Using Machine Learning in Software Development
When it comes to assessing the impact artificial intelligence (AI) is likely to have on businesses, there’s no shortage of articles and expert predictions on the Internet. Even though the technology is still in its infancy, scientists already attribute a 4-6% growth in the global economy solely to AI and its subsets, such as machine learning (ML).
Technologists at heart, the innovation analysts at Symfa – custom software engineering company specializing in end-to-end digital transformation, have always been curious about the use of machine learning in software development.
In this article, we’ll explore the matter a little further to discover how forward-thinking developers utilize AI beyond having parts of their code written or reviewed by ChatGPT.
Top 5 Use Cases for Machine Learning in Software Development
Machine learning, a branch of artificial intelligence that involves teaching algorithms to process and act upon data without being explicitly programmed to do so, has been around for decades.
But it was not until recently that we saw a tremendous increase in its usage among software engineers. Studies indicate that up to 47% of all software development tasks can be automated with AI while 41% of new code on GitHub is already being produced by algorithms.
No, ML won’t replace programmers any time soon — simply because machine learning models cannot think like humans, which limits their ability to understand complex processes and systems and come up with non-trivial solutions. What will happen is the uptake in ML usage for routine tasks, such as generating boilerplate code or fixing minor errors during debugging. This, and the emergence of more advanced software products, of course.
Here’s a rundown of promising ML use cases every software engineer should be aware of:
- ML helps programmers complete and optimize code. ML-based tools like Tabnine, OpenAI Codex, CodeT5, and ChatGPT assist software engineers with writing superior-quality code in several ways. They can provide you with contextual suggestions and identify areas for improvement. ML can also translate natural language into code and vice versa. Additionally, these tools can autocomplete code lines, allowing you to focus on higher-value tasks, and support integration with commonly used code editors, including Sublime Text and Visual Studio Code.
- ML enhances and automates quality assurance (QA) activities. The use of machine learning in software development does not eliminate the need to collaborate with QA engineers in a project. However, ML can help the latter create better QA documentation, including test cases and automated test scripts, subsequently reducing the number of errors in production code. To that end, several tools can be used. [aqua cloud] is capable of generating test cases based on software requirements written in plain English. Applitools offers a resilient infrastructure for running tests across multiple browsers and devices in the cloud. And Testim leverages AI algorithms for effective API and user interface (UI) testing.
- ML simplifies application performance monitoring (APM). APM practices play a crucial role in maintaining software products in perfect shape and boosting user experience. In traditional application performance monitoring, development, operations, DevOps, and site reliability engineering (SRE) teams analyze various software performance indicators, such as response time, database queries, and CPU and memory usage. Effort-intensive as they are, these APM processes may be further hindered by the use of cumbersome tools and limited cooperation between project teams. By utilizing ML-powered APM solutions like Dynatrace and New Relic, IT teams can tap into spot-on anomaly detection, perform advanced root cause analyses, and timely address application performance issues before any significant damage is done.
- ML personalizes user experience. In the era of digital-first, channel-hopping customers and shortening attention spans, software products — from B2C mobile apps to enterprise data ecosystems — should be created with the end user in mind. Machine learning tools enable software engineers to create context-aware apps, adaptive interfaces, and advanced recommendation engines that help users make better-informed decisions and find the content they’re after. Some examples of such tools include libraries like TensorFlow Recommenders and pre-configured cloud services, such as Amazon Personalize and Azure Machine Learning.
- ML powers a new wave of software products. Before the widespread machine learning adoption, software engineers were largely limited in their choice of data analytics tools. Today, we can finally move past “if-then” programming and descriptive analytics. Machine learning paves the way for more advanced data processing, helping engineers analyze both structured and unstructured information, spot correlations between events, and configure algorithms to deliver accurate, contextual recommendations. The primary drivers of this ML revolution are robust cloud services, such as Amazon SageMaker, and open-source machine learning libraries and frameworks, including TensorFlow and PyTorch. These technologies are used in next-gen data ecosystems and self-service business intelligence (BI) solutions that provide a 360-degree view into business processes and democratize access to data for end users, regardless of their tech skills.
Should You Start Using Machine Learning in Software Development?
A short answer is yes, by all means.
But one does not adopt ML on a whim.
Before embracing machine learning in software projects, you should:
- Educate yourself on the basic ML concepts and techniques
- Assess the availability, capacity, and ease of use of the machine learning tools that could help you solve your development goals
- Find a mentor or join a machine learning community to get expert, hands-on tips for solving the challenges you’ll inevitably face in your projects
- Keep your finger on the pulse of ML advancements to continuously expand your knowledge and technology stack
By following these steps, you’ll be able to join the lucky cohort of software engineers who will never be replaced by AI and will instead leverage smart algorithms to excel at their job.
