Of late, large organizations have software applications as their foundation to build, design, develop, and maintain business since more and more firms are reaching out to markets and consumers via online platforms. The need for online platforms and software applications is quintessential as they create a passage for your end-users to communicate with you about your services or solutions. In other words, software bridges the communication and sales gap between customers and businesses, which makes it the cornerstone of any successful organization.
The rapidly advancing and constantly changing needs of software users in the era of software testing have paved the way to come up with efficient solutions to address the challenges in the industry. Software testing, in the early days, was nothing more than just debugging that was leaning more on the development side rather than testing or evaluating the quality of the software. Over the years, with the emerging need to build more error-free applications and websites debugging was differentiated from testing. Thereby, software testing evolved at a great speed to focus more on the verification of every piece of software application and meet every specific need of the client for good customer experience.
How Far Have We Reached Now?
Like mentioned earlier, the exponential change in technology creates a major impact on the way organizations develop, validate, deliver, and operate software applications. Therefore, it has become necessary for organizations to consistently innovate better ways to deliver high-quality software while optimizing time and money. The innovative solutions should be capable enough to provide the development teams with good testing strategies to determine the quality of their software. Automation testing is a highly successful method to validate the quality of the software and help the testing teams to efficiently run a large number of tests in a short period of time. This is the reason why automated testing is prevalently adopted by organizations that strive to deliver the best to their customers.
What are the Trends to look out for in 2020?
AI/ML in Testing:
AI is expected to be omnipresent in every domain of innovative technology. Experts anticipate that investment in AI can get sky-high, which is something like USD 190.61 billion by 2025. Many IT organizations in the market are dependent on artificial intelligence for its unique features that make the work easier and simpler. AI has annexed the software development industry and the outcomes are better than what it once used to be. Unlike the traditional methods of software development that requires the developers and compilers to convert the actual code from a high-level programming language into machine language to make it easy for the hardware to follow, AI fastens the process by regenerating the code to make it legible for the hardware. In addition, Robotic Process Automation (RPA) is the ideal solution for any organization that deals with users, data, and systems such as feeding information to multiple systems, invoice creation, or application handling. It makes it possible by using the software that enables a metaphorical software robot to mimic human activities whenever complex and redundant tasks are automated. Bots are another example which when install in software can supervise the seamless and error-free process of the software by ensuring for any modifications in coding or suspect the presence of any malicious codes. By embracing such AI-based solutions, these tasks can be completed within a few hours instead of months.
The fusion of AI technologies such as advanced machine learning, deep learning, and natural language processing, and business rules can create a direct impact on all the sections in the software development life cycle. AI is capable of elevating the overall software lifecycle to the next level where AI handles designing new applications using the right tool, architecture and user experiences and scrutinizing the business value and impact for the organization.
The Internet of Things (IoT) is a network of dedicated physical things capable of accumulating and sharing data across. These connected ‘things’ are not just general-purpose devices like smartphones or PCs, but dedicated-function objects, such as vending machines, jet engines, connected cars and so on. The digital nexus gathers data and events from which an organization can learn behavior and usage, react with preventive measures, or augment or transform business processes. This is the reason why IoT is looked upon by many organizations and believed to have a huge impact on the economy by facilitating new business models and improving efficiency and employee-customer relationship
In order to support IoT devices to communicate well with each other and perform seamlessly, it is crucial to develop and deliver high-quality products. Since IoT paves way for multiple users from various platforms to access, the chances of data theft, cyber-attacks, and device malfunctioning are higher than usual. Secured and seamless performance of these devices can only be achieved through thorough end-to-end testing, IoT testing that involves Protocol and device interoperability testing, security and privacy testing, network testing, performance, and real-time testing, and end-user usability testing which are very crucial to address the challenges associated with IoT devices.
Big data describes the large volume of both structured and unstructured data that inundates businesses on a daily basis. The weightage of the data that flows is not what matters but what organizations do with the data do that can often lead to better decisions and strategic business shifts. The usage of big data is bloomed across various sectors like healthcare, banking, technology, retail, telecom, media, and many others. For example, ecommerce business giants like Amazon, Flipkart, and many others have countless visitors every day to buy or check out thousands of products. Amazon uses big data to store and manage information or data regarding the products, customers, and purchases.
With more and more humongous volumes of peculiar sets of data incoming, it is not enough to rely on relational database Instead organizations are in desperate need to have resources that have high skillset and expertise. Funding and retaining resources with higher salary costs while trying to meet project needs at the same time is a feat and this is when big data testing comes to rescue since it can easily handle humongous of data that vary in formats and volumes. Big data testing is the validation of big data applications and datasets which involves various tools, techniques, and frameworks to process. Testing a big data application is more of verifying the data processing more than testing the individual features of the software product. The need of the hour is a sound testing strategy to cope with big data that is more likely to rule the testing landscape in the year 2020.
Performance issue in an application or site is ubiquitous and often the reason behind this is the developers who have a very minimal idea about where they go wrong while building the code. Performance Testing may not be the right solution since it is not competent enough to address the challenges that occur while software application development that is far ahead in the future. The challenges that Performance Testing fails to resolve can be addressed with the Performance Engineering solutions that help organizations to expedite application delivery. Performance Engineering (PE) allows the basic elements and building blocks to be put in place early in the Software Development Life Cycle (SDLC). The best feature of PE is the decoupling system involved that helps the testing teams to detect the slowest component and optimize the overall performance of the system. This eventually aids the development/testing teams to perceive the change in providing better performance.
Using Performance Engineering, organizations can build more scalable software applications that can accommodate growth, provide the best user experience and enhance Return of Investment. This is made possible by implementing Performance Engineering solutions that understand every part of the system and builds performance from design to deployment. Testing approaches like Shift-Left Testing, Continuous Performance Testing, Capacity Planning, Performance Benchmarking, Performance Monitoring, and Framework Design are the key features of PE. Tailored frameworks can help several CIOs to deliver quality results in no time, enhance customer value while boosting return on technology investment.
Ensuring the security of any application, designed for desktop, mobile or IoT, is more important than ever which is because the stakes for a data breach have become enormous. Any organization is expected to uphold security in order to make the applications more resilient to the existing cyber threats. Applications or sites should be tested in terms of security to help development and testing teams to avoid any last-minute identification of vulnerabilities that may easily lead to delay in fixing the issues and delivering the product.
The solution to beat these challenges is to implement security testing that comes as a savior to both the development and testing teams to build secured and good quality applications. Implementing security testing earlier in the software development lifecycle helps the developers to identify issues promptly during the development phase and testers to have a better grasp of the risks and threats to the application. This often leads to faster mitigation of vulnerabilities, less last-minute defect fixing before the release, and decreased time intervals between releases.
Latest posts by Christina Sridhar (see all)
- Thinking of Migrating to Cloud? Adopt the Right Testing Strategy for Faster Roll Out! - October 6, 2020
- Cloud Migration Testing: Things You Wish You’d Known Earlier - September 18, 2020
- Robotic Arm 2.0 : The Most Innovative Thing Happening in POS Testing - August 24, 2020