Benefits and Challenges of AI in Software Testing: A Complete Guide

By 2024, there will likely be more than 28 million software developers working worldwide. Additionally, more than 2 million mobile apps were simply released in 2021. Software releases need to be carefully tested to guarantee that their functionality, performance, and security meet all necessary standards. However, adding more testers is not the most effective solution because testing takes a lot of time. Testing tools can incorporate artificial intelligence (AI) to improve their effectiveness and efficiency.

In this article, we will look at how AI can be used in software testing and the benefits and challenges of AI testing.

How can AI be used in software testing?

Smart test selection

AI can determine the necessary tests and remove the redundant ones that create noise because AI can:  

• Collect and analyze information automatically,
• Analyze the results of tests.

For example, Deep Exploit is an AI-based penetration testing tool that uses reinforcement learning which means as pentesters use it more, it becomes more accurate. Examples of this tool’s capabilities are  :  

• Intelligence gathering,
• Threat modeling,
• Exploitation,
• Reporting.

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PULSE is an AI-enabled automated API testing tool provided by Testifi. PULSE creates tests, detects errors and analyzes the results and returns them in a dashboard format by just analyzing the documentation of your API. Industry leaders such as Amazon and BMW use Testifi services.  

Machine learning

Machine learning (ML) can benefit software testing by automatically:

• Generating test cases,
• Auditing the current tests for coverage, performance, and completeness,
• Run tests.

An ML model first must be trained using the dataset of the organization. The dataset should include:  

• Codes,
• Logs,
• Test cases,
• Documents.  

After the training data is supplied to the ML model, its accuracy must be measured to ensure that it is working at an adequate level. ML models must be continuously updated using new data to improve its accuracy.  

Computer vision bots

Elements of a code such as shopping cart, login box, etc. can be labeled and computer vision bots can be used on the labeled elements of a code to detect changes and adopt the tests. The bots can be trained using the deep neural network method which enables them to recognize the elements and maintain the test even if the code changes. You can watch the following video to see computer vision bots in action:  

4 Benefits of AI in software testing

1- Faster test execution

AI can reduce the time that tests require to be executed by:

• Nullifying or removing duplicate tests,
• Improve test data management flow by ensuring high-quality data reach the test cases first.

2- Better test maintenance  

Software codes change a lot and when they do tests can break. They must be updated again to ensure compatibility with the new source code. This causes a significant increase in time and cost of test maintenance. However, AI can reduce the effort and time of test maintenance using computer vision bots and smart test selection.  

3- Increase test coverage

AI can examine the conducted exploratory tests to create new tests in order to improve the test coverage. It can be coupled with software testing metrics to identify the probability and severity of breaks in the different parts of the software.  

4- Test data generation  

Machine learning can be used to create test data similar to production data such as profile pictures, age, and weight. The test data can be used for:

• Training ML models for testing,
• Directly testing the software.

Challenges of AI in software testing

1- Data availability

AI models require a substantial amount of high-quality data before going into production otherwise developers have the risk of “garbage in, garbage out”.  

Recommendation: As mentioned before, ML models can generate data similar to a sample. They can be used for training AI models.  

2- Lack of generality  

Most optimization issues in search-based software testing need the AI algorithm to thoroughly look for a solution. They can provide adequate solutions for specific problems however they can’t be generalized which reduces their use cases.  

Recommendation: There is no direct solution to this problem. When the model is developed, developers should try to make the model as flexible as possible and identify methodologies to prevent generality loss.  

3- Computationally expensive  

Machine learning & deep learning are computationally expensive to implement. It is estimated that the required computational resources for developing a large AI model have roughly doubled every 3.5 months between 2012 to 2018. The high computation cost has made some AI models developed by Facebook and Google economically infeasible to run.  

Recommendation: Actions that can be taken to reduce the cost of computation are:

• Investing in the most advanced Graphical Processing Units (GPU) and Tensor Processing Units (TPU)
• Using GPU/CPU memory consumption estimation tools,
• Adopting a Hybrid Cloud infrastructure

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