Trends in Software Testing in 2023 and our Predictions for 2024
Testing is one of the most important stages of software development, as it helps to keep products at the proper level of security, speed and usability. There are a huge number of ways to test program quality, and every year new tools and trends in this industry appear, so testers need to constantly improve their skills and follow the trends.
This article is about trends in testing that have emerged over the last 10 years, the current trends of 2023 and what will happen with the software testing evolving for 2024.
Table of contents
Topical software testing trends that occurred over the last 10 years
- Mobile testing
- The Internet of Things (IoT) and scriptless tests
- Mobile web testing
Testing trends in 2023
- Using Agile & DevOps
- Implementation of QAOps
- Using AI in software testing
- Test Automation
- Moving to testing services and APIs
Software Testing Perspectives in 2024
- More test automation
- More AI usage for testing?
Topical software testing trends that occurred over the last 10 years
Every year software market analysts conduct numerous studies that reveal popular trends in testing, and experts regularly propose new standards to keep software quality at the highest level. Many of the trends written below became firmly established in development and remain relevant to this day.
In 2014, mobile applications took a special place in the software industry, and they started making multi-billion dollar profits for various companies. Today we have a huge amount of mobile devices with different types of operating systems, architecture, screen size and hardware functions. In this regard, the requirements for security, speed and convenience increased for mobile applications, which led to the need for quality testing of such products.
The Internet of Things (IoT) and scriptless tests
A year later, in 2015, there was a breakthrough in IoT and smart devices started to actively enter the market. In 2021, there were more than 20 billion such devices active, and by 2030 there are expected to be more than 50 billion devices. This is why a big trend in IoT testing emerged, which is still relevant today.
Moreover, there are so many smart devices that developers and testers thought about automating some processes, and in particular in evaluating the quality of software products. So in 2015 the trend of scriptless testing, i.e. creating tests for software using convenient tools rather than code, began to develop. This approach allows you to quickly and efficiently test a large number of functionalities under the conditions of product scalability and not to spend a huge amount of time and money on additional programming for these purposes.
Mobile web testing
In 2016, Google began to give preference in the mobile devices search results to those sites that are adapted for smartphones. Since then, mobile web testing has become one of the topical trends and every year it acquires new aspects and features due to the development of technology and increasing demand and requirements of users.
Testing trends in 2023
All three of the above-mentioned software testing trends make it to the lists of the most important trends every year. However, there are also new aspects that every tester should definitely consider along with them. We tell you about a few of the most important trends in testing in 2023, which have become an integral part of the work of QA engineers around the world.
Using Agile & DevOps
Recently companies adopted new methodologies, among which are varieties of Agile methodologies for managing product development and DevOps practices and processes.
In a nutshell, Agile is a group of approaches and methodologies aimed at more flexible development of software products. They include shorter feature realization cycles, regular meetings, planning and breakdown of already created functionality for continuous improvement of the team. DevOps is a set of practices that bring development and operations teams together for unified product work.
The combination of Agile and DevOps methodologies allows companies to improve the quality of their products more effectively and bring them to market faster.
Implementation of QAOps
In the last couple of years, the industry has started to talk more often about QAOps, which brings QA specialists together with product development and operations teams, i.e. a kind of unification of quality assurance and DevOps processes. A few years ago the testing related tasks were apart from the continuous software development cycle, now QAOps introduces testing tasks directly into the continuous integration cycle. A couple of years ago, companies were only discussing the introduction of such practices into their workflows, but this year it became clear that the introduction of QAOps in most companies is a matter of time, and we will definitely hear more about it soon.
Using AI in software testing
Artificial Intelligence (AI) and Machine Learning (ML) became an integral part for everyone, not only for regular users, but also for developers. In the last several months, AI tools have impressed many people with their performance, as demonstrated by experiments on software development with ChatGPT and image generation with Stable Diffusion models.
AI and ML algorithms are now gradually beginning to be found in testing tools as well, and they are being actively introduced by companies into their workflows. According to Gartner about 40% of companies are using AI-based automated testing for their needs. AI improves the quality of testing, helps to cover important aspects and features of the product, as well as show the QA specialist what to pay attention to and indicate the next steps. All this reduces the load on the specialist, lowers possible product development costs and increases productivity, which can positively affect the speed of product development and its quality.
Of course, specialists have only just started using AI and ML for testing, and it is too early to speak about full-fledged automation with the help of these technologies. However, neural networks are a very promising assistant for developers and QA-engineers. Where can AI potentially help them?
- Artificial intelligence is already good at generating simple tests for further use.
- Neural networks can help with selecting optimal parameters for tests so that they can be quickly reused for other parts of the product.
- Testers can use AI to predict future results, because neural networks rely on the huge amounts of data on which they have been trained and validated, and the results of previous tests, and can predict which functionality will pass or fail tests.
As we wrote above, 2015 saw the emergence of a trend related to automation of testing processes and the use of scriptless tests. This is an important aspect of DevOps and QAOps processes, so product development companies should not overlook it.
Why is test automation so important? In recent years, with the development of technology, there has been a trend to replace manual testing with automated testing, because manual testing becomes a big problem in the real world when you need to quickly release quality software product functionality. To avoid QA-engineers having to spend many hours on studying the nuances and specifics of programming languages, frameworks and other technologies for software development, and to deal directly with the tasks of software quality control, automatic testing without scripts can become a great tool in the hands of the user. Since automated testing is not so widespread (about 20% of tests are performed automatically), companies have a place to turn in this direction, for example, in regression testing, which, according to experts, should be mostly automated.
Now there are many tools for automating testing processes, such as Selenium and Katalon, which are regularly updated and supplemented with new features that can be convenient, simple and effective for users and QA-engineers.
Moving to testing services and APIs
For a long time, companies implemented various tools into their processes, and separate solutions were often required for each aspect of testing. This complex approach created a great deal of complexity and risk, as it was constantly spending a large amount of time and money to keep this complex system up and running, as well as to constantly train employees to work with it.
That is why this year many companies began to strive to implement universal testing tools and services that contain a large number of functions necessary for effective software quality assurance and that can be easily integrated into the working environment of the development team. If a team is actively using QAOps rules and principles, unified development tools can help QA-developers gather the necessary information from the product design and development phases themselves, including for AI and ML recommendation algorithms, to understand where to focus their testing.
Software testing perspectives in 2024
If we consider the perspectives of software testing technologies and methodologies development, we can consider that all the above trends will remain relevant and even increase their influence on the industry, i.e. more and more companies will implement them in their processes. Testing of mobile apps and websites, IoT and microservice part of web applications and also greater merging of testing processes with the continuous integration and product delivery cycle will remain relevant next year.
But there are a few trends that may not seem quite so clear-cut at first glance, so here’s a look at what may happen to them next year.
More test automation
QA experts agree: you should strive for 100% automation of testing with the help of scriptless technologies, because manual testing and writing code for tests greatly slows down the process of releasing software products to the market. Automated tests allow a tester to focus on important tasks in identifying problems related to software security and performance and perform more of them. All this will happen due to the advancement of technologies, AI and ML algorithms in particular, more unified development and testing tools.
More AI usage for testing?
We would like to say separately about the use of AI and ML in testing, because the answers to this question are not quite straightforward.
On the one hand, AI is a promising tool for many aspects of software development, including testing, because it potentially allows to speed up and simplify the testing procedure significantly, and, as it was said earlier, QA-specialists will be able to cope with a larger number of tasks. On the other hand, software testing is a very conservative industry, and the quality of the product, rather than the novelty of technologies for its verification, is the first priority for specialists from there. Of course, AI can be used to create simple tests, and many specialists actively use it, for example, as part of work with Copilot X, but AI and ML are still too new and unproven technologies to be fully used in testing, and careless use of the technology can lead to fatal consequences for the security of software and the data used in it.
This is why the predictions for the use of AI and ML algorithms can be formulated as follows: first of all, the use of AI should be firmly adopted in software development processes and be standardized, and as soon as this happens, these technologies will start to be actively implemented in testing.
No matter how fast software development technologies evolve, QA specialists should only rely on those that are time-tested and proven by the industry itself. Therefore, companies must actively incorporate current practices into the development process to improve their product development metrics. However, QA-engineers should maintain a conservative view on new trends and tendencies and carefully check that new technologies do not put the security and performance of products at risk.