Broadly, there are two types of companies in terms of software testing: software development company that carries out all the tests during its software development and companies that simply outsource the testing activities to a preferred provider. While software testers focus on timely product launch by deploying the tested software as soon as it is ready for release, the software development company does not expect timely results due to the lack of a suitable test environment and limited resources. The test company usually follows a lengthy process of testing projects to reduce operational problems and costs. The company encounters many problems with the new project, as there is no suitable test environment and limited resources for test projects.
Software development has changed significantly in the last five years with the introduction of new tools, new technologies and new approaches to testing. Many of these changes have been triggered by increased customer demand for speed and reliability. While business leaders continue to seek a new lead, many are turning to new technologies and new approaches to testing. Simply offering advanced software is no longer a reliable strategy, as the market is increasingly saturated with powerful options. While technology companies set the bar for applications, customers use only the best products.
QA testing is a long-established principle in software, but traditional methods are no longer producing great results fast enough. The concept of testing remains the same: finding defects before the product is brought to the customer.
Traditional tests rely heavily on the technical and business requirements of a project to assess the basis of the code. The product must meet the specifications outlined before the start of the project, as well as several other requirements. Traditional tests do not consider the need for speed, which can lead to users switching products quickly to meet their needs. Modern users expect bug fixes, updates, and new versions in seconds, not just minutes, which can sometimes take just a minute.
The most effective companies use advanced analytics to transform their data and make testing more efficient and reliable. Companies set up such feedback loops, for example, by collecting user usage data, but also from every step of the test process, enormous data streams are recorded for analysis. The change is to look inward, not only at the test process itself, but also at the feedback loop between the test and the analytics.
Have you ever wanted a tarot card reader that predicts what you need to do to optimize your product or report a problem before it occurs?
Predictive Analytics is exactly that, allowing you to analyze old data and make more accurate predictions about the future performance of your software tests and products.
Predictive Analytics is a data-driven technology that can be used to predict test failures and determine the future. It has the power to optimize project data and allows business leaders to make fast strategic decisions. It aims to predict future results based on the current condition, user needs, and future performance of a product or service. The practice aims to generate future knowledge with high precision.
As the name suggests, predictive analytics is a type of analytics-based prediction or prediction of future events. Predicting unknown events based on the analysis of past data is therefore called predictive analytics.
In today’s world, there are many companies that must deal with the lack of predictive analytics in the testing phase.
Predictive analytics helps make software testing more efficient, effective, and user-friendly. Unlike other analytics methods, Predictive Analytics can help teams prioritize and streamline their testing activities. By using predictive analyses to understand users’ needs, organizations can focus the testing process on these needs, rather than devoting valuable time and resources to activities that have no significant impact on product results. You could put a product on the market and use a feedback loop to adapt to user reactions, but predictive analytics goes a step further, allowing you to make any changes before the product or feature is released.
Other analytical methods can be used in conjunction with predictive analysis but are also crucial for efficiency optimization and can also be used in conjunction with other analytical methods such as machine learning, machine intelligence, and data mining. For example, because analysis helps to determine the cause of defects and to generate a clear understanding of historical data. Predictive Analytics can then use this data to predict potential problems in the product and guide testers to higher priority activities.
The test organization must conduct predictive analyses to avoid productivity delays and problems by addressing the causes in the early stages. The development company, which outsources testing activities, prefers to focus more on its core business and avoid the ever-increasing costs associated with testing. If we look at the expectations of outsourcing tests, the company faces a lot of delays in delivery and costs constantly outstrip each other. Consider the challenges of internal audit and the expectations of outsourced testing.
The development company must conduct forward-looking analyses to avoid slow outcomes by identifying the causes of problems and making proactive decisions early on. Predictive analytics has become one of the most discussed topics in the software testing industry, as it can reduce operational risks and help with planning and quality delivery. It helps the development and testing company to find the right supplier, the right team, and the right project and to make a proactive decision at an early stage. This can also be used in software tests to significantly improve the business.
Cognitive automation, as we all know, is automating business processes through artificial intelligence. The scope of the process that cognitive automation can handle is wide, due to its flexibility it can be applied to automate any kind of processor operations. Embedded and empowered with various types of A.I. with cognitive functions, the automation that can be done is more than just mimicking keyboard strokes and mouse clicks or controlling monitor screen, but also other high-level tasks that usually require human knowledge. It is, for example, predictive analytics.
Cognitive automation, empowered with A.I. Machine Learning, enables new predictive analysis methods to transform the way development teams develop, test, and deploy powerful products that meet users’ needs. A.I. Machine Learning plays important role in making smart and close to 100% accuracy prediction in indicating if the software will be defective or not. Predictive analytics in software testing through cognitive automation is highly possible as this A.I. Machine Learning enables the robot to have self-learning ability, where the robot can be fed with data then understand the pattern. This understanding will result in a training model that will be used to make predictions on the future data, e.g. whether the software will be defective or not.
This predictive analytics in software testing, performed with cognitive automation, can be beneficial in:
Competition in the software industry is getting tougher, customer expectations are rising, and high-performance products are no longer dependent on advanced software to gain a competitive advantage. The focus is shifting to better internal processes such as testing that produce superior speed and reliability. This is possible by leveraging A.I. Machine Learning-empowerment in automating predictive analytics for software testing.
Check how Gleematic can perform predictive analytics to improve software testing!
Written by: Kezia Nadira