People in the tech world are very well aware of the emerging AI tools and trends in the IT world. Moving ahead from this awareness people in our world have started benefiting from the latest AI tools and technologies. From the software engineers to the customer support folks, from chatbot integrations in software to plugins being launched, we can find AI tools and tech all around. Artificial intelligence is steadily advancing across various industries and business cases. The more artificial intelligence enters our lives, the greater the need to test software, websites and applications using it. Take the example of a self-driving car: if the car’s intelligence does not work properly, makes wrong decisions or reacts slowly, it is easy to cause a car accident and put people’s lives in danger.
Using AI in software testing processes not only helps improve the end service to end users and helps software production stakeholders work better with less time and effort. Software testing is one such domain which got big help in leveraging AI technologies to improve the QA process.
Artificial Intelligence (AI) has become so famous recently that it doesn’t need a formal tech definition now. It can be simply explained as an area of computer science that emphasises the creation of intelligent machines or software that work and react like humans. This human-like work and react tendency is what QAs nowadays are embedding into their workflow using AI tools so that software testing can become more efficient and streamlined with minimal human intervention.
Following are how QA Engineers are benefiting from AI tools and software:
- Test Automation
QAs can automate their repetitive tasks by identifying the tasks, evaluating automation tools, developing automation scripts, testing and refining the scripts, and integrating them with the CI/CD pipeline. AI language models can provide suggestions for automation tools and help with the development of automation scripts, which can help QAs save time and effort in their daily tasks.
- Defect prediction
AI algorithms can assist in defect prediction by analyzing large amounts of data, including software development and testing records, historical bug reports, and code changes. By processing this information, AI tools can help identify patterns and anomalies that may indicate potential defects, allowing developers to take proactive steps to prevent or fix issues before they occur.
- Test case optimisation
AI can be used to optimise test case selection and prioritisation, helping QA engineers focus their efforts on the most critical areas of the software system.
- Test case generation
AI models understand needs and translate them into different types of inputs and outputs. Therefore, it will help automate the entire process of writing test cases and reduce human error and time. It can create current events to ensure needs are met properly. AI models not only help with generating generic test cases but also helps in generating unit cases.
- Test data generation
AI tools can help generate comprehensive and diverse test data that covers all possible scenarios, edge cases, and combinations. This can ultimately lead to better test coverage, improved software quality, and reduced time and costs for testing.
- Understanding Tech Jargons
For a person new in the tech world or who has switched the field, it’s often difficult to understand the abbreviations or terms, for example, a term like RTMP Streams. AI language models these days can help understand tech jargon for QAs by providing clear and concise explanations of technical terms and concepts. These models can assist in understanding technical terms, providing examples and suggesting resources.
- Answering Common Questions
In the day to days lives, QAs have a lot of common questions like “How can I improve my testing process?”, “What are the best practices for writing effective test cases?”, “How can I ensure that my tests are effective and reliable?”, “What are the latest trends in software testing?” and many more like these. AI models give very concise and bring answers to such questions which we cannot get without less effort from internet surfing.
- Identifying trends and issues
AI tools can help QAs with identifying trends and issues by analyzing data, using natural language processing, predictive analytics, and providing customized reports and dashboards. By leveraging these capabilities, QAs can gain valuable insights into their testing process and take proactive steps to improve it.
- Drafting PRD Outlines
This document is typically created by product managers and serves as a roadmap for developers and testers to build and test the product, but QA engineers should also be aware of how PRD can be drafted because it helps in understanding product requirements, collaborating with project managers, being aligned with the development work and accordingly prioritizing testing. Due to a lack of experience and expertise in the management part, it can be difficult for QA engineers to draft the PRD on their own. This is where AI models come in handy.
- Explaining Use Cases
Writing effective use cases is an essential skill for QAs. By identifying the actors, defining the goal, writing the steps, including alternate flows, and reviewing and revising the use case, QAs can ensure that the software meets the needs of its users and is thoroughly tested. The AI language model can assist QAs write the use case of the desired feature in the syntax best suited according to the company and project requirements.
- Success & Error Messages
Knowing the expected and desired messages in a particular flow on a website or in software is also a part of the QA job and AI models help understand the language of success and error messages that should appear in a flow.
- Writing release notes
With the help of AI language models, writing release notes has become an easy task. Release notes can now be generated automatically with a good and proper choice of words within a few seconds. AI can also help write multilingual release notes. AI language models can help QA teams save time and effort, improve the quality of their release notes, and provide a better user experience.
- Suggestions for an A/B test
A/B testing is one of the components of the overarching process of Conversion Rate Optimization (CRO), which is used to gather both qualitative and quantitative user insights. AI models provide insights based on data analysis and trends, which can be a valuable resource for QAs in suggesting effective A/B tests.
In the end, we all need to remember that AI tools and tech are not sentient, these are very clever tools which can look extremely smart and almost sentient when it works but can also cause you to believe in false information when it fails, and that’s why it’s extremely hard to trust anything it produces without the oversight of information verification. So while you can use it to generate “Tests” or understand a topic, the snippet of code or anything really, always take the information with a pinch of salt and try to verify it from other sources before you trust its credibility.