AI and ML algorithms are transforming every sector and every aspect of human lives. With the help of AI and ML algorithms, software testing has also seen significant improvement in test coverage, test effectiveness, and test results.
AI applies problem-solving and reasoning to software testing which improves the test automation quality as well as product quality. Let us now try to understand some ways to use AI in test automation.
What does AI in test automation mean?
It is an advanced software testing technique in which AI and ML algorithms are used to automate the software tests. With the help of AI-bots testing process becomes fast and with the help of ML algorithms, machines can mimic user behavior and learn from past actions.
This results in faster tests, better test coverage, and earlier detection of defects along with faster delivery of high-quality software.
What are the benefits of AI in test automation?
1. Improves test coverage –With the help of AI in test automation, test coverage is significantly improved. AI bots can run 1000+ test cases at a time which increases the test coverage
2. Increases testing accuracy – With the advent of AI in automation testing, repetitive tasks are handled effectively. This eliminates the chances of errors and improves the overall testing accuracy
3. Saves time, money, and efforts –Software testing includes a lot of repetitive tasks which are time-consuming and takes a lot of effort from testers. But with AI-based testing, repetitive tasks are handled properly
4. Ensures early bug identification – AI helps in early and fast bug identification which ultimately reduces the defects and makes the product bug free and reliable for end-users
5. Enables faster time to market – AI-based tests are fast, accurate, and reliable. The time and cost involved with regression tests are considerably reduced. AI and Ml supports continuous testing thus, products are released faster which helps the enterprises reach the market early
What are the different ways to use AI in your test automation?
Below mentioned are four ways in which AI is being used in test automation:
1. Differential testing – It is a software testing technique in which similar inputs are given to a series of applications, comparisons are made about the application versions overbuilds and a difference in execution is observed.
2. Visual testing –It is a software testing technique in which the look and feel of an application is tested by leveraging image-based learning and screen comparisons capabilities of AI and ML algorithms
3. Declarative testing – It aims at specifying the intent of the test in a natural or domain-specific language. It only specifies what needs to be accomplished
4. Self-healing automation – It is automation within automation. In this, the self-healing automation tool applies AI and ML algorithms to dynamically adapt testing to changes in an application’s user interface (UI) or environments.
Leverage AI testing from a next-gen QA and independent software testing services provider to get high-quality software.
Which agile principles are the most important?
Well if we look at each of these principles, we will find that each of these principles is important as these principles depict business reality and human behavior.
Some of these principles have evolved by time as the agile manifesto was developed in 2001. But in the year 2021, the software industry has access to more advanced technologies and automation testing methods.
Teams are now able to deliver a better quality product using the combination of automation methods and agile principles. For instance, the principle that states – working software should be delivered from a couple of weeks to a couple of months has evolved with time.
With advanced test automation techniques such as AI and ML-based testing, RPA testing, etc. software is delivered within few days not even weeks.
Also, the principle that states – most efficient and effective method of conveying information to and within a development team is face-to-face conversation depicts an old way of working. Now teams are virtually connected even at a global level and work in harmony with each other.
Teams can deliver a quality product on time by leveraging various kinds of connectivity methods such as video calls, instant messaging, team collaboration tools such as slack, kanban board, etc.
Apart from these, we believe each principle is still used by enterprises as it helps them to deliver a quality product to the customer faster.