IT companies and business leaders in the industry need to come up with more and more promising digital solutions as there are increasing pressures to be innovative and competitive. The growth of new technologies and demand for faster release cycles are driving the standard for quality software applications through the top. To stay ahead of the curve, QA teams are pushed to upgrade their testing processes to get the work done in a shorter time at lesser costs. Carrying out the testing process manually is highly time-consuming, tedious, and not very accurate results can be generated. The testing cost is directly proportional to the number of errors or bugs identified in the applications which are often quite expensive.
It is of paramount importance to know the kind of impact and expense of implementing test automation to decide how much coverage is required for a given project. Also, automation teams should continuously evaluate innovative technologies that can help them make the process more efficient within less time and cost and greater test coverage at the same time. Eventually, it reduces the time taken to bring a digital solution to production and allows enterprises and its QA teams 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 becomes a difficult task unless it is kept to a 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 many organizations, this costs a few mistakes, 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 a top testing strategy like Hyper-Testing that can scale up the testing efforts and integrate test automation with high-end intelligent frameworks like AFTA. Implemented with advanced AI/ML components, AFTA 3.0, a modernized version aims to overcome the challenges of conventional methods of test automation and avoid any last-minute error detection. This collaboration of Hyper-Testing and its framework, AFTA 3.0 helps to map an end-to-end quality process for the product development pipeline and is expected to play a bigger role in the organization’s TestDevOps agenda in the future. However, all these are time-consuming when done by manual testing.
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
- 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 have 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.
Also, the best AI tool 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.
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.
AI aims to help testing teams develop and test their code in a more efficient and effective way 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 needs to collect colossal amounts of data like the following.
- How many tests passed/failed
- How many tests were executed
- How much time a user spends in developing/executing test cases
- What kind of screenshots are generated from the tests
- What dashboard reports they can offer
- How much time a user spends with an automation tool before using 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.
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.
Our Contribution – Aspire, as a pioneer in the industry for decades now, has been helping ISVs to accelerate their software delivery using best practices of Agile, DevOps, and Shift-Left methods with best in class automation strategies like Hyper-Testing and frameworks like AFTA 3.0. Our Managed Testing & QA services take complete ownership of all the applications, migrate legacy apps to new-age apps, perform various types of testing like API Testing, Mobile Testing, Performance, Security Testing, and Functional Testing. It brings together Quality Engineering and development teams to deliver quality work and results like around 35% cost savings within the first year, 50% reduced test efforts, and 20% improved time to market.
Hyper-Testing and AFTA 3.0 for automation make 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.
To know how automated testing strategy and framework can help your organization to reduce testing efforts and increase ROI.
Latest posts by Naveen Srinivasan (see all)
- Cut Down Your Testing Efforts with Intelligent Test Automation Strategies - September 7, 2020
- Top 10 Challenges Retailers Face While Executing Their Testing Strategy - September 2, 2020
- How to implement a robust Retail POS Automation testing strategy - June 18, 2018