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Software testing is a process that constitutes a very fundamental aspect in the area of development. However, many a time developers are unable to carry out an exhaustive testing of an application due to the paucity of time and resources. In such a case one can argue the need for a system that could intelligently identify areas that more elaborate and focused attention from the aspects that could be handled through automation based on repetitive patterns.
Software testing usually takes up the most amount of time, human resource, and capital. And with developers seeking faster deployments with inadequate infrastructure, AI is an appropriate way forward. Since 80% of testing is just a repetition of the checks the software already possess, AI is better equipped to automate it and take it on with efficiency instead of a human tester which needlessly inflates costs and effort. It would be a better practice if the human intelligence and effort used in identifying the application problems by creating unique and innovative test environments. Therefore, it is ideal to leave the repetitive work to the AI-powered automation which leaves only 20% of the testing operations to human creative and reasoning ability. This would ensure more foolproof results because hand-crafted testing not only requires extensive human hours but is prone to inaccuracies and inconsistencies. Consequently, manual testing faces scalability issues, requiring the management of several machines to run.
On the contrary, AI bots-based testing requires very little maintenance and are capable of discovering new paths through the product on their own. Software testing is just the right field for applying AI as testing, whether manual or automated. This scenario exists because testing incorporates lots of human and machine-generated data. With industries waking up to AI, developers are considering it to facilitate decision making, drive automation and improve efficiency in the area of testing.
AI algorithms can be immensely helpful in the testing industry in making a smarter and more productive software for the end-user. It is, however, important to decipher how to use AI intelligently. Algorithms that operate like an actual user accessing automation. After that, one must identify the areas within the process that can be optimized with AI and incorporate the algorithm. Having a smart algorithm can facilitate this process and help testers find the maximum number of bugs. The results thereafter can be used by the developers to refine the product and learn from trial and error.
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