Artificial Intelligence has graduated from being just a technical tool and concept to a fully-fledged buzzword. This is the same case with Machine Learning (ML). These two words (or technologies) have inspired all spheres of life from philosophical debates to great films. More importantly, AI and ML have made it possible for all human activities to develop incredibly, be it gaming or medicine. AI companies are working around the clock to find ways of using this technology to make human life as comfortable as possible. And in this spirit, software testing hasn’t been left behind. But how is AI and custom software solutions used in testing? Read on.
It isn’t uncommon for Quality Assurance Officers (QAs) to waste a lot of time ensuring that as they write new codes, they don’t disrupt the codebase. Every time developers add a unique code, testing must follow to ensure everything still runs smoothly. Regression testing consumes a lot of time, especially when conducted manually, and can be a great burden to the QAs.
Traditional software testing involves listing tasks to be performed then checking them one by one as they get done. This is the only way to ensure that the system will continue working as expected at the end of the exercise. This method works well only if there are only a few features to deal with.
However, with many features to deal with and some of them being very complex, this method isn’t feasible, and comprehensive tests can never be conducted. This particularly true because humans are prone to err, and the crafted list may also not be complete.
Sanity tests, regression tests, and other repetitive tests can only be done successfully by automation of the testing process. There are many tools for doing this, and with a little research, you can quickly identify some.
However, AI is taking over the software testing process. With AI, software testing can be done in the most comprehensive manner, and with no errors. Not only that, but the tests can also be done fast enough to ensure the QAs work within their timeline.
AI and Software Testing
There are several ways in which AI can optimize software testing. The most common methods used by many AI companies include:
Manual testers go through thousands of codes to conduct software testing. This is not the case when AI is used in this process. With AI, log files can be sorted in a matter of seconds, and errors detected.
Furthermore, AI doesn’t get exhausted and is error-free, meaning the results generated will be more accurate. When you use AI in your repetitive tests, you can focus on testing the new features and be more attentive only to the software parts that matter most.
As a QA, your core duty is to ensure any new code you design doesn’t cause any instability to the existing codes. As systems develop and new features get incorporated, the line of codes to test increases. This will overwhelm you, even if you have a large team of QA engineers.
The best way around this is to go the AI way. Through machine learning, AI develops as new features increase. AI bots can detect any modifications to the codes and can learn and adapt to new functions. AI can also be programmed to detect whether the change in codes results from new features or defects in the existing codes.
Better implementation of client requirements
When it comes to software testing, it is essential to understand clients’ needs to ensure whatever you implement addresses those needs. With AI, you can easily monitor what goes on with their competitors’ apps and websites, and identify what makes the apps and websites successful.
AI will also help you study thousands of competing products to identify their most vital selling points. As a developer and tester, this will help you know what kind of functions users are looking for in software.
After developing the software, you can use AI to detect the flaws in coding that might prevent the software system from running seamlessly. Additionally, after understanding what clients need, you can develop a test case to ensure the final product never fails when put into practical use.
A software bug can cause a system or computer program to produce unexpected or incorrect results. Bugs can cause a program to freeze or crash a computer. When this happens, a company can be in a total mess. It is quite costly to fix bugs. Luckily, AI can detect bugs early enough and give instant feedback. This way, the bugs can get fixed before things run out of hand.
The Systems Science Institute (IBM) reported that fixing a bug after the product has been released costs four to five times more than it would cost if it was discovered during design. And it will cost 100% more if the bug is detected during the maintenance phase. With this information, you can imagine how expensive it will be to fix the bug after the system has crashed.
The gaming company, Ubisoft, already has an AI tool that instantly alerts programmers whenever it detects a potential bug during coding. The tool detects patterns associated with coding mistakes, then alerts the programmer to take action immediately. Without this AI tool, Ubisoft can spend up to 70% of its game development budget on bug fixing. Meaning, the company benefits a lot from AI technology.
AI Tools used for Software Testing
There are many AI software testing tools in the market. AI Companies that offer digital solutions like Icreon can help you identify which tools can suit your unique needs. Some of these tools include:
- Eggplant AI
Some of these tools are web-based, while others are app-based. They all work differently but are all important in software testing.
AI is the future of software testing
It is worth remembering that the use of IA in software testing is a work in progress. AI is a relatively new technology, and many engineers are still fine-tuning it to fit their unique needs. That’s why you need to monitor advancements and innovations in AI to help improve your business operations.
As far as software testing is concerned, many developers and software testers are already enjoying this technology’s benefits. AI has introduced more incredible speed and accuracy in software testing, making the lives of developers and testers easier.