Assurance Of Quality Software Testing With Artificial Intelligence
Software testing can be defined as an imperative process of validating and verifying a software application that meets the necessary conditions of the client’s business and the technical requirements. An application can be observed under certain conditions in which understanding the threshold and the risks involved in the software implementation can be studied. The software testing now with the help of artificial intelligence does the rerouting whenever needed for the product design and development, safeguarding the application against potential application failovers and making sure the application works as expected.
Major topics covered in this article are:
- What is Artificial Intelligence?
- Why do we Need AI in Software Testing?
- Phases of AI software testing.
- Advantages and Importance of AI testing.
1). What is Artificial Intelligence?
Artificial Intelligence is the new trend which is eventually evolving by spreading its wings in every major industry. Artificial Intelligence can be defined as the science of making computers perform the tasks that earlier would have required human intellect. Artificial Intelligence is capable of making the machines to process information about its environment, by helping to adapt to the changes intelligently, in the similar manner the intelligent humans think. Now with AI, the computer can be fed with a huge amount of data to get accustomed to new conditions as per a set of inputs so that it can identify patterns and logic, and get trained to accordingly make the valid connection between inputs and output.
2). Why do we Need AI in Software Testing?
In the area of development Software testing is a very fundamental process that constitutes a fundamental aspect in the area of development. Due to the paucity of time and resources developers are unable to carry out an exhaustive testing of applications, it is then the requirement for an intelligent system arises that identify the areas that could be handled through automation based on repetitive patterns.
3). Phases of AI Software testing
- Process: Testing is a not a single activity, rather it is a systematic collection of processes.
- Software Development Life Cycle: The software development undergoes six phases before completing the model
- Requirement gathering and analysis
- Implementation or coding
iii. Static Testing: Static testing is done initially without having to execute the code. It can test and find defects during the verification process. Static testing includes reviewing the documents, source code and static analysis and is the most useful and cost-effective way of testing.
- Dynamic Testing: Dynamic testing is done during the validation process. Here the code is executed to demonstrate the result of the running tests.
- Planning: Planning is a major part in order to control the test activities, report the progress on the testing and the status of the software under test.
- Preparation: Preparation helps to choose and decide what testing can do, by selecting the conditions where testing is performed and in designing the test cases.
vii. Evaluation: Evaluation is a continuous process which checks the results. Evaluation of the software decides the completion criteria and analyses whether the testing process is completed and checks if the software product has passed the tests.
4). Advantages and Importance of AI testing
It is a commonly known fact that humans make mistake, and mistakes are unavoidable. We need to check everything that we do because things can always go wrong. Some mistakes can be ignored, but some can be dangerous and can be expensive too. With the help of Artificial Intelligence creating systems that exhibit intelligent behaviour is possible. The AI learns, demonstrates, explains, and implements human intelligence in Machines. AI creates systems that understand, think, and learn like humans.
- AI testing is fast and cost-effective. A developer can feed in huge amounts of data to test for various functionalities.
- AI has inherent nature, and automatically discovers and evaluates every new feature.
- In a short span, AI-based testing provides the phenomenal amount of test cases.
- AI testing eliminates business risks by anticipating and by amplifying capabilities, ensuring operational efficiency and quality early in the project cycle.
- Intelligent insights are provided like application stability, failure patterns and predictions, defect hotspots etc.
- AI is not hardcoded hence during alterations it automatically determines if the changes made are new features or are bugs.
- Solutions provided in software testing using AI.
- Test suite optimization distinguishes duplicate, similar and unique test cases.
- Predicting the next parameters help to predict key parameters of software testing processes based on historical data.
- Identifies hotspots and automatically execute test cases using Log Analytics.
- Identifies complex scenarios from the requirements traceability matrix and extracts keywords to achieve test coverage using traceability.
- Analyzes data from social media and provides an interactive visualization of feedback trends.
- Identifies risk areas which help in the risk-based prioritization of regression test cases in the application.
- Using self-learning algorithms, Improved quality, Prediction, prevention, and automation is achieved.
- Significant reduction in efforts enables complete and faster delivery with test coverage.
- Scientific approach towards the localization of defects, and early feedback with unattended execution.
- Identifying dead test cases for the changed or redundant requirement.
- Adaptable to client technology landscape is enabled by integrated platform.
Artificial Intelligence is not a buzzword in the field of information technology today, and “JanBask Training” lets the testers to be equipped with the tricks and trends of software testing. By working in harmony with AI,the software testing field will open up to the most interesting and valued aspects of testing. Human testers and machines to exist simultaneously to find a balance will bring in a different thinking to the future of testing.
Contact: Manchun Kumar