Software testing is a key factor in successful software development. The perception of software testing has shifted in a positive direction due to the rapid development in the last few years. Development companies have realised that quality assurance can save them huge costs.
For several years now, the world has been in the midst of a digital revolution. Almost every day we are witnessing huge technological innovations and advances. This will be the same in 2024. We will see new innovations in technology and approaches that will require companies to constantly innovate and reinvent themselves to keep pace with the digital transformation. Already, we can mention some popular software testing tools and methodologies favoured by businesses for their efficiency and optimisation. Are you ready for software testing trends in 2024? Whatever the answer, the news is already here!
The concept of QAOps
A few years ago, a new term, QAOps, started to spread in the software industry. By 2021, the buzzword has gained increased attention and acceptance. QAOps is predicted to play a strong role in the software development lifecycle in the coming years, so it's worth keeping an eye on how this area unfolds.
QAOps is a conceptual fusion of DevOps and Quality Assurance, integrating both processes into a single approach. Quality Assurance or QA, is the final gateway ensuring the delivered software meets high quality and customer expectations.
DevOps seeks to integrate software development with IT operations and maintenance. By integrating quality assurance into the DevOps cycle, the resulting unified process is called QAOps. This integration allows testers to collaborate with developers during the software development phases. The introduction of QAOps is supposed to usher a new software process model that will improve the overall quality of the development process.
Testing with artificial intelligence
AI approaches have been used for some time to address challenges in software testing. Recent developments in AI and data already offer unique opportunities to optimise testing practices. Nevertheless, the application of machine learning models in software testing processes is still in its early stages.
AI algorithms can help to write more accurate test cases, scripts, generate data and reports. In addition, predictive models can help decide where, what and when to test, following a kind of test selection model. Intelligent analytics and visualisations can help teams detect errors, increase test coverage and more accurately identify high-risk areas. In the future, AI-based models can contribute to improving the quality of source code, as well as the quality of testing and the efficiency of resource allocation.
For example, Chat GPT already has the ability to come up with varied inputs, life situations or specific cases that can help in the overall testing of the software. Chat GPT can also help you create documentation, including test case descriptions, user manuals, or even bug reports. This makes it easier to document and track tests. It also makes it easy to create automated test scripts. The potential for error is somewhat lower for this type of task, but documentation generated quickly and efficiently is worthy of expert scrutiny.
Robotic process automation
According to research and market surveys, revenue from robotic process automation is expected to reach $3.4 billion by 2027, a remarkable annual growth rate of 28.2%. But what do we mean by RPA? Robotic process automation, can automate primarily repetitive tasks that do not require manual intervention.
In the field of testing, this can take the form of. It initially records the actions taken by the tester to create the reference. Using a machine learning-based model, RPA repeats these actions in several scenarios on the screen. Its automated nature significantly reduces both time and unnecessary operational and testing costs in the long run for businesses, although the upfront cost can be lump sum and higher.
Test automation
This trend is not new, but is likely to become more widespread in 2024. It opens up new dimensions in testing by reducing the need for human resources and creating opportunities to avoid errors.
Most IT professionals believe it is time for quality assurance teams to eliminate manual testing and automate testing processes. Today, companies can find more test automation tools on the market, but the popularity of automation is still low. According to a 2022 Hungarian survey, 38% of companies surveyed think they should automate their processes to improve testing. It can be seen that there is plenty of room for improvement in the statistics.
In-sprint test automation
Agile methodology requires fast and efficient work and frequent release cycles within a sprint that usually lasts 2-4 weeks. But this pace often leaves inadequate time for comprehensive software testing within the sprint timeline. Testers often have to go through an earlier version in depth, which is a major drawback. Ignoring changes and then not thoroughly testing the software can lead to most bugs slipping in, potentially resulting in colossal costs. A single bug can cost up to 100 times more than the amount spent on testing.
In-sprint test automation addresses this challenge by allowing testers to work in parallel with the development process within the same sprint. This approach eliminates the need to wait until development is complete, so testers can start their testing activities during development. This helps to improve software quality and allows testers to align their testing efforts with the version currently under development.
IoT testing
The IoT refers to a network of connected physical devices that exchange data and communications, such as smart devices, self-driving vehicles or Wi-Fi connected household appliances. The exponential growth of IoT testing stems from an increased focus on security and functional testing to ensure the smooth operation and efficiency of these connected devices. The demand for IoT testing remains extremely high to verify that current IoT systems and modules continue to meet user expectations and perform their functions as intended.
Developments in testing
These are just a few of the many trends that are already evident in testing and will continue to be of great importance in 2024. We can also expect to see advances in areas such as low-code or no-code testing, cloud-based testing platforms and collaborative testing techniques.