Software testing is an imperative process that ensures customer satisfaction in an application. It is the only premeditated way in test automation where an application can be observed under certain conditions where testers can understand the threshold and the risks involved in the software implementation. Testing helps to safeguard and application against potential application failovers which may prove to be detrimental to the application and the organization in the future. Software debugging can essentially be carried out once an application has been thoroughly tested.
Testing, however, is slowly transitioning to greater automation to ensure maximum accuracy in the journey towards digital transformation. In a bid to make the application failsafe, we are turning more and more towards Artificial Intelligence (AI). As testing increasingly moves towards greater automation, we may be turning over most of it to Artificial Intelligence (AI). This categorically implies that instead of manual testing done by humans, we are slowly moving towards a scenario where machines will take over writing and execution of test codes. With little human input, however, will be required to help machines ‘learn’ and improve themselves.
AI is the new buzzword which is gradually spreading its footprints in every major industry. Essentially put, AI is the science of making computers perform tasks that would have required human intellect in a traditional scenario. AI gives machines the ability to process information about its environment, perform ‘intelligently,’ and learn to adapt to the changes. We can now feed the computer with a huge amount of data to acclimatize as per a set of inputs so that it can train to identify patterns and logic and accordingly make the valid connection between similar input and output pairs.
Machines are now learning faster than ever given the latest technological advancements. The computer can now easily analyze copious amounts of data, form patterns with it and make effective connections by on its own with the help of some stipulated algorithms.
It is through machine learning that Facebook customizes the content by deciding which stories would be most relevant to a particular user and modify the newsfeed accordingly. Websites like Amazon and Netflix use machine learning to deliver targeted ads and content to their customers by using enormous amounts of data based on the web pages that the customers visit, or what they search for, and what they buy on the platforms.
Machine Learning has transcended a long way with people using it to identify cancerous tumors and driving cars without a human, it is now paving the way for AI to gain a stronger foothold in literally every industry. As AI begins to gain ground, techies across the globe are finding ways to accommodate its potential in the area of software testing. It is only a matter of time when training applications to recognize problems for faster and more efficient testing becomes a reality.
AI bots can effectively communicate using human inputs and can also be trained for more complex tasks like testing software. AI could prove to be extremely beneficial in plugging the gaps of inadequate infrastructure for testing results, high failure rates and costs to increase the efficiency in the testing processes.
Why do we Need AI in Software Testing?
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.
Pros & Cons of AI in Software Testing
Like all things, AI in software testing comes with its share of good and bad. One of its biggest selling points includes the instant feedback mechanism. Since manual software testing is a long way off in its evolution shift to agile and DevOps, it’s simply not achievable for manual testers to give agile developers immediate assessment on how their constant inputs and alterations to the application impacted the existing user experience. AI application can also effectively help in generating and optimizing test cases, prioritizing testing and automation, enhancing UI testing and reducing tedious analysis tasks.
Though the advantages of AI are manifold, it has found its share of critics for a looming possibility of job cuts and the lack of intervention due to judgment. AI intervention in software testing almost certainly will displace several from low-skilled jobs. Its intelligent adaptability will also leave little control for human involvement in making judgment calls to exclude special cases. For instance, when Sydney went through a deadly cafe siege in 2014, people called for Uber taxis to help them get out of the area. However, the software displayed surged prices based on its demand and supply algorithm with no consideration of the prevailing crisis.
Manual Testing v/s AI Testing
Let us examine the advantages of AI testing in answer to the shortcomings of manual testing. Manual testing often relies on the tester role-playing as the end user to ensure correct application behavior. On the other hand, an AI approach to testing is built on the solutions to the very problems that are inherent to manual testing and thrives on change.
As AI finds its way into software and automation testing, organizations are still contemplating whether or not to embrace it in their product engineering practices. Since humans are good at creativity, exploration, understanding, analysis, and the application of knowledge, these are the areas that will see being catered by them, with the rest moving to AI.
Once the initial investment to set up an AI system in test automation is made, organizations will be able to generate greater testing rewards for less money. These savings can then be invested in increased QA innovation efforts like testing uncovered areas and devoting more time to exploratory testing.
A good way to approach this would be through retraining humans while monitoring AI bots and their results. In the long run, artificial intelligence will be able to help not just software testers working to deliver top-quality products to the market, but all roles involved in software development. Thus, human testers don’t have a reason to worry about their careers becoming obsolete. They only need to start thinking differently about the future of testing.
However, it is important to find a balance. While AI has arrived in the American workspace, it will still be awhile before machine-learning technology takes over wholly as it lacks intuition. There’s a gut instinct that can’t be duplicated via algorithms, making humans an important part of the puzzle. While human-powered testing is important for the creativity it introduces in the process of identifying bugs, most testing is repetitive. It is here that AI and automation can come in to make things easier. This can free up testers’ time to focus on the more interesting parts of software testing.
By working in harmony with AI in the future, human testers will see the most interesting and valued aspects of testing open up. Therefore, the best way forward is for humans and machines to exist simultaneously, leaning on each other’s strengths.