brings in new challenges to Blockchain debugging.

Moreover, once the smart contract is implemented, its execution cannot be reversed and hence, smart contract codes define how seamlessly the software performs even with increased workloads. This entire process of Blockchain testing calls for efficient outsourced next-gen testing services, specialized in debugging the code to deliver productive Blockchain applications.

7. Demand for Cyber Security Testing on the Rise

Undoubtedly with the digital revolution, there has been the emergence of various security threats. The CIOs of enterprises continue to realize the importance of security testing of their applications, network, and systems to ensure not only secure transactions but complete protection of end-users critical data. Thus, security testing has gained a lot of importance as it safeguards brand loyalty and prevents economic losses.

8. RPA Testing is Even Bigger than Automation Testing

RPA can also be named as an extension of Automation as it can be applied to anything which is in a structured form, unlike automation which needs a software product to work upon. RPA can be used with very complex processes that can be automated with AI. Specifically, it is a style of automation wherein a machine, or computer mimics a human action and helps in the completion of rule-based tasks. The Robot led automation has the true potential to change the workplace and does all tasks performed by automation testing tools.

9. Performance Testing to Shift towards Performance Engineering

Product performance has earlier been the major segment of testing but now, it is slowly shifting towards performance engineering which is not an easy process. The performance engineering process involves the collaboration of hardware, software, configuration, performance, security, usability, business value and it ensures to deliver the highest value that exceeds end-user expectations.

10. QAOps

The digital world is effectively in need of software applications that are released faster with no compromise in the quality of the applications. Earlier, the need for testing teams was limited only for performing application testing, but now the importance of QA is effectively increasing. QA is playing a crucial part in the complete software development.

Similarly, DevOps is another automation approach that has gained crucial importance in order to deliver applications faster. And the combination of these two methodologies, i.e. QA and DevOps brings a new practice called QAOps or DevTestOps.

With this practice, the testing, development, and operation teams can be on the same line by erasing the boundaries. With this latest approach, continuous testing can be combined DevOps and thus assures changes in software are made effective with the practice of Continuous Integration (CI) and Continuous Deployment (CD). Thus, software testing is not practiced at indefinite intervals and the applications are delivered with quality and without delays.

11. Mobile Test Automation

Mobile phones have become a very popular medium and this has enhanced the rise of mobile applications. Enterprises are choosing the pathway through mobile apps as an easy way to reach users. And, to ensure that mobile apps work efficiently for users, automation testing for mobile applications is essential.

With the rising mobile app market, the need for mobile testing with automation tools becomes crucial. Considering this effective needs, there have been many automation testing tools for mobile applications such as Robotium, Kobiton, TestComplete, Appium, etc. Mobile automation testing can be on the list of top trends until there is another powerful medium like a mobile application.

12. Testing to become effective with Behavior-driven

This has been in the list of software testing trends for many years and it is continuing the same still. Yes, Behavior-driven development (BDD) is effectively getting enhanced with the adoption of AI and ML by many organizations.

With the BDD implementation, it becomes easier for many organizations to upgrade their insights and business with higher user acceptance. This is because, the adaption of BDD requires the implementation of BDD tools, frameworks, and requires test cases built based on visitor’s interaction. A few of the top BDD tools and frameworks include Cucumber, Concordion, FitNesse, HipTest, etc.

13. Use of Scriptless Automation Tools

Software testing is effectively seeking many advancements and improvements continuously. Every change and advancement in this field is helping the teams in releasing applications faster with quality assurance. Among the many such advancements, scriptless automation is one of the important trends in 2020 that is reducing the human efforts and helping in accelerating quality applications that can help in achieving greater ROI.

With Scriptless test automation, there is no more the issue of coding for test engineers for automating test cases. This is an effective advancement helping to deliver quality results faster by reducing the coding time.

14. Multi-device Testing

The use of mobile devices is effectively increasing year by year and this is increasing the need for automation testing on a range of devices. However, this is not only limited to mobile devices, it is for many devices such as wearables, smartphones, smart speakers, etc. Thus, this year, automation testing over a range of devices become essential for many testing teams.

15. Integration of tools

The growing demands in the businesses are effectively in need of solutions that help in delivering faster and quality applications. In order to overcome this difficulty, it has become important for organizations to consider the integration of tools so that it eases in achieving seamless functionality within businesses.

Thus, the businesses of the present digital world are effectively in need of testing tools that can easily get integrated with other tools as they assure better application management. This is a very important software testing trend in 2020 to help enterprises and benefit them in the reduction of cost, management of application, etc.

16. App penetration testing

Almost for every industry, applications have become major important and these are playing a crucial role in their development as well. But, to ensure these work efficiently for users, the internet-based applications have to overcome several attacks and vulnerabilities.

Web applications have the most chances of facing vulnerabilities such as hacking. Thus, it is important for enterprises to implement successful security strategies for achieving systems that are secured and work efficiently for achieving business goals. And, to do this, it is necessary to include Application Penetration Testing into software testing practices. With this testing practice, it becomes easier for the teams to validate the effectiveness of the app’s security controls and identify the risks that are exposed to vulnerabilities.

Conclusion

Software testing method like any other services segment is attaining a new level with more traction towards the new trends stated above. Manual testing is slowly being replaced by automated testing, leveraging enormous open source and paid tools. Thus to ensure quality products, enterprises need to be updated with the latest software testing trends. It is important to partner with a next-gen testing services provider to be assured of quality for your products.

The post Top 16 Software Testing Trends to Watch Out in 2020 first appeared on TestingXperts.

2018 has come to an exciting end for us here at TestingXperts. Join us as we look back at some of our most significant accomplishments and prestigious recognitions in our ‘Year In Review 2018’. Also, a big thank you to TestingXperts teams and clients who were a part of this journey.

TestingXperts Year in Review 2018

The post TESTINGXPERTS YEAR IN REVIEW 2018 first appeared on TestingXperts.

The rapidly approaching GDPR deadline, i.e., May 25, 2018, is clearly on the horizon. GDPR is not just limited to the European Union. Even if you are not a European-based business, you will be affected if you have customers in the EU.

Contents 1. GDPR Compliance 2. Steps to make your Testing GDPR compliant 3. How can TestingXperts help?

GDPR affects all businesses within and outside of the EU who hold personal data for individuals within the EU. From IT operations to marketing, anyone dealing with personal data needs to recognise how GDPR affects their data workflows. GDPR applies to all the data that is gathered from the ecosystem, whether it is provided by customers or gathered by automated systems. This also includes personal data stored and used in big data analytics platforms.

GDPR Compliance

In many organisations, development teams deal with data from real production environments and usually, this data originates from customer databases. However, testing with real data often causes problems regarding information security and confidentiality. GDPR necessitates explicit attention to this practice. Every data that includes personal data is subject to GDPR compliance. It is illicit to have personal data anywhere where it is not-obligatory.

Therefore, Test Data Management(TDM) is an area that definitely needs attention from GDPR perspective. From bringing efficiency to data processing and testing the quality of deliverables, TDM is susceptible to vulnerabilities around organisational and regulatory standards. Therefore, various measures (e.g. masking) should be employed to ensure that the personal data is encrypted.

Test data may become a block in your preparations for GDPR. In order to address the challenges associated with testing and make the testing GDPR compliant, it is important to follow the below steps.

Steps to make your Testing GDPR compliant

1. Document the use of personal data in test environments.

Documenting the personal data should be the first step in your GDPR compliance process. This includes listing down the data in backups and the subsequent replicas that the testers have created for themselves. This step might expose uncomfortable surprises, like huge amounts of personal data in test database tables.

2. Develop a smooth test data management process

A lean and adaptable process is needed to stay in control for a smooth test data management process. Properly analysing and tracking the document from where the real data is coming, and where it is going is important. According to the new regulation, it is important to ensure that no personal data is open to business users, software testers, test managers, and other team members during software development, maintenance and test phases.

3. Employ a combination of masked data or synthetic data for testing

Though using synthetic data is a desirable option, but it is not always promising. Hence, it might be prudent to use a combination of carefully masked data along with synthetic data.

4. A proper review of privacy policies

Privacy policies must be articulated accurately. There should be a specific reason for collecting, sharing, storing, and using the personal data among third-party processors. Consequently, it is also important that you are reviewing the third party policies as well to make sure they comply too.

How can TestingXperts help?

Adherence to the regulation will require a comprehensive test data management approach. TestingXperts will help you build cross-functional teams for you to carry out various GDPR assessment and implementation activities with Tx’s GDPR framework. Tx has a step-by-step phased approach to GDPR compliance to provide a comprehensive solution.

The post Is your Test Data GDPR Compliant? 4 Steps to Make your Testing GDPR Compliant first appeared on TestingXperts.

Internet of Things (IoT) is taking over the world both in the idea as well as practice by transforming the way industries are working together. IoT has initiated the quicker advancement of ideas that affect our everyday activities. It has also helped in accelerating the technical world’s proficiency to make digital ecosystems of the future an existing reality.

Contents 1. IoT Testing 2. Unique features and requirements of IoT Systems 3. Challenges Impeding IoT Testing 4. 5 Major Types of IoT Testing 5. TestingXperts’ IoT Testing Solution

IoT Testing

It has become a reality that the world is ready to consider and leverage for better results. Its acceptance can be accredited to many of its core features such as:

• Increase of various protocols

• Proficient Machine to Machine communication

• Overall smart working and living

• Unification of several embedded devices

Unique features and requirements of IoT systems

Compared to other applications, IoT applications are categorized by several unique factors, such as:

• Combination of sensors, gateways, hardware, connectors, and application software in a single system

• Real-time stream analytics and intricate event processing

• Support for data velocity, volume, variety, and veracity

• Conception of large-scale data

Challenges Impeding IoT Testing

• Security and data privacy: Impending security vulnerabilities across various layers of IoT system

• Real-time complexities: IoT applications can have various, real-time scenarios and its use cases are complex

• Dynamic environment: With millions of sensors and devices in combination with intelligent software, IoT has a dynamic environment, unlike application testing that is performed in a defined environment.

• Expertise to automate: Due to numerous scenarios and dependency on sensors and devices, automation has become a challenging process

• Scalability of the system: Building a test environment to measure functionality along with scalability and consistency is challenging

5 Major Types of IoT Testing

With the advancement in technology, a whole new level of complexity has been introduced for testing IoT devices. To abridge the complexities involved in IoT testing process, testers can allude to these 5 IoT testing types:

1. Security Testing

2. Performance Testing

3. Compatibility Testing

4. User-Experience Testing

5. Exploratory Testing

TestingXperts’ IoT Testing Solution

The post IoT Testing: A Connected Approach for Applications first appeared on TestingXperts.

The advent of Agile and DevOps as a practice over the few years has helped enhance the quality of software immensely. The highly collaborative nature of both the methodologies has led to rapid deployment cycles, and in some cases, it has also helped in achieving the speed of Continuous Integration. The best way to ensure the quality of the software is by implementing the right amount of QA. In fact, implementing an integrated DevOps and continuous testing quality approach has become relevant for all the organizations now.

Contents 1. DevOps QA Best Practices 1.1 Disentangle from the traditional roles and responsibilities of QA 1.2 Communication and Collaboration are Imperative 1.3 Include QA in the Requirements Gathering Process 1.4 Detecting Software Defects Early 1.5 Multiple Virtual Testing Environment is Essential 1.6 Conclusion

A highly iterative DevOps process lays more pressure on the QA role to properly evaluate software while working closely with developers, network professionals, and business stakeholders. In this article, we will explore how DevOps is the best way to strengthen the project and be absolutely sure of what you are shipping to the customer. With high-quality software as the main aim, here are some DevOps QA best practices that will help accelerate the software delivery and also maintain the quality of the software.

DevOps QA Best Practices

1. Disentangle from the traditional roles and responsibilities of QA:

2. Communication and Collaboration are Imperative:

3. Include QA in the Requirements Gathering Process:

4. Detecting Software Defects Early:

5. Multiple Virtual Testing Environment is Essential:

The post Accelerate Software Delivery with DevOps Best Practices first appeared on TestingXperts.

There was a time when the banking sector was less involved and open to technological innovations, primarily due to compliance and security concerns. However, technology has infused our lives so much that even the banking sector could not refrain from embracing it. Since then, the world of banking has seen incredible changes; one of the changes is ‘Digital Transformation’.

Traditional banking has evolved into digital banking; it is an important change as to how banks have started interacting with customers and satisfying all their business needs remotely. In this infographic, we will discuss the 6 incredible ways that technology has evolved in the banking sector.

Ways Technology Has Transformed the Banking Sector

The post 6 Incredible Ways Technology Has Transformed the Banking Sector first appeared on TestingXperts.

Today’s fast-paced technology-centric world presents opportunities and threats at every turn. It has become challenging to practice agile development no matter how big or small an organization you are. To become sustainable in this disruptive environment, agility is key.

Contents 1. How to bring Agility into your organization and how to survive? 2. Conclusion

Let us have a look at the 6 most effective ways an organization can achieve business agility.

How to bring Agility into your organization and how to survive?

1. Scalability To deliver customer satisfaction, there is a need for a reliable value-delivery engine aligned with top business priorities, giving you fast and predictable implementation across the entire software development lifecycle. Waterfall teams are unable to deliver faster, as required by the enterprises these days.

2. Performance Bringing agility into your organization means sensing and adapting to change rapidly and confidently. Building responsiveness into your organization’s DNA is important, in order to take a disciplined approach to managing change, for which business agility is critical. Delivery is the origin for bringing agility. Improving cycle time can significantly affect your end result. Agility helps in fulfilling customer requirements early and often, assuring that you are building the right products.

3. Adaptive Planning Funding and scope are two key factors that can allow your business to move and build responsiveness. Establishing a consistent planning speed can help enterprises decrease risk and increase investor confidence.

4. Growth It is observed that most enterprises spend approximately 15% of their budget on innovation. However, highly effective organizations allocate at least 50% of their budget on growth initiatives. By increasing speed, productivity and quality, you can spend fewer resources on fixing bugs, technical debt and refactoring. Agility helps in making the right decisions with confidence.

5. Bring Change In order to nurture growth and innovation, it is important to follow a deliberate approach to investments in innovation. Business agility helps in connecting horizon investments and disciplined exploration with the principle of fail fast and fail cheap.

6. Organize around Customer Value It is important to connect with the market, customers and stakeholders. For this, breaking down functional silos is important, as it helps in improving collaboration, minimizing dependencies and reducing waste. It helps in allowing you to respond to opportunities and threats rapidly and confidently.

Scaling helps in organizing around customer value at the delivery level by creating teams that are aligned with common release objectives. Scaling also helps in ensuring the business to identify the true value of your agile developments.

Conclusion

The post 6 Most Effective Ways to Build Business Agility first appeared on TestingXperts.

Every enterprise trying to succeed in the digital economy is seeking for two things: speed and scalability. If it’s important for a company to get to market faster, it’s equally important to be able to scale up appropriately to support increasing customer demand. But the key motto that should be followed here is speed and security at scale.

Contents 1. What are Microservices? 2. How can an enterprise benefit from microservices? 3. Testing Microservices 4. Conclusion

Agile and DevOps models support distributed and decentralized ownership of software assets and promote faster turnaround of changes and rapid deployment. However, to skillfully segregate complex, monolithic applications into independent units, a design strategy namely “Microservices” is essential.

Monolithic applications were once effective in the digital economy, but not anymore. Development teams across enterprises have been struggling hard to deal with large applications pertaining to deployment, development time, and scalability. With the extensive adoption of DevOps frameworks and agile methodologies, development teams felt the necessity to break down complex application silos into simpler code blocks, which enlightens microservices.

Microservices has successfully made its mark in the software architecture market. The Microservices architecture differs from the traditional monolithic architecture where the application was built as a single entity. However the monolithic architecture is rather sound, obstructions around it are building precisely when almost all applications are being deployed on the cloud platform. Microservices architecture constitutes an integrated skelton where rather than plugging the components together, the software is fragmented by breaking it down into services.

By fragmenting a big application into microservices, development teams become more active with updates and deployments. Whereas, it also removes dependencies to create complex and large builds, by eliminating the need for over-sophisticated architectures.

Every enterprise has different needs; there can be times when microservices would be a good solution and, times when a monolithic application would make more sense.

For enterprises on a digital transformation journey, with the need to be agile, implementing a microservices architecture has several advantages in the app economy.

1. Easier to scale: In the microservices architecture, only the components that need improved scalability are affected, than the entire app. This also provides substantial cost savings to the enterprise.

2. A Grander architecture for larger applications: In this architecture, components can be built or swapped at will without having an impact on the entire application. In this case, if something goes wrong, only that component is affected.

3. Agility is the key: Microservices architecture provides more agility and makes it easier to pivot segments of an application.

However, microservices based applications also have:

1. Trouble testing the entire API app but becomes far easier to test individual components. 2. Extra moving parts than a monolithic app, which becomes difficult to monitor. 3. Extra modern back-end infrastructure requirements to the growing codebase.

Evidently, the testing strategy that is applied to the traditional monolithic application needs to change with the shift to microservices. Considering that applications that are built in the microservices architecture offer high performance and functionality, testing has to cover each layer of the service. A microservices architecture comprises of small, focused services that create a whole application together. Each illustration of a microservices represents a distinct responsibility within the application. The actual advantage is these services are independent of each other, which makes them independently testable. However, due to the disseminated environment of microservices development, testing can often become a challenge. Some of the challenges are given below:

• Accessibility of a dedicated test environment since development is agile and not integrated.

• Recognizing the right amount of testing each time in the test lifecycle

• Complex extraction logs during testing and data validation

Conclusion

Today’s DevOps and Agile-focused enterprises are endeavoring for fast changes and quick deployments. Microservices architecture is a boon to all these companies. Organizations can facilitate smaller development teams with more objectivity and agility, resulting the business being more in tune with their changing demands. At TestingXperts, we ensure that effective testing is implemented by the right set of testing specialists. Our intelligent test automation framework Tx-Automate ensures accelerated testing to provide you with higher productivity and the desirable time to market.

The post Achieving Agility and Scalability Becomes Reality with Microservices Architecture first appeared on TestingXperts.

The last few years have witnessed an insistent acceptance of new method named ‘Containers’ as organizations wish to deal with disruption at a faster pace. Containers have been around in the IT world for over a decade now, but the arrival of Docker made them more popular as it re-invented containers by adding a robust platform, integrated management tools, and code registry.

Contents 1. What are ‘Containers’ and why do we need them? 2. Containers and DevOps: What’s the connection? 3. Conclusion

What are ‘Containers’ and why do we need them?

Containers are the answer to the common problems faced by software to run consistently while moving from one computing environment to the other. This could be from a developer’s system to a test environment, or from a physical machine in the data center into a virtual machine in the cloud. Containers have become an integral part of the application development space, particularly in cloud computing. This is mainly because portability has been the biggest hitch in this space and given the proprietary nature of these public clouds, this method helps in abstracting applications into a virtual container that can be moved from one cloud to another.

The architecture of containers is an added advantage as it contains a standard method to divide applications into dispersed objects or also known as containers. This flexible approach offers more advantages around workload management and also offers the ability to make defect-tolerant systems. Containerizing the application platform can help in abstracting away the differences in the OS distribution. Another great benefit of containers is ‘modularity’ i.e., rather than running an entire application inside the container; the application can be split into different modules.

Containers and DevOps: What’s the connection?

You might have heard ‘DevOps’ and ‘Containers’ in the same sentence quite often Though, they are different concepts, but the effectiveness of containers makes it easier to enable DevOps workflows. Though DevOps is not tied to any particular technology; it can be implemented through any tool, it is convenient to implement DevOps with the help of containers.

-Containers have become an easy solution for the DevOps teams as it makes collaborating with various teams such as development, testing, and operation easier and convenient.

-Containers are able to support multiple frameworks, and it becomes easier to switch between different programming frameworks in DevOps.

-Rolling out application updates on a streamlined basis is required for a continuous delivery of software. When the application is distributed into multiple microservices, each one hosted in a separate container, you are able to update one part of the application by restarting the container without barging in the rest of the app.

While both containers and DevOps are helping improve software quality, the focus on automation and continuous delivery have been leading to various quality issues. Developers are often challenged with log files that are scattered in a variety of isolated containers each with its own log system dependencies. Continuous testing should be implemented to allow development teams to detect problems early on. If a continuous testing approach is not followed, fixing of errors will take much longer. TestingXperts’ intelligent continuous testing platform Tx-Automate has been enabling end-to-end automation of applications to enhance the quality of the software and increase speed to market. Connect with our Test Advisorsy services and allow us to help you build a defect-free and quality software/ application.

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