Software Testing and AI/ML as a good recipe.
Hello Readers,
The important topic that we are going to discuss today and the topic that everyone is talking and discussing these days and also the title of my blog will also give you an idea of what recipe we are going to make in this blog.
Software testing continues to evolve owing to the constant pursuit of quality. Earlier, testing was a manual process, with testers being required to methodically develop and carry out test cases line-by-line. This technique was sluggish, prone to human errors and unsustainable at scale, particularly as software complexity advanced.
Then, automation testing came into the picture and manual testers also started automating test cases and using automation tools to save testing time and deliver the product in less time.
Today, the development of test automation has reached an intriguing chapter with the introduction of artificial intelligence (AI). It has transformed testing automation by intelligently creating and sustaining test cases, identifying errors, automating repetitive processes, collecting test data, analyzing test performance and delivering comprehensive results. In many respects, artificial intelligence has elevated software testing to new levels of efficiency and effectiveness.
Let’s try to understand with example
Imagine you’re teaching someone how to use your app. You’d explain where to click, what to expect, and what to do next, right? AI does something similar but in a much more sophisticated way. It learns everything about your app, from the obvious paths users might take to the not-so-obvious ones. Then, it uses this knowledge to create test cases — scenarios that need to be checked to ensure the app works as intended.
This method is brilliant because it uncovers scenarios we might not even think of. It’s like having an uber-creative team member who says, “What if a user does this unusual thing?” and then makes sure the app can handle it.
AI and ML in software testing
As technology continues to expand itself into various facets of computer science, artificial intelligence and machine learning can be implemented into various stages and features of software testing. Nowadays, software testers may employ AI/ML to accelerate testing processes by maximizing these technologies’ human-level decision-making skills. Consequently, this may provide significant advantages to QA teams who know how to implement it effectively. Although AI/ML is not an entirely novel technology, it has advanced considerably in recent years, and there exist several instances and applications for its utilization.
AI/ML test use cases
There are several ways to employ AI/ML to enhance software testing, and the key to unlock those capabilities is to understand what these technologies may potentially accomplish and then discover innovative methods to incorporate them into day-to-day testing operations. Here’s a look at some of the ways in which these technologies are transforming the testing process efficiently:
Test case recommendation: Machine learning’s value lies in its capability to learn from data and generate predictions without being explicitly programmed for every possible scenario. This implies that as time passes, AI develops a better understanding of how people engage with the app on which the tester is working. It continues to modify its test creation process to comply with the specifications set by the company. Furthermore, it determines the standard ways by which a tester examines things by analysing the rules, existing tests, and historical data before recommending the best test cases for the given situation.
Predictive analysis: Keeping track of test script updates after modifications to the application can turn into a hassle. AI excels at identifying patterns in massive datasets. Using AI and ML technologies, testing teams can analyse code changes and automatically update test cases to react. These technologies also assist in proactively analysing product usage and faulty data to identify where the product is most likely to encounter issues. AI then recommends regular testing of certain locations to detect flaws before customers discover them. This predictive strategy of testing enables organisations to stay ahead of maintenance requirements and provide higher-quality software.
Faster development cycles: AI is propelling the growth of low-code test automation technologies, which make test creation accessible to non-technical users. With a low-code testing product, anyone can create automated tests by demonstrating the steps required on a real mobile device. AI then develops a reusable test script that can be executed on several devices. Moreover, low-code tools enhance the capacity of automated testing across the organisation.
Debugging: AI can effectively identify errors based on patterns and defect records, conduct root cause analysis, pinpoint the source of the issue and provide alternative solutions. Implementing an AI-driven autonomous testing system can accelerate the entire software testing process, resulting in increased productivity, accuracy, and cost savings.
Difference
Artificial intelligence (AI) and machine learning (ML) are often used interchangeably or as synonyms. This is the wrong approach. Let’s first understand the definitions of artificial intelligence and machine learning.
Artificial intelligence is a technology that allows computers to learn from experience, adapt to given parameters and perform tasks that were previously only possible for humans. The first research in the field of AI was carried out in the 50s of the last century, at the same time the term “artificial intelligence” arose.
AI automates repetitive learning and searching processes through the use of data. AI is not a stand-alone application, but a technology that is being integrated into existing products. For example, the technology of intelligent assistants like Google assistant, Siri, Alexa were added to existing products: Google search engine, Apple devices, Amazon platform. AI in general can improve various existing technologies and products.
Simply put, machine learning is a pattern recognition technology. It works with those templates that are determined by algorithms, and predicts future trends. ML can process huge amounts of complex information, find patterns, classify data, create predictions, and warn of possible risks or discrepancies.
AI/ML in test automation: how does it work?
here are several basic machine learning algorithms for test automation. Let’s see what they are.
Supervised learning. The machine receives training datasets with correct answers. The set includes data with information about the number of commits, tests and their results, code coverage, releases, the coverage of features, and fixed bugs. Also, this machine learning algorithm is often used to measure potential risks.
Unsupervised learning. The machine makes decisions by working with demarcated inputs. This algorithm helps to identify the possibility of an error, as well as to understand if the data in the same cluster are similar to each other, and simplify the data.
Reinforced learning. In this case, quality assurance engineers train the neural network on the reward and punishment mechanism. In case of an error, the work of the function is corrected so that the next time it chooses the true option. In this case, the more you teach the neural network, the fewer mistakes it makes.
Benefits of AI/ML in Testing
The Benefits
- Comprehensive Coverage: Ensures no stone is left unturned, covering even the most unexpected user actions.
- Efficiency: Saves a ton of time. Instead of manually thinking up and writing out test cases, AI does the heavy lifting.
- Quality Improvement: By predicting user behavior, AI-driven tests help make apps more user-friendly and bug-free.
In the real world many Tech and non-tech companies are implmenting AI/ML in their business or taking as a service.
Let’s understand that the AI/ML in the testing with basic example. We already know the Chat-gpt and other AI based tools in the market. Please read this blog for better understanding.
Consider a scenario where QA teams verify the entire application quickly using AI/ML-powered automation, freeing testers to focus on tasks requiring human intelligence.
While many AI and ML tools are designed to automate testing processes, some can assist with manual QA testing tasks by providing insights, recommendations, and other supportive functionalities.
One such tool is Test Craft.
Here’s how to generate Test Cases for any of the screens using Test Craft:
Step 1. Install the TestCraft extension in Google Chrome
Step 2: Open the Instagram website, activate the TestCraft Extension, and click “Pick Element”.
Step 3: Select the Phone Number text box to confirm element selection.
Step 4: In TestCraft, click “Generate Test Ideas” to generate various test cases for the selected element.
Step 5: To automate test cases, select desired test cases, choose a tool and programming language, and then click “Automate.”
Then, we will get below result.
What should you know about the pitfalls of AI/ML?
Do not think that AI/ML technology takes testing teams into fantasy worlds. There are also issues here.
Lack of data leads to false results. AI/ML technology works based on the data it has. If for some reason there is not enough data, all the work on the implementation of artificial intelligence will go down the drain.
Changes in the application that are difficult to adapt. During development, the product is constantly evolving, and this is a serious challenge for AI/ML. For this technology to be useful, it must adapt to the changes that have been made. In practice, this is difficult to ensure.
The accuracy of the algorithm needs to be checked. To understand how accurately AI/ML works, involve experts in this field. They will measure the accuracy of the technology and suggest what needs to be done to make the technology work as efficiently as possible. Without overcoming this challenge, the expediency of using AI/ML becomes low.
Final thoughts
The journey with AI and ML in automated testing is as much about the destination as it is about the voyage of discovery, learning, and adaptation. By selecting the right tools, embracing continuous learning, and leveraging community knowledge, organizations can navigate this journey successfully, turning the challenges of today into the triumphs of tomorrow.
Today, many top companies like Alibaba who is using AI/ML in their products Artificial intelligence (AI) is integral to Alibaba’s daily operations and is used to predict what customers might want to buy. With natural language processing, the company automatically generates product descriptions for the site.
Not only is Amazon in the artificial intelligence game with its digital voice assistant, Alexa, but artificial intelligence is also part of many aspects of its business. Another innovative way Amazon uses artificial intelligence is to ship things to you before you even think about buying them.
Thank you for reading my article. I would like to here your thoughts on a various topic like AI/ML, Testing so post your feedback in the comments.
Thank you.
