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Will artificial intelligence replace QA and IT testers in the future?
The emergence of large language models (LLMs) has revolutionized the field of software development and raised questions about their potential impact on software testing jobs. With the ability to generate content and code instantly, these models offer developers new wave of productivity, leading to increased software production. Test automation has already replaced testers doing the work of robots, such as running the same tests repeatedly. But the testers have not disappeared. Their time was freed up to design more and better tests and, of course, to automate them.
But the question remains: will artificial intelligence large language models (LLM) replace software testers entirely? In this article, we’ll look at the impact of these models on software testing and discuss how testers can leverage artificial intelligence to increase their productivity and efficiency.
What is artificial intelligence – AI?
Artificial intelligence (AI) is a field of computer science that deals with the development of intelligent agents, which are systems that can perceive their surroundings and react to them in a way that is similar to how humans do.
AI addresses a wide range of problems, including:
- Image recognition: AI can be used to recognise objects in images or videos.
- Speech recognition: AI can be used for human speech recognition.
- Language translation: the AI can be used to translate between different languages.
- Gameplay: the AI can be used to play games such as chess, go or poker.
- Automated decision-making: AI can be used to automate decision-making processes.
There are many different ways AI can be implemented. One of the most common ways is to use machine learning. Machine learning is an AI method that allows a system to learn from data.
AI has the potential to change many aspects of our lives. It can be used to automate tasks that are now performed by humans and can be used to create new products and services.
Artificial intelligence vs human IT testers
Winning this fight seems impossible, but the positive thing for testers is that:
- While artificial intelligence can automate certain types of testing (read the ChatGPT and Test Automation article), there are still aspects of software testing that require human expertise and judgment. Especially when dealing with complex scenarios or user experience problems.
- AI models need to be trained and updated to keep up with evolving technologies and new software development practices. Human QA testers are often better equipped to adapt and learn new technologies quickly.
- AI models rely on data and can inherit biases from training data, which can lead to biased testing results or overlook certain issues. Human testers can provide critical insight and consider ethical implications during testing.
- Human IT testers play an important role in working with developers, product managers, and other stakeholders to understand requirements, provide feedback, and prioritize testing. Effective communication and collaboration skills are essential in software testing, something that AI tools can struggle to reproduce.
- An entry-level test engineer can outperform the current GPT-4 model for software testing. Our human generalized skills can give us an advantage over the ChatGPT model. But how will it affect jobs if it cannot surpass current levels? This is a question worth asking and exploring.
Artificial intelligence meaning in software testing
- AI technologies have made significant progress in writing programs, although they are not flawless. However, they have significantly increased productivity of developers.
- This increased productivity results in a greater volume of software produced in less time, which creates more opportunities for software testing.
- In addition, some AI startups have even started generating unit test during the development phase, which has led to improvements in the quality of applications over time.
- However, it is important to note that generative AI can introduce bugs. While it can help find bugs early, it cannot completely replace the expertise of human testers.
- The second part is tools to increase Quality Assurance productivity. Currently we can generate test cases and test data e.g. using the juniorqa site, thus increasing the productivity of software testing.
Over time, many more tools will appear and there will be an eruption of software production that will improve software testing productivity. AI algorithms can analyse huge amounts of data and perform large-scale test scenarios that might not be feasible for human testers alone. This can improve test coverage and better identify potential problems or defects.
- Tools like GitHub Copilot can be used to create automation scripts to increase automation productivity.
Although artificial intelligence will not replace software testers, it will certainly increase their productivity. Assuming it will double the productivity, we may only need half the current number of employees, resulting in job losses for about half the workforce.
However, the counter-argument suggests that if AI improves productivity, we can get more projects to test rather than just double the workload. Nevertheless, it’s critical to recognize that project scope, schedule, resource availability, and business priorities affect the overall delivered software , which goes beyond the productivity of the developers alone.
Given these factors, the introduction of AI in software testing is likely to change the stability of the number of jobs, but it will not replace testers 100 percent.
Artificial intelligence in software testing
Artificial Intelligence is increasingly coming to the fore of software testing thanks to its ability to automate repetitive tasks, analyse large amounts of data and discover patterns. AI-powered testing tools and frameworks are being developed to support testers in their work, increase productivity and improve the quality of software products. Let’s take a look at some of the main ways AI is being used in software testing.
Test Automation – Test automation is one of the main ways AI is used in software testing. Automated test cases can be generated and executed by AI algorithms that have been trained to recognize patterns in software systems. Compared to manually creating and running test cases, this is more efficient in terms of time and effort. In addition to being able to adapt and repair, AI-driven test automation systems are more durable and resilient.
Test Case Generation – By examining the program under test, AI can help in generating effective test cases. AI algorithms can learn from previous data, accurately identify key program components, and provide test cases that account for different scenarios, using techniques such as machine learning. In this way, test coverage can be increased and hidden bugs can be found.
Predicting and analysing errors – Artificial intelligence can help in analysing and predicting software errors. By analyzing historical defect data, AI systems can find trends and anomalies that indicate potential vulnerabilities. This can help in prioritizing testing efforts and focusing on key elements or functions where problems are more likely to occur.
Performance testing – Artificial intelligence (AI) can improve performance testing by modelling large-scale user interactions and stress testing situations. More accurate performance evaluation is possible thanks to the ability of AI algorithms to create realistic load models and dynamically adapt to changing workloads. In this way, system resources can be optimized and performance bottlenecks and scalability issues can be found.
Faster defect identification – Did you know that the most serious problem in QA today is the sheer amount of data that testers have to analyze in a given period? And most of the average tester’s work (sometimes as much as 80%) involves examining hundreds or even thousands of cases in the regression process. On the other hand, new AI tools could reduce the time it takes to find bugs in software. This is because large volumes of data are used when moving from manual testing to an AI test automation platform. And you’ll be pleased to know that AI loves big data, because it can easily learn from them. And the more it can learn, the better it can use different testing scenarios.
Reducing development costs – Regardless of the methodology, product quality and fast product launch are top priorities in modern software development, and many project managers know what it takes to ensure both. The good news is that scaling test automation can significantly reduce development and testing costs. AI-driven testing also has the potential to reduce these costs even further. This is because it pinpoints the specific areas or elements of the application affected by the version and identifies the test cases needed to test these areas.
Increased Accuracy – Traditional testing still requires human resources, and let’s face it, even the most experienced QA engineers are prone to making mistakes. Because of the large amount of data, testers stop focusing on software quality assurance and overlook some important bugs. This is where AI and machine learning technologies come in. They teach systems to learn resource analysis and apply knowledge in the future. This is how the AI tester can get more accurate results with each iteration. Using AI technologies to analyse data therefore eliminates the likelihood of human error.
Improved visual regression testing – Visual testing verifies that an app’s user interface looks as it should in a variety of scenarios such as devices, web browsers, and screen resolutions. (For more information, see the article Aplitools and AI Testing.) This process can typically produce inconsistent results due to small changes, such as a 1 or 2 pixel variation in an element, which triggers false alerts. Some platforms use artificial intelligence algorithms to improve the analysis of app content. These systems collect information that saves a lot of time over time by detecting significant differences while ignoring minor changes that other tools might flag as a change.
The human element
Although artificial intelligence has great potential for software testing, it is essential to remember that humans are still an essential component. Although artificial intelligence is perhaps the best technology in the world today, human intervention is still needed to achieve perfect results. The testing process benefits from the critical thinking, ingenuity and subject matter expertise contributed by human QA testers.
They can do things that AI alone can struggle to do, including exploring unfamiliar territory, recognising usability issues and assessing the overall user experience. As a result, the future of software testing is likely to be collaborative, with artificial intelligence working as a complement to human testers rather than as a replacement for them.
AI and software testing – challenges and limitations
The use of AI in software testing is not without its challenges and limitations, despite its potential benefits. A few limitations are listed below:
Ethical and bias issues – AI systems may not be able to account for ethical issues in software testing, such as privacy issues or bias detection. Human testers are better qualified to handle these ethical issues.
Limited understanding – It could be difficult for AI to understand the broader context of the product being tested, which includes domain knowledge, user expectations, and specific industry needs. This limitation can lead to testing findings that are either false positives or false negatives.
Large time and financial investments – Time and money are required to train and configure artificial intelligence tools for a specific application. Companies with fewer resources or smaller software development teams may not be able to afford these expenses.
Lacks human intuition – Because it helps testers approach complex problems from multiple angles, consider different perspectives, and identify hidden difficulties that are difficult for AI to replicate correctly, human intuition plays a key role in software testing.
A glimpse into the future
The future looks very promising. The symbiotic relationship between artificial intelligence and human testers in software testing is expected to continue. The testing process will continue to evolve and will be expanded with artificial intelligence to automate repetitive tasks, increase efficiency and offer insightful data. Higher-level tasks such as exploratory testing, usability evaluation and strategic test planning will be the main areas of focus for human testers. The joint strategy will result in a more robust and higher quality software application that leverages the capabilities of both AI and human testers.
Conclusion
In conclusion, artificial intelligence is revolutionizing the software testing industry by automating processes, creating test cases, anticipating problems, and improving performance testing. We don’t think AI will completely replace human testers. Despite these early glimpses of AI’s potential, its integration into existing processes still faces challenges. Privacy concerns, trust in automated outputs, and the incorporation of new systems are just some of the obstacles organisations must overcome if they want to reap the benefits of AI. We also need to overcome fears that AI will rob us of our jobs, because AI tools and services are no substitute for the creativity and critical thinking needed to produce stable and reliable software. This is where testers will always have an advantage over automated systems.
We believe that a collaborative strategy that uses artificial intelligence to complement the skills of human testers is the direction that software testing will take in the future. By combining the benefits of AI and human testers, software development teams can increase productivity, improve quality and reduce time to market for their products. That’s why AI is here to stay, not to eliminate software testing, but to improve it.
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