What and How AI adds to Software Testing?
- April 21, 2020
- Hiba Sulaiman
Software testers are equipped with highly analytical and creative problem-solving abilities. The job requires them to ask questions that others don’t and see what others can’t. Only then can they identify hidden defects and areas that might frustrate users.
But the analytical process is time-taking and it isn’t typically as efficient as today’s businesses and users demand. This is where Artificial Intelligence (AI) and its ability to search data sets for golden nuggets could be very useful.
AI tools have the ability to locate tests that have already been written to cover a new line of code or a particular scenario. The system could even highlight the most appropriate test cases for testers for the given requirements.
Over a period of time, AI tools might even pinpoint the root causes of the bugs that those tests find, based on the past data. AI can significantly increase the efficiency of testing and enhance the results when combined with testers’ knowledge about the product and its users. Here’s how.
How AI Could Help Testers
Let’s look at some of the key ways this technology could evolve to help QA organizations and testers.
- Identify high-risk areas in a sprint to ease prioritization for testers: This is essential when timelines are tight and there is no margin for error when it comes to decision making that could have a significant impact on the success of the release.
- When resolving an issue, identify which tests to run: This saves the time needed to fix the issue, thereby minimizing calls into the help desk and reducing the loss of revenue during a data loss or outage related to a security loophole.
- Isolate a bug faster and indicate the most probable causes: It’s crucial to note the exact line of code responsible for a glitch. That’s root-cause analysis at its best.
- Comb through databases: Test cases, resolution data, log data, and defects can identify areas of a product, allowing developers and testers to be proactive on quality.
- Perform real-time reporting on test coverage, issues, and defects: The team must continually be apprised of quality metrics.
Do More with Less
AI helps testers and developers to do more with less while making the work more fun at the same time. AI-powered tools can eliminate the repetitive and manual nature of the testing job.
AI doesn’t replace testers but instead, helps them get better at predicting where bugs exist so that those areas can be tested. These testers will create strategies for testing and leverage machine learning to create more tests stemming from the original requirements.
Apart from enabling wider test coverage, time can also be freed up by AI for the kind of manual exploratory testing that helps organizations understand a user’s feelings – both what frustrates them as well as what frustrates them.
Hire a Software Testing Company for a Future with AI
With all of the available data from AI, organizations will need to hire a software testing company that can create strategies to put this wealth of information to use. A testing company essentially provides information that helps organizations make the most informed decision possible about the readiness of release. In this sense, AI could become an invaluable tool, enabling organizations to deliver quality with each release.
AI-enhanced testing tools that are now hitting the market include various capabilities such as highlighting areas of risk that weren’t covered at all or need further testing. The market is expected to see a great influx of such and even more advanced tools in the coming months and years.
But before these tools are used by anyone, organizations will need to get all the test and development data linked to enable rapid search and analysis, much as Google indexes web pages. It will be crucial to sync data between repositories and test management systems like Jira and GitHub.
That sounds like a lot of hassle, so why do it? The user expects its products to be cleaner. AI can work as a bridge between this expectation and reality. The amalgamation of rapid analysis and the expertise of a trained tester can bring high-quality products to the market more consistently. This gives a boost to branding.
AI will also save organizations a lot of money. We’re aware of the damage defects can do. Especially those that make their way into production, increasing the cost of fixing significantly and causing irreparable damage. It’s not possible for testers to test every scenario which means that the ones they don’t cover could be critical.
Another benefit AI testing brings to the table is that it could help development teams understand the user’s likes and dislikes in a better way, with the accuracy generated from analyzing massive streams of usage data. This is something that expensive surveys and focus groups can never offer, but they can determine the success of an application.