Current software testing trends

With the latest Agile and DevOps processes on the go that brings in faster and quality releases, many enterprises continue to leverage and adopt test automation. Undoubtedly, test automation has already penetrated the industry in testing repetitive tasks quickly and efficiently. With DevOps substantiating proper collaboration between all allied departments of Dev and Ops, test automation tools continue to lead the software testing space.

According to Globenewswire — the global automation testing market is expected to grow up to USD 29, This growth in the automation testing market is due to the increased importance of AI and ML in the testing process which helps to reduce the testing life cycle.

Usability Testing to lead the charts for user experience. With the rapid influx of mobile apps, especially the eCommerce, banking, insurance applications, etc. Undoubtedly, today, mobile apps continue to be the major business enablers; an effective and great user interface with a streamlined usability flow is the need of the hour for all these apps.

Mobile users continue to show a preference for apps that have great usability embedded in them. Poor usability can affect customer and brand loyalty; hence usability testing identifies all the flaws if any before the app is actually released into the market. According to Statista — there are almost 2. For an app to stand out in the crowd, the app should provide seamless navigation and user experience to end-users. With the increased usage of mobile apps, user experience continues to remain the primary focus for all businesses and enterprises.

Big Data Testing to continue empowering businesses. 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 business strategizing with informed decisions drawn from this big data analysis.

This growth is due to the increased use of IoT devices in organizations, and due to the increased initiatives by the governments to enhance the usage of digital technology. Huge dependency on data across all industries requires effective big data testing to ensure quality, accuracy, and integrity of data that is essential for making informed decisions by all organizations. IoT Testing to boost digitally connected smart devices.

It is expected that the number of connected devices will be more than 20 billion by when compared to the figure of just 6.

These figures represent the massive expansion and the need for an effective IoT testing strategy. This IoT testing 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 that are much needed for end-users. 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.

There is a possibility of vulnerabilities seen in the hardware chipset of many new IoT products which is susceptible to multiple threats that need 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. This growth in the IoT testing market is due to the increasing adoption of the smart cities concept initiated by the governments and the keen interest of organizations towards incorporating IoT in their processes.

Increased use of IoT in the healthcare sector will be a new trend that can be expected due to the current pandemic. As more and more devices are getting connected and a large amount of data is transferred across these devices, a huge rise in IoT security testing services will be the new trend in the upcoming years to ensure data safety.

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 Machine Learning ML and user interfaces such as speech recognition and gesture recognition will advance in the future.

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

The current pandemic has led to increased use of transformative technology including AI technology. There are many areas where AI-based technology is playing a vital role. AI-based robots are minimizing contacts between humans, especially contact tracing apps are one of the greatest examples that showcase the use of AI in healthcare which is widely being used today.

Also, AI-enabled drone-based delivery will be the new trend in the upcoming years. Blockchain Testing to usher more prominence. 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, 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 their smart contracts are being used in every field of business from the energy sector to governmental services. The wide range of blockchain applications 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 the increased workloads. Hence, this entire process of Blockchain testing calls for efficient outsourced next-gen specialized testing services, for debugging the code to deliver productive blockchain applications.

In the year , security practices are going to come more into play, and these are some reasons why:. Cybersecurity tests protect not just transactions be it money or data , yet also secure their end-users.

As cyber risks can easily happen at any moment, in any shape, cybersecurity testing will continue to be a buzz in the following year. Here are some crucial reasons why:. By implementing this, the developer and QA teams work mutually and gain a good understanding of the Quality Assurance process. This teamwork will then help to make the procedure of testing and development further efficient.

In a nutshell, QAOps is a rising trend that enables the automation of procedures between IT, software development, and Quality Assurance to deliver software rapidly and with superior quality. Hence, gradually more organizations are inclining towards DevOps, and this places QAOps on the go in Automated thorough manual test effort is the perfect strategy to reveal the capacities of any skilled QA team.

Combining these two efforts can increase productivity, save time with enhanced quality. Currently, there has been a hike in automation and the requirement for automated QA engineers. Why should we merge manual with automation tests? The faster the QA team can detect the error, the less will be the time required to mitigate them, and therefore spending money on test resources is more valuable than spending it on errors after their release. It also illustrates that, all through testing, every technique, branch, situation, route, and choice has been well-tested so that glitches are discovered at the primary phase.

If the bug is identified at the beginning, the expense gets minimized to fix it. Automation testing has the benefit of consistency and speed; however, it lacks a user standpoint. This is wherein the manual test is better used, so it picks up where test automation leaves off.

Both the techniques might be used to cover up distinct sections of the same traits or for coverage of entirely separate features.

But, automation tests can only work and the scripts written for it, whereas manual tests are only as perfect as the QA engineers. Incorporating both these testing can result in a harmonious balance of usability, functionality, speed, minimized bugs, and an overall better user experience.

With the enhancement of microservice architecture on the web as well as software development, the usage of application programming interfaces APIs is mounting every day. APIs are being utilized in almost every component. Even the Client-Server development is at the peak, and the QA team must confirm these APIs are communicating perfectly with each other, plus functioning separately. To keep this procedure highly effective, automated testing on the Application Programming Interface and service level will rise as we move toward Enterprises face enormous challenges as they try to manage app quality while responding to additional demands from the business, counting inconsistent testing procedures across locations, geographies, and test groups, under-executing testing and QA functions, and sub-optimal consumption of resources, infrastructure, and tools.

In return, several big giant companies are looking to Centers for Quality models with devoted teams determined on standardizing deliverable implementation models to make sure the quality of significant business systems and processes. Testing Center of Quality is a model for a centralized test platform that offers standardized test procedures and optimum use of resources for quality and test reasons.

The testing center for Quality has test teams devoted to building up a reusable tests framework and standards for enterprises to follow whilst developing. In the long term, this aid in building superior-quality software and enhances the overall workflow of the software development procedure.

It will also offer effective automated tests and make flexible standards in QA practices to be executed in upcoming projects to come. Some of the other rewards of Testing Centers for Quality models being in are more agility to Quality Assurance and helps to set up a continuous improvement procedure driven by metrics.

Cloud-based solutions are being utilized by several enterprises, mainly IT firms, in enormous numbers, to attain cost-effectiveness, scalability, and flexibility.

The increasing use of cloud and virtualization has modified the way servers were used. It has simplified the bottleneck that was a problem in the past to allocate servers and configure them. The leading-edge infrastructure management technology has modernized the procedure of managing the architecture. The use of varied tools such as Terraform, Kubernetes, Docker, etc.

As the name proposed, infrastructure as code IaC is mainly the concept to manage your operations environment in a similar method, you do apps or other code for normal release.

In spite of manually making configuration modifications or making use of one-off scripts to make infrastructure changes, the operations infrastructure is controlled instead using the similar structures and rules that govern code development chiefly, while new server instances are turned up.

It means that the core DevOps best practices such as virtualized tests, continuous monitoring, and version control are applied to the underlying code that governs the design and management of your infrastructure.

In simple words, the infrastructure is treated the exact similar way that any other code would be. The use of advanced coding systems such as Puppet or Ansible is designed to make infrastructure as code environments available to any person with fundamental knowledge of modern coding structures and techniques. In easy words terms, Infrastructure as code is a framework that takes proven coding methods, practices and extends them to your infrastructure straight, efficiently blurring the line between what is an app and what is the setting.

In a nutshell, this is the similar thing DevOps is doing with the personnel in charge of these two worlds, melding operations and developers staff into a single unit with a portmanteau of a name. Manual procedures result in errors, period.

Humans are not always perfect. Communication is tough, and we are generally pretty bad at it. Even sometimes, manual infrastructure management would cause discrepancies, regardless of how hard we try. However, Infrastructure as Code solves that issue by having the configuration files themselves be the only source of truth. In this way, one guarantees similar configurations will be deployed again and again, with zero discrepancies.

The main benefit Infrastructure as code offers is speed. IaC allows you to rapidly set up your entire infrastructure simply by running a script. One could easily do that for each environment, from development to production, exceeding all the way through staging, Quality Assurance, and more.

Infrastructure as Code can make the whole software development lifecycle highly effective. This one is easy and fast. As you can version Infrastructure as code configuration files similar to any source code file, you have complete traceability of the modifications every configuration suffered. No more presumption games anywhere. The major benefit of Infrastructure as Code is, undoubtedly, lowering the infrastructure management expense.

By employing cloud along with Infrastructure as Code IaC , one could dramatically reduce the expense. By employing IaC, you can set up your infrastructure architectures in several phases. With the coronavirus epidemic, chatbots have become well-liked within the healthcare industry by offering remote support to patients plus several other sectors.

Due to global lockdown for months in a row, several companies implemented chatbots. Even chatbots provided 24x7 support to millions of retail stores, financial organizations, brands, etc. ChatBots will continue to conquer the globe as a part of RAP robotic process automation. Bots allow reducing costs on support while giving a better user experience. The smooth functioning of chatbot necessitates careful testing.

The open-source guide provides around questions to assess the user experience that your chatbot brings. It generally operates at 3 levels:.

The good thing about this chatbot test tool is that it incorporates seamlessly with key platforms such as Slack, Telegram, Facebook Messenger, and WeChat. Make use of it to discover any errors in the conversational flow of your bot, in the user experience that it offers.

Right from conversational flow to usability to the delivered user experience, this customized service allows you to test each main aspect of your chatbot. As per reviews from software test teams, the testing community seeks end-to-end, cross-platform testing solutions and robust test automation capacities. Here are some of them:. All are valuable to check out if nothing of the choices in the list above seems perfect for anyone. In order to leverage a product-market fit, several companies pay their complete attention to quality and depend on professionals working for the software testing company.

The solutions provided by such companies help you find resources and skilled software testers or QA engineers that are mature in the matter of accomplishment and applied technologies. It is estimated that there will be an expansion in independent testing in the upcoming decades. Concentrating on security and automated tests could also be a wise decision. With a transformative influence on your business in , it could be better to refocus Quality Assurance on user experience and build it on the DevOps and agile best practices.

In order to move products to market rapidly, consulting independent software testing companies would be better to address concerns professionally. These are the most recent trends in testing that are exceedingly useful for organizations and businesses. No matter whether you are a testing company or a QA professional, you need to continually brush up with these emerging software testing trends to stay ahead in the competitive and ever-changing industry.

Performance Zone. To sum up, scriptless test automation can be said an important practice that test teams should adopt. You can check this detailed guide on Codeless Test Automation. Big Data Testing will be one of the key software testing trends that will have a positive impact on different industries. Big Data is majorly used across different industries such as finance, banking, healthcare, retail, media, telecom service, etc.

Most of the enterprises across all sorts of industries are working with big data and dealing with a high volume of data to make marketing strategies that require constant and effective testing. Big data testing deals with various data types and huge data volumes and helps to improve decisions, validate data, and make better marketing strategies and business decisions.

To do big data testing we need to have a strong Big Data testing strategy rather than following traditional testing techniques. You can read this guide on Big Data for more information. The quality of data is crucial in big data testing irrespective of whether the data is structured or unstructured. Functional testing and performance testing of applications are also important components of big data testing along with the quality of the data. The Internet of Things IoT devices are digitally connected devices that connect wirelessly to a network and streamline the flow of data over the network.

According to the Gartner forecast, the installed base of active IoT connected devices reach This shows the growing capabilities of the Internet of Things.

It states that IoT testers will face a lot of challenges in terms of testing strategies to test IoT devices. Companies are planning to develop a testing strategy to test IoT devices. As a QA professional you have to adopt these advanced technologies and enhance your skills to test the usability, performance, and security of IoT devices. As per markets and markets report , the machine learning market expected to reach 8. AI and ML can help businesses to make better decisions using real-time data.

It enhances the quality of testing by quickly finding the bugs. Companies are using AI and ML in software testing in many ways from visual testing to the self-healing mechanism of automation testing tools.

Automation testing tools are developing using AI as part of a visual testing and self-healing mechanism. Visual testing helps test teams in writing fewer tests for the user interface when performing functional testing. The self-healing mechanism fixes issues in test scripts, reduces test maintenance, and reduces the cost to create automated tests. Robotic Process Automation is used to automate software jobs that run regularly without manual intervention.

A specialized computer program bots are used to execute certain events on a system for example change the quantity, calculate the price, etc. RPA tools are majorly used across different industries such as finance, banking, health care, customer service, etc.

RPA cuts cost improves quality and improves operational controls. Check out our detailed guide on Robotic Process Automation. Usually, in the traditional software development life cycle, requirements are placed on the left side and delivery and testing are placed on the right side of the workflow.

The problem of the traditional way is the negative outcomes for the business such as time-consuming, delay bug detection, and increasing costs. With Shift-left, software development teams collaboration become more proactive to ensure all initial ideas are feasible and developed properly.

This approach helps organizations to reduce the time to market the product and delivering the best outcome. It allows the QA team to work in tight coordination with the development team and product team.

With shift-left testing, we can detect bugs early in the SDLC. By testing in the early stages of software development, we can ensure a high quality of code as well as save time and money. Check out our detailed guide on Shift-Left Testing.