Retailers are forced to innovate rapidly and deploy accurate on working digital solutions as there is increasing pressures to be innovative. The growth of new technologies and demand for faster release cycles are driving the standard for quality software through the top. To stay competitive, QA teams are pushed to enhance their testing processes to get more done in less time at fewer costs. Repeating these tests manually is cost much and time consuming.

The testing cost increases gradually with the number of errors or bugs detected in the applications which may be very expensive for the customer.

It's significant to know the costs and uses of test automation in order to decide what level of coverage is appropriate for a given project. Also, automation teams should constantly assess new technologies that can help them make the process more efficient and also pave way for less time and cost as well as greater test coverage. As an outcome, it thoroughly shortens the critical time taken to bring a digital solution to production, enabling retailers to achieve the competitive advantage much sooner while booming their results.

Reasons to Minimize the Cost with Automation

Rather than automating from day one, many prefer manual testing. Even though it makes sense, due to smaller upfront investment, manual testing quickly turns into a difficult task unless it is kept to minimum. The increase in project demands weakens the reliability of manual tests.

Therefore, the thumb rule is to always look for what can be automated. Whenever testing is done manually, be on the lookout for well-covered functionality and UI areas that automated regression testing can work with. This will gradually reduce the cost and time involved in test runs, and make manual testers concentrate on more productive tasks like exploratory testing. For the retailer, this costs few mistakes, a better customer experience and valuable brand protection alongside budding increased sales.

While considering automation for tests, things we need to keep in mind are:

  • How to write automated tests

  • How to quantify the costs

  • How to determine what all is really necessary

  • How to reduce those costs

  • Automation of new tests

To differentiate the tests that can be automated or done manually, drives to the cost-benefit. The biggest profits of automated tests include:

  • Less Time - Automated tests can be written once and run unlimited times that keeps time into the feature development

  • Better Onboarding – With an existing and reusable test suite in place, the new beginners can easily cope with it

  • Improved Reliability - Automated tests are run before each production deployment, which ensures the new code does not break anything on releasing into production

  • Faster Cycles - Automated testing increases the development speed and minimizes the need for any future bug fixing

Counting the Testing Costs

Three of the most obvious costs include:

  • Cost of writing tests - Testers need to spend ample time to write tests. However, the time spent to write the tests can add a great deal of cost

  • Environment maintenance costs - Test environments have costs associated with it, including server costs, license costs, and costs of various tools.

  • Test maintenance costs - Testers must maintain tests over time, which means spending more time on writing tests, which adds to the cost of the project.

How Automation Can Help

Automation testing is better than manual testing, for reasons like one could schedule the test case execution to run at any hour of the day, remotely from any location, and analyze the test results by reports generated based on the test suite execution.

Automation tests integrate with top continuous integration systems, like Jenkins, SCMs, and Git, as well as Azure and other DevOps tools. That can also scale the testing efforts by running tests concurrently on remote or virtual machines. All these are time-consuming in manual testing.

Test automation also can simulate thousands of users interacting with online purchasing web applications securely. This fact offers retailers security in the knowledge that their online sales platform will perform optimally even during periods of high demand without much chaos.

What it does

  • Record & Replay - Creates complex and mountable automated UI tests in seconds with a record and replay functionality powered by the most accurate and customizable object repository

  • Flexible - Choose between scripting and script less testing to create functional GUI tests with the flexibility to use VBScript, JavaScript, Python, or other languages

  • AI Recognition – Some hybrid object recognition engine leverages artificial intelligence to detect and test every application by finding dynamic and complex UI elements

AI to self-heal the execution of automation tests

On using AI, we've released an automatic test case generation that helps automation testers to fill in the gaps when starting from a sparse JUnit harness. With AI-enabled automation codes, testers can have higher code coverage while cutting in half the time and effort required to build a comprehensive and meaningful suite of Junit test cases.

It achieves this by making it easier to create stubs and mocks for isolating the code under test. The underlying AI component enables automation codes to observe the unit under test to determine its dependencies on other classes, and when instances of these dependencies are created, it suggests mocking them to the tester to create more isolated tests. Automatically creating the necessary mocks and stubs reduces the effort on test creation that is one of the most time-consuming parts of manual testing.

The best AI tool also automatically detects code that is not covered by existing test suites and traverses the control path of the source code to figure out which parameters need to be passed into a method under test and determines how subs/mocks need to be initialized to reach that code. By enabling, it can automatically generate new unit tests, applying modified parameters to upsurge the overall code.

AI track and prevent customer attrition by creating customer analytical records and detailed journey maps help in identifying which customers have deviated from their usual behavior and what should be the next best action. This helps the retailers in identifying the action that is most likely to achieve the objective.

Test data should be dynamic

Some automation framework has been widely adopted for UI testing, but still struggles from common testing challenges of maintainability and stability. This is where AI technologies can help, providing self-healing at runtime to address the common maintainability problems associated with UI test execution.

The end goal of AI is to help testers develop and test their code more efficiently and effectively, to create higher quality software at speed and thus reduce the cost rigidly.

How AI works best in Test Automation

AI in test automation gives smart insights like application stability, defect hotspots, failure patterns, and failed predictions.

Usability Testing - It is ensuring that software is compatible across devices, operating systems, and browsers with a comprehensive test automation framework. Manually testing could be time-consuming instead.

Security Testing - Test automation keeps away human errors that are crucial with regards to ensuring the system. Another advantage of implementing test automation is that it can screen even large applications for possible error zones. This saves a lot of time, resources, and money for the organization.

Influence of AI in Test Automation

It handles the data challenges with automated test data generation and management by test automation strategy. Data produced can be well used to check the test suite efficiently and introduce creative test case preparations. New testing methodologies like BDT and Model-Based Testing (MBT) helps in getting the most out of business value and convincing that automation strategies are aligned to business objectives.

AI Quickens Test Automation Process

By utilizing AI-led automation testing, 80 – 85% of the effective workload can be refused by testers, decreasing the stress of recurring tasks, and developing coding efficiency.

AI Excludes More Defects

With the help of AI, the requests regarding when and how the bugs enter the program are kept on hold. AI tests for bugs or defects and also recognizes minor changes that need improvement in the code. This improves the chance of bugs being exposed during the process of development. AI is used always to test mainly to remove bugs from codes.

Improve Software Testing Competencies

With AI, a set of regulations can be made to generate test data. Similarly, after having the initial data into an AI machine, different tests can be executed at each stage simultaneously to assure the reliability and stability of a program.

Creating More Reliable Automated Tests

Sometimes tests fail due to change in the application, such as renaming a field ID. But with AI, testers can use machine learning to adjust to these changes automatically. This makes tests more maintainable and reliable.

AI Analytics

AI needs to collect colossal amounts of data like how many tests passed/failed, how many tests were executed, how much time a user spends in creating/executing test cases, what kind of screenshots are generated from the tests and what dashboard reports they can offer, how much time a user spends with an automation tool before going to another tool, how were tests authored, how reusable the codes are, etc. We need enormous and efficient computing power to process all this data. Using these statistics and data collected on user preferences and multiple other user actions, AI recommends the options based on user’s usage patterns.

The Future

Automation testing is a cornerstone of high-quality software, but the tests don't come for free. The key aspect is to consider the costs and benefits of test automation before deciding on the appropriate testing strategy and coverage level for the requirement. At the same time, we should carefully consider tools that can help reduce testing costs.

DCqaf framework for automation makes testing and test automation easier and more accessible to cut down on time and cost. With an easy-to-use interface, test engineers can easily create reusable tests that scale and integrate them with automated testing frameworks.

“Schedule a demo to know how automated testing framework can help your organization to reduce testing efforts”

Nallammal Sampath

Nallammal Sampath

Project Leader

Janaki Jayachandran

Janaki Jayachandran

Vice President

Practice Head