This article highlights the need for machine learning in quality assurance and how machine learning can transform quality assurance.
- Machine learning in quality assurance.
- The benefits of using machine learning led quality assurance instead of traditional testing.
- The various types of machine learning algorithms used for testing.
- Various techniques used to check the software for quality and its benefits.
- The use of machine learning to improve software quality and performance by leading industry players.
There are many innovations in the software development industry in a bid to reduce the development cycle and release high-quality products. The transformation has already started with the rise of testing automation and hybrid applications. The next big thing in software testing is machine learning.
What is Machine Learning?
Machine learning is a statistical method that reads historical data to predict and optimize any process. It helps in data analysis and quality control. Black Box testing and white box testing can be done using machine learning. It enables computers to learn and observe patterns and data without the need for programming. The algorithm learns from the existing and uses the information to explains the current model.
How does machine learning work for Software quality?
Using machine learning to ensure quality assurance will give information on common errors, critical effects, and malfunction patterns that can risk software stability. ML assist in prediction and automation testing, where quality is tested at each step.
How can AI and Machine learning enhance software quality?
The machine learning testing strategy offers accuracy and efficiency benefits in the quality assurance journey. It helps in detecting redundant test cases, failed test cases, predict and prevent the errors in the code as well.
The key benefits in machine learning led quality assurance includes the following:
- Speeds up manual testing and improve the overall quality
The traditional application development has the code written in the thousands of lines, one after another. Manual testing each line of code takes weeks or even months and has higher chances of errors. ML helps in accelerating the entire process by writing scripts and analyzing data faster. ML can be used to easily manage files to enhance program accuracy and reliability. ML also provides access to a complete list of possible outcomes to check the alterations needed in the program.
- Automated Testing Process
Machine learning provides more test coverage to the test cases whenever there is a change or update in application. It reduces the additional effort required to maintain the testing. Testers can also use AI bots that can adapt and learn application functionalities. AI bots can detect any modification in the code and find the defects if any. Now, instead of using manual refinement process AI bots can be used to improve the testing process.
- Say no to bugs
QA engineers work hard to detect bugs, but due to one reason or the other, bugs remain unnoticed and can easily slip. AI in software testing evaluates test cases and various error incidents in a short span of time. This help testers to decide the course of action using the information gathered and assessed by AI-enabled bots. Testers can also track bugs due to change in code to avoid or apply changes.
- Reduces Ignored Bugs Probability
The biggest problem is overlooked bugs. This problem might seem small, but it has adverse consequences on a company’s reputation and business. The issue of ignored bugs often occurs due to a lack of time and negligence caused by testers in seeking bugs.
Machine learning offers a reliable testing approach by learning from previous bug patterns in code and applying it to a similar code pattern.
- Predicting Requirements
Machine learning can be a great help in understanding and forecasting client requirements. The correctness of client requirements can change the whole testing process as it will help testers analyze the customer data to full potential. Predicting gives more information on the company’s products and features as they need them.
AI and ML offer advanced features such as more revenues at a reduced cost, enhanced user experience, competitive positioning in the industry as the company delivers a high-quality product.
Machine learning concepts are now being leveraged in the quality assurance industry and testing as well. IT organizations accumulate a vast amount of data on defects to prepare test cases and execute them.
How do leading industry players use machine learning?
- Facebook has been using machine learning to find data predictions based on user interactions with the platform. Channels are using machine learning to mine the data and personal information to show relevant ads to explain to the end-user.
- Accenture has already been using machine learning to be ahead of the curve. A lot of financial firms using AI to check quality.
Quality assurance teams usually try to predict the type of defects which can occur in the software and zero in on the error-prone areas of application based on past testing and development experiences.
How to use machine learning?
Companies use machine learning and analytics to gain insights from data from project documentation, defect logs, test results, production incidents, test artifacts to improve the quality of the software.
Machine learning will improve testing efficiency by learning from the results and improve the testing cycle.
How machine learning ensure quality?
Machine learning models represent the data model that uses data from a given set and make predictions on the new data set by learning from the data. Machine learning models are created to work on the existing data set and forecast the patterns for the new one.
Here is the machine learning example where its models are fed with patient’s data(reports) from cardiac issues to predict the chances for patients whether suffering from cardiac disease. This method is also called supervised learning.
Machine learning for quality assurance uses neural networks. Neural networks are a set of layered algorithms which are adjusted according to the learning process. The learning process involves data inputs to create outputs and then comparing them with set results.
Systems are using automation to derive patterns from data and analyzing the vast amount of data to deliver quick and accurate results.
Different Classes of Machine Learning Algorithms
Here are the different classes of Machine Learning:
In the supervised learning algorithm, the machine is trained to use data. This method means the data is already classified and is tagged with the answer. The tools are trained with labeled data so that they can provide results with the new set of data using the previous data learnings.
A Machine evaluates the new set of data based on train data and sends results.
For example: If a machine is trained with data to find the purchasing power of the customers based on their age, income, and area of living. A new set can be checked on purchasing power as yes or no.
It has two subdivisions:
Regression: Regression models are used to make numerical predictions. For example, what would be the price of the stock on a given day?
Classification: Classification models are used to predict the class of a given data. For example, whether a person is suffering from a disease or not.
In unsupervised learning, the training of a machine is done with new data which is not classified or labeled, which enables the algorithm to work without any prior learnings or guidance.
The machine learning algorithm groups the unsorted information based on their understanding of patterns, similarities, and differences.
It has two subdivisions:
- Clustering: This is applied when the data needs to be grouped into a similar cluster of data. Based on the customer’s purchasing power or buying behavior.
- Association: This is applied when the data has some rules or associations with each other. For example, people buying bread are more inclined to buy butter as well. Based on their purchasing history.
In this type of learning algorithm, learning takes place from the machine’s own experience. There is no learning on the data, but the reinforcement agent defines the process and how to perform the task.
The process of quality assurance using machine learning has the following steps:
Discover: Make a data repository including defects, logs, tickets that are used for learning. Predicting the defects by analyzing the errors, logs, tickets, and other social data.
Learn Analysis of any relationship between test assets such as requirements documents and defects to produce insights.
Sense: It forecasts the occurrence of any defect or error incident, its likelihood produced through machine learning reports. Triggers responses which test scripts to check target features.
Respond: Creating a response to the input, resolution, and the results of the learning and finding the source from logs, tickets.
The machine learning uses the following techniques to evaluate software quality.
Test Scenario Mining
This method enables to execute the test cases based on the potential areas of application based on the likelihood to find errors.
It is a futuristic approach based on the risk-test based testing algorithm, which is the deciding factor for test case execution.
It increases testing efficiency, thus improving software quality. This method also reduces cost as it detects defects early.
Test Suite Optimizer (TSO):
Machine learning is used to optimize the test suite. The growing test repository with a high number of test information creates data redundancy. Test suite optimization reduces 15% efforts by eliminating redundant data. It reduces effort by reducing the regression suite to executing test cases on a time-boxed window. Structured risk-based testing checks the AUT against all the possible failures and risks.
Analysis of impact
It identifies the impact with less involvement of subject matter expert, absence of UML diagrams, establishing relationships between entities in a document.
ML offers benefits such as easy knowledge transfer, enablement during the support phase. It also provides simpler analysis, effective triage of defects, which involves prioritizing bugs based on occurrence, severity, and risk.
Customer Sentiment Analytics
This method is used to find end-user feedback to gain customer’s insights and their needs. This method finds rigorous issues that drive customer behavior. It also ensures real-time learning to provide a feedback loop.
It helps in improving customer experience with better risk management. It prioritizes the critical areas to improve testing efficiency.
Machine learning has the power to improve software quality and can be implemented to develop a better system based on customer’s feedback and experience.
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