Software testing teams analyse and correct thousands of code on a daily basis to ensure the final product is free of errors. However, the on-demand customer expects software to be comprehensive in functionality and delivered with precision and speed. Current software testing procedures are not scalable to meet these needs, nor are they cost- or time-efficient in the digital economy.
As products become more complex to create, the code becomes more challenging to test accurately. Manual testing exposes development teams to many challenges—code changes causing errors elsewhere in the product, the considerable length of regression testing cycles, resourcing constraints of hiring skilled software testers to meet demand, and more.
While the current practices of agile and DevOps increase the pace of software development, meeting near-future market needs requires the power of predictive technologies to enhance traditional software testing solutions.
Artificial intelligence (AI) and machine learning (ML) provide a dynamic framework to predict and solve code writing errors before they appear. The more data patterns ML analyses, the more processes and self-adjustments can operate based on those learned patterns. This continuous delivery of insights increases in value with the “intelligence” of the technology. AI has enormous potential to reshape software development. When properly leveraged, AI solutions drive efficiency, optimise processes, and enhance experiences.
Let’s take a closer look at some of the key advantages of implementing AI/ML for testing software during the development process:
Automating and accelerating the testing process
Deploying AI/ML to the software testing process is not to replace human testers, but rather for technology to work in collaboration with humans to make the software development lifecycle more efficient and productive. Software companies utilise the skills of AI/ML experts to apply technology solutions that operate in conjunction with, and complement, traditional software testing processes and solutions already in place.
AI can automate and reduce the number of routine tasks in development and testing, beyond the limitations of traditional test automation tools. Software companies can train AI algorithms to instantly recognise, capture, and analyse large amounts of data sets to expedite the testing process because speed, cost, and efficiency are vital when it comes to testing software codes.
For example, a traditional software test tool analyses tests without discernment, analysing every possible test available. AI can significantly add value and efficiency by reviewing the current test status, recent code changes, and other code markers, deciding which tests to run, and executing them. This allows for scalable and efficient decision-making, freeing software engineers to spend time on more complex and strategic tasks.
Bugs naturally occur during software development, posing a major pain point for software testing teams. Software companies can use AI and ML algorithms across the company’s library to flag coding mistakes and discover bugs before developers include them in the code. This application of AI algorithms can help development teams save a significant amount of time and resources by not having to manually find and address bugs. Ultimately, AI can also help software companies to decide whether further coding changes are required to prevent program errors.
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