In the beginning, when the Waterfall project methodology was still widely implemented, testing was deployed as an end-of-project solution to evaluate the software just before it hit the market.
Since then, project methodologies have advanced, incorporating the ever-popular Agile, DevOps, and others, requiring more creative and flexible QA approaches.
One of the latest proposed solutions is to employ the innovative technologies made possible with artificial intelligence (AI) and machine learning (ML); however, to date, many companies remain unsure as to the effectiveness of the outcomes.
Below, we will examine automated and AI/ML-based software testing types to see where AI fits into the testing tech landscape and if there are any challenges associated with its adoption by QA engineers.
Software testing is an essential part of software development that is vital to ensuring the quality of a product to be released. QA specialists verify the solution in question to solidify its reputation, create a positive customer experience, and, as a result, increase revenue.
This is a considerable challenge for many businesses, QA consultants from a1qa point out. To manage testing workflows successfully, companies need to onboard highly skilled professionals and often engage the services of specialized partners who come equipped to tackle the most complicated issues and determine the best solutions for each particular project.
More challenges come from the essence and goals of automated testing, which is where AI testing tools are increasingly finding their use. For teams setting up automates cases, there are the following pitfalls to watch out for:
– It’s time-consuming. For automated tests to run properly with code expansion, QA engineers have to keep tests adjusted, which becomes more complicated over time due to stockpiling and software debt.
– Missing bugs when adding new functionality. Automated tests can run successfully; however, they may also fail to detect code errors in newly added features. Often this requires manual testing, which isn’t always possible unless the project is staffed accordingly.
– Long-term test cases. When instigating long-term test cases, errors may occur in setting up test -case steps, not in the code. In this matter, QA engineers will have to improve a test and start it over.
– Testing engineers’ skills. Test automation requires particular knowledge and skills in creating automated test scripts, meaning that searching for the right specialists can be a time-consuming process.
– It’s hard to automate user interface (UI) tests as it requires human interference and judgment.
Though not a panacea, AI and ML technologies are poised to alleviate much of the automated testing burdens. At the same time, these nascent tools may bring along their challenges, which are described below.
Currently, AI and ML testing methods are going through a stage of intense development, and while many remain hopeful for their advancement, others fear that the technology is still raw and thus inevitably taxing for early adopters.
Before diving into the benefits and challenges of AI and ML, let’s have a look at what they are actually about.
Essentially, AI and ML tech are trained to process data, identify schemes and patterns, create and evaluate tests without human assistance. This is made possible with deep learning and neural networks — when a machine self-educates based on the provided data sets or data extracted from an external source, i.e. the web.
There are two main approaches for AI and ML implementation in testing:
– To train the AI and ML to create automated tests. Some attempts have been made in this field with varied success.
– To teach AI to organize tests, deciding autonomously on what to run, what to fix, and what to discard.
It’s expected that in the near future AI and ML will enhance testing in many ways. There are already a number of use cases, some of which include:
– Detecting any changes in software and defining whether it’s a bug or an added feature that should be tested.
– Updating tests accordingly to the features being added.
– Fixing tests on the run in case a certain element is not found.
– Quickly detecting software changes by inspecting history logs and correlating them with the test results.
– Analyzing code to estimate test coverage.
– Creating dashboards to unite and share data on tested code, current testing statuses, and test coverage.
– Prioritizing test cases.
– Speeding up maintenance and test runs.
– Predicting and timely notifying about possible code or test issues.
Although the expectations around AI and ML are ambitious and show potential, there are some hidden pitfalls.
First, businesses eager to deploy AI and ML tech may not fully realize how to use them in daily operations and may lack the data required for AI training or input raw unsorted data, leading to unexpected results.
Second, they need to estimate how efficiently AI and ML work in comparison to traditional approaches if there is a substantial benefit, or, conversely, a combination of both approaches is required.
Third, adoption of AI and ML for testing requires changes to working culture and workflows.
For these reasons, QA teams should exercise caution and gather knowledge to ensure that AI or ML suit their needs. For example, by running a pilot study to experiment on how technology could be deployed in real-life business situations.
Software testing approaches evolve day by day to catch up with the pace of application development techniques, becoming more complex and sophisticated with each step. As a result, fast-growing businesses often need to run ongoing, adjustable tests for their systems and products.
For companies seeking to implement AI and ML testing solutions, we advise an educated approach where research is done to ensure that the software products are appropriate to the business needs.
Credit: BecomingHuman By: Maxim Chernyak
