Delivering software that performs well across different user types and levels of technological sophistication represents serious challenges in marketing these products. Artificial intelligence changes options for software testing, and it continues to open ways for improvement and innovation in marketing software products.
For any company, gaining new customers and delivering on promises to these customers determines your venture’s profitability and even survival. And, if your business delivers software as the key product or a significant element in achieving customer satisfaction, the testing component must ensure the software meets or exceeds customer requirements and that customers understand how to achieve benefits from the software with a superior UX (user experience) design.
Throughout the lifecycle of software development, software testing becomes inevitable. It aids diagnose any arising problems in the software that can deleteriously affect the company. Sure, you could spend hundreds of hours testing software prior to deployment or, as some firms do, use early adopters as human guinea pigs to encounter and report software failures. The first is very costly, and the second is potentially damaging to your brand image. AI (artificial intelligence) agents, called bots, reduce the cost of software testing, seek out vulnerabilities, and generally run the software through its paces to simulate the operations of multiple customer groups with potentially varied software applications and sophistication.
This testing process identifies improvements or changes necessary before software deployment and pinpoints when release is reasonable.
Today, artificial intelligence changes the software testing process radically.
This guide will show you the AI tools used for software testing and the opportunities available through AI.
Before we delve more in-depth into artificial intelligence changes, let’s start with some basics of software testing.
What is software testing?
Software testing diagnoses whether performance issues exist with the software and identify aspects for improvement. Software testing ensures products deliver as promised and meet company standards.
There are two types of testing:
Manual testing: For this process, humans identify faults in the software application by employing the software across multiple applications. Testers assess whether the software application aligns with established standards and whether performance satisfies clients’ needs.
Automation testing: This type of testing involves applying automation tools to assess software applications for faults and ensure that the software meets the business’s standards.
Meanwhile, there are two methods used for testing, and these are
- Static testing: For this approach, proponents review documents and files to ensure the software application process aligns with business’ standards. Among practices done as part of this type of testing are walkthroughs, inspections, and reviews.
- Dynamic testing: This type of testing validates the application of the software through simulated usage to ensure the software performs tasks as expected. This testing stage involves testing software in use.
- Firms might use multiple rounds of testing across both manual and automated processes that involve both static and dynamic testing before releasing a product to customers.
Why use AI for testing software
The application of AI in software testing tools is focused on making the software development lifecycle easier. Through the application of reasoning, problem solving, and, in some cases, machine learning, AI can be used to help automate and reduce the amount of mundane and tedious tasks in development and testing. [source]
Compared to traditional approaches, AI more easily diagnoses issues before the software reaches QA (quality assurance, which is often the last stage before release). Using AI helps prevent mistakes that might lead to severe consequences. Similarly, computers yield more accurate results as they produce reports demonstrating the types of testing run as well as following testing procedures without skipping or duplicating a step, as humans are prone to make such mistakes.
Therefore, AI software testing leads to more accurate results than when completed by human testers. Moreover, AI helps testers avoid repetitions, ensuring efficiency and saving time and resources.
Likewise, AI makes software testing faster. Unlike manual testing, the use of machines for software application testing happens much faster.
Artificial intelligence tools for software testing
Applitools: Applitools uses a sophisticated algorithm to monitor software applications. It functions well in the areas of engineering, DevOps, and Digital Transformations. Its primary uses are the diagnosis of faults in the software application and the identification of potential bugs.
Appvance: This is one of the most popular AI-laden tools today, and it’s known for basing parameters on user behavior when testing a software application. Companies use this to boost software applications’ security, performance, and quality. By speeding up software testing, Appvance also helps improve the agility of companies.
Functionize: Functionize is an AI software testing tool with cloud-based AI that helps improve load testing, performance, and functionality. It also boosts the pace of test creation maintenance and diagnosis. It adds on an NLP (natural language processing) engine to process every step of a test plan written in English. It then automates the plan.
Upon the completion of tests, Functionize uses ML (machine learning) models to update tests with every changing application, which saves substantial time and resources. Saving time allows for more effort spent toward sustaining scripts for minor style changes or UI (user interface). Even better, the software is easy to use, fast, and efficient. It’s also capable of running thousands of tests in a minute using either desktop or mobile devices.
Mabl: Mabl is an offshoot of efforts by previous Google employees geared up to help users ensure proper integration through the entire development lifecycle, ensuring continuous delivery to yield various benefits.
Mabl captures the interactions and intent of users and, from these, generates an automated test script. Whenever there are changes to a web app during the development process, Mabl’s AI-driven test automation framework provides automatic updates, which help ease the burden of test maintenance. The tool also relays crucial information when it diagnoses performance and visual regressions over time.
Mabl also features a built-in link crawler, which helps locate and identify JavaScript errors and broken links. Its crawler also considers test coverage requirements based on existing tests, depth, and complexity of the page. Mabl also has a cutting-edge AI technology, making it easier to scale continuous testing.
Eggplant AI: Eggplant makes use of various concepts, including model-driven software development for software testing. With the application of the SUT model, it creates test cases. Among its specific actions during the testing process are:
- Bug detection. The software determines the presence of bugs by looking over common patterns.
- Coverage evaluation. The software conducts coverage analysis in terms of actions, states, and data.
Sealights: Sealights also features a web-based AI that is helpful for quality assurance personnel and developers. It boasts machine-learning technology that assesses the tests and codes executed against the codes. In this case, testers don’t have to conduct repetitive testing. With Sealights, users identify a test’s actual coverage. It implements various kinds of criteria, including functional, performance, manual testing, unit test, and others.
Testim: Testim allows users to develop a reliable end-to-end test that can be either codeless or coded. Or it can be both. It can dramatically reduce the maintenance of tests and make them stable with the use of Smart Locators. By implementing cross-browser and parallel tests on Testim and other third-party grids, it hastens testing.
Moreover, Testim makes it possible for testers to stay in their workflow to develop tests, collaborate, trigger tests on CI build, and branch tests. It can also aggregate error types, therefore, showing before/after screenshots and providing essential data. Upon discovery, the tool documents the bug and shares it with a bug tracking tool.
How artificial intelligence changes software testing
In many cases, developers don’t have sufficient time and resources to conduct exhaustive testing on software applications prior to deployment due to the expense or planning horizon dictated by the competition (ie. to quickly enter a market to maintain parity with competitors) or other external factors requiring fast deployment. Artificial intelligence changes the game by meeting these goals. AI helps by identifying components in software requiring attention quickly and efficiently.
Unlike with traditional approaches, the use of AI helps reduce the needed volume of human intervention because AI identifies issues and faults, thereby making the human testing process’ pace faster.
AI also made the software testing process simple. With the use of sophisticated algorithms, AI-based tools assess a huge volume of data efficiently and quickly. They also ensure the accuracy of the testing program.
Furthermore, AI relays comprehensive information to the testers relative to the areas requiring improvement. That means testing no longer involves as much guesswork, and through the reports output from the AI, they get a clear idea of the changes that need implementation.
Here are other ways artificial intelligence changes software testing:
- AI lessens bugs. Elimination of bugs via manual testing is difficult. But removing bugs is necessary since they negatively affect the user experience, customer satisfaction, and purchase intentions.
In a lot of cases, bugs infiltrate the program undetected. By using AI, software companies determine how the bugs entered the program, when they entered, and their corresponding origins to improve future software development efforts. Likewise, AI assesses the presence of bugs and diagnose errors that they tap into to improve the code.
Proponents also know whether they need to use coding to make the software immune to bugs based on the reports provided by AI. Through that, users can remove bugs during the development process of the software.
- AI automated the software testing process. Because of time constraints, some developers just drive software applications to the market without thorough testing at different levels. As a result, the subsequent software comes with various mistakes and issues. Some companies have to deal with these aggressively, but small budgets prevent them from hiring experts needed to ensure quality.
Again, that is where AI-enabled software testing comes into play. Software testing is a mostly repetitive process where every program is assessed at each stage in development and across different applications. A manual process used in software testing takes substantial time. With AI, algorithms determine software functions and identify faults automatically. In that way, testers don’t have to test the entire process repetitively while still resulting in accurate results
- AI boosts testing capabilities. With its machine learning feature, AI helps create test data allowing companies to integrate data into an AI machine, which makes it possible to function correctly in different stages without the need for manual testing. As a result, the reports are more reliable and secure. This applies to different types of test cases in software testing.
AUTHOR BIO
Patrick Panuncillon is an online marketing expert who has been helping eCommerce sellers boost their online visibility and increase conversions and sales. His expertise also expands to the latest trends, including AI software testing.
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