Digital Themes

AI-Augmented Development

AI-augmented development is making headlines. According to Gartner®, a company that delivers actionable, objective insights to executives and their teams, “By 2027, 70% of professional developers will use AI-powered coding tools, up from less than 10% today.”  

In plain terms, AI-augmented software development utilizes machine learning and artificial intelligence (ML/AI) tools to accelerate the software development life cycle. AI tools also enhance accuracy, code generation, code reliability, and error elimination.

Traditional root cause analysis (RCA) can be time-consuming and inefficient. AI-assisted RCA automates the software development process and prevent human error. By analyzing data patterns in existing code, performing code reviews, and automating code, the AI tool – acting as a code assistant – is both diagnostic and therapeutic. 

Organizations must be careful when calling upon the help of AI-augmented tooling. Data privacy laws and company-specific regulations prevent the sharing of sensitive data with large language models (LLMs) like ChatGPT and Google Bard, which utilize natural language processing (NLP) and public data input. When interacting with AI-powered coding tools, some developers might not be aware that customers’ personally identifiable information (PII) is present within the code and suddenly available to external parties.

Used intelligently and carefully, however, artificial intelligence can accelerate and improve the software development life cycle (SDLC) and the generation of powerful code, enabling developers to focus on higher-level tasks. Present examples of AI-augmented software engineering tools include Google Codey, Amazon CodeWhisperer, GitHub Copilot, and OpenAI’s ChatGPT.

The benefits of AI-augmented development tools are many. They include, but are not limited to, the following:

  • Code generation
  • Language translation
  • Greater accuracy and efficiency within the software development life cycle (SDLC)
  • Enhancement of knowledge sharing, provided that workers have a positive evaluation of AI and expect AI tools to help them, per a 2022 Mills & NY study
  • Increased ability to meet software development demands without increasing the frequency of human error
  • Greater involvement from citizen software developers who do not have a software development background
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