Artificial intelligence continues to create opportunities across almost all technologies that seemed impossible in the past. We can now explore the universe, diagnose diseases and program self-driving cars. And, with thousands of other use cases across all industries, that’s just the start of what artificial intelligence can achieve.
What will AI accomplish next? That decision is up to the companies ready to take artificial intelligence to the next level. And now that we’ve reached a new decade, those changes are happening sooner than later.
Back in the beginning, artificial intelligence was described as a program or machine that could perform any human task that required the application of intelligence to carry out the activity. This definition is considered too broad by today’s standards—after all, AI has advanced rapidly since the 1950s.
Today, artificial intelligence is the application of a machine or program responding to the real world with behaviors consistent with human responses by relying on the human traits of contemplation, judgement and intention.
In essence, artificial intelligence can be summarized with these four consistent traits:
Artificial intelligence and machine learning are often used interchangeably in conversation, but there are distinct differences between the two. To remember which one is which, note that machine learning is actually a subset of artificial intelligence. In other words, many systems using AI technology often apply machine learning to advance capabilities and continuously refine AI behavior.
Machine learning is an intricate system of algorithms designed to receive inputs, produce outputs, review outputs and then adjust the system’s original algorithms to produce even better outputs. In simpler terms, machine learning is the practice of machines or programs adjusting future behavior based on the results of their experiences.
Deep learning takes this a step further. With deep learning, data undergoes multiple non-linear transformations to obtain an output. Think of “deep” as “many intricate steps” to reach a conclusion on how to respond more effectively in the future.
So, should you apply machine learning or deep learning to your AI practices? That depends on the size of your data set. If it is relatively small, machine learning is a great starting point. Deep learning is essential if you have a massive amount of data, if that data has a variety of features and if accuracy can make or break your company.
All of this follows the process known as data science. While artificial intelligence incorporates the practice of data science to improve AI technology, one is not a subset of the other. In fact, companies across all industries, even those not using AI, apply data science to their daily practices.
Data science is the study of a sparked curiosity carried out by examining data related to the question, then processing, analyzing and visualizing that data in order to make meaning of it for IT and business strategies. In summary, it is the practice of understanding and making sense of data through a structured analytical process. That’s why other tools outside of artificial intelligence apply data science, including statistical tools, probabilistic tools and software testing tools.
There’s no slowing down in software development. Teams are always aiming for faster deployments and more efficient processes without cutting corners or sacrificing time, human resources and capital.
Is this even possible?
Not with traditional testing practices of the past. More than half of tests executed during a project are to verify that the software’s already-existing behavior performs as expected. And it’s these tasks that are often repetitive and prone to human error, often costing companies more than anticipated in terms of lost revenue or brand damage. Relying on human testing alone often slows down testing cycles, delays launch to market, increases the likelihood of software defects and inflates user frustration.
By incorporating artificial intelligence in software testing, your team can automate tedious test cases so that you can just about guarantee accurate and consistent results. In turn, your team’s testers can reallocate their time and attention on test cases requiring user interaction, human creativity and the ability to reason.
But isn’t AI testing expensive?
Not according to the numbers. Even with the best of intentions, human testers make mistakes when testing, from not executing the steps correctly to overlooking crucial data because they may be too close to it. It’s these errors that can lead to costly consequences in poor software performance, security risks and reduced customer satisfaction.
Essentially, investing in AI testing is more cost-efficient because it prevents larger, more expensive errors that can cost your company thousands, if not millions.
Leveraging AI in software testing can revolutionize your software testing practices, allowing your team to deliver to market at a faster rate without sacrificing software quality. Explore some of the advantages of artificial intelligence in software testing below.
Mistakes are bound to happen during manual software testing. And when those test cases require performing repetitive steps over and over, errors are inevitable. AI testing in software testing is designed to perform the same test steps every time with full accuracy and provide detailed, documented results. With an AI tool executing monotonous test cases, manual testers can spend their time exploring the software application and testing more complex features.
The role of AI in software testing is to increase the overall depth and scope of tests, thus improving the quality of your software. AI software testing can quickly scan through memory, file contents, data tables and internal program states to conclude whether or not the software is performing as expected. At the same time, AI testing tools can deliver more triage information. Thousands of different test cases can be executed at the same time, surpassing test coverage that manual testing can provide.
Whenever the source code changes, software tests must be repeated. Manual testing these instances can quickly add up in terms of time and money. Or some organizations brush over this altogether or ask their developers to run basic tests, which can be risky. Software testing using artificial intelligence lets your team create the automated test once, then execute the test repeatedly at the same time. No additional cost is required, time is saved and a faster delivery to market is achieved.
Artificial Intelligence in software testing allows developers to catch problems quickly so that the issues are resolved before reaching the QA team. AI testing can run automatically with every source code change, alerting your team if any of the tests fail. Not only can artificial intelligence testing increase your team’s confidence in releasing your software to market, it can also improve the communication between your development and QA teams.
While AI testing technology is growing and evolving every day, there are less-than-ideal consequences to consider. We always encourage QA teams to examine all outcomes, including those less than ideal, before applying artificial intelligence in software testing.
AI testing does not guarantee to fix all your testing problems. The role of AI in software testing is to execute test cases that are otherwise performed manually. If the problem cannot be solved manually, then it likely cannot be solved by AI testing.
The problem has to first be addressed manually before AI can perform testing. In short, your artificial intelligence testing can only do what it is told to do, nothing more. If insufficient instructions are delivered, AI testing will deliver insufficient results.
Incorporating AI testing in software testing is an investment. The price of AI tools alone starts at $6,000 and can be as costly as $300,000. This, of course, doesn’t include the expenses paid to keep the system up to date and meeting industry standards. This alone can sway companies of a smaller size or those with tight budgets from ever pursuing artificial intelligence in software testing. This is when you should consider a QA testing partner like QASource who can help you set up a viable program with the right resources, methodologies and best practices for your business.
Artificial Intelligence testing performs what the AI tool is programmed to do. Its intelligence doesn’t go beyond the algorithms and programs stored internally. Manual testers can apply reasoning and problem-solving skills from test case to test case, but this cannot be expected from your AI tool.
Software testing using artificial intelligence may lead to a reduced need for human testers to review test cases. That is often a valid concern, however, this often means that a human testing team can re-allocate their time and focus towards manual tests that require reasoning and problem-solving. It’s hard to imagine AI testing taking over for all testing forms and more and more, a hybrid model is implemented.
Automated testing exists so that repetitive tests can be executed by machines while more complex test cases can be examined by humans with intelligent thought and reasoning capabilities. Many companies interweave test automation in their testing practices because the end result is higher efficiency in testing, higher productivity across teams and lower costs.
Think of artificial intelligence in software testing like a marriage between manual testing and automated testing. AI testing still follows the procedures of test automation by delivering fast results for thousands of monotonous test cases. Like humans, the role of AI in software testing is to go beyond the simple command of following orders.
AI takes test automation to the next level. Since AI imitates the intelligent behavior of humans, AI testing is designed to recognize patterns, learn from experience and select appropriate responses based on the current situation. Not only is AI testing in the context of software testing completing thousands of repetitive, monotonous tests at a rapid speed, its application of artificial thought processes catches errors and defects throughout the software that often go undetected with traditional test automation.
Not sure if your team should proceed with software testing using artificial intelligence? While AI testing can be applied to many test cases, it may not be the right decision based on resource allocation and project scope. Review your test cases and see if they meet the following recommended AI testing criteria.
An AI-based application is software technology that incorporates the behaviors of human intelligence, from learning and planning to social interaction and creativity. AI-based applications are intelligent systems that learned how to perform specific tasks since its initial programming because of its skills in pattern recognition, memory knowledge and language processing.
AI-based applications can be found across industries, from financial and banking to healthcare and agriculture. Because of AI-based technology, applications today can detect credit card fraud, predict future market behaviors in the economy and develop life-saving abilities.
AI testing in software testing refers to the testing activities for software systems using well-defined quality validation models, methods and tools. The goal of AI testing is to validate the health of the system’s algorithms, functions and features so that its behaviors do not stray from the expected user experience.
To achieve this, AI software testing maintains the following primary goals:
With AI-based applications, complications in testing are inescapable. While test cases can remain relatively the same from project to project, AI-powered applications are constantly evolving in behavior and ability. The trick to testing AI-based applications is to not let the application act smarter than your testing practices. This means recognizing the challenges that can hinder testing AI-based applications.
Because AI-based applications are constantly learning, the expected behavior of the application alters continuously. In other words, how an AI-based application is supposed to act yesterday can be different from how it acts today, making yesterday’s testing results insufficient for today’s needs.
Tests must be regularly reviewed before execution so that the performed tests actually confirm that the AI-based application is performing as expected for today’s behavior. Without the proper testing practices in place, this can be a drain in both cost and time.
How do we know that the system under test for the AI-based application is correct? When it comes to traditional software, this insight is available from a source of truth, such as a Business Analyst, Product Owner or a Stakeholder, because the behavior of the product has been mapped out and predetermined.
However, behavior predictability isn’t always the case with AI-based applications. Because they develop more skills based on past experiences, the anticipated behavior of AI-based applications is everchanging. This can make it difficult to integrate the expected behavior accurately within testing practices.
AI-based applications gain skills based on its exposure to real-time experience. But its these experiences that can land companies in hot water when applications encroach on personal data, such as payment details and unique identification information.
Companies must be vigilant in their personal data protection practices. Restrictions within the learning environments as well as within the AI-based application itself must be upheld. This can be challenging with changing data privacy laws across countries and regions, not to mention the evolution of the AI-based application’s behavior.
While refining the machine learning model improves the process of how the AI-based application learns, it is not enough for predicting future behavior. You need to check the accuracy of predicted behavior given by the machine learning model so that the learning process of your AI-based application doesn’t spiral out of control into dangerous behavior.
Is there a way to measure the trust of an AI machine?
Companies across industries apply a variety of validation methods to ensure that the learning abilities of the AI-based application advance with more foreseen certainty. The following evaluation techniques optimize AI outcomes and authenticate the AI-based application’s actions for stronger results.
This method, considered to be the easiest validation methods for AI models, helps you find how your model gives conclusions on the holdout set. A given label data set is taken and divided into test and training sets. Then, fit a model to the training data and predict the labels of the test set.
The fraction of correct predictions establishes the evaluation of the prediction accuracy. Remember to withhold the known test labels during the prediction process to ensure accuracy in the prediction process. Experts recommend not training or evaluating the model on the same training data set, as it can create a positive bias due to overfitting.
This validation method for AI models is often the go-to evaluation technique for big companies and large corporations. This method evaluates AI models by training a variety of AI models on subsets of the available input data, then evaluating them on the similar subset of the data. This approach is designed to detect the fluctuations in the training data that is chosen and learned as concepts by the model.
While this evaluation technique for AI models can be relatively expensive because of the workload required for validation, it can also provide insightful results. This validation method uses all the data except one record for training because that one record is solely used later for testing only. In this scenario, your team can use your entire data set for training and testing, thus the error rate is almost average of the error rate of each repetition.
Companies that offer validation services often use this validation technique when evaluating on the behalf of their clients because it can be repeated an unlimited number of times to ensure accuracy. With this validation method for AI models, data is randomly divided into training sets and tests sets multiple times, meaning multiple sets of data are randomly selected from the data set and pooled to form a test data set and a training data set. Once each set is established, the accuracy obtained from each data set is averaged and the error rate of the model is the average of the error rate of each repetition.
This validation method for AI models is effective for diverse situations such as evaluating a predictive model performance, ensemble methods or estimation of bias of the model. Under this evaluation technique, the AI training data sets are randomly selected with replacement while the remaining data sets not selected for training are used for testing. The error rate of the model is average of the error rate of each repetition.
AI testing is the future of software development. Successful companies adopt best practices in software testing using artificial intelligence by teaming up with a reliable QA service provider like QASource. Our team of testing experts specializes in all aspects of AI automation testing and can help your business achieve reduced time to market while ensuring accuracy and scalability. Learn more or Get a free quote today.