Automated AI Code Generation: How Does It Work? Here’s How To Handle Development Tools

AI code generation tools, combined with visual programming interfaces and domain-specific languages, can enable non-technical users to create applications without extensive coding knowledge.

By Karthikeyan G

Generative AI is a game changer in the engineering landscape, offering promising avenues in enhancing efficiency, improving automation, and reducing errors. In this realm, the Gen AI platform for engineers, Digital Ascender shines as a prime example. It leverages Gen AI to revolutionise the software development lifecycle. 

As we delve into the potential of these Gen AI tools, we uncover their value as an engineering companion, catering to the diverse needs of all stakeholders involved. Some of the most significant advantages that tools like this offer include:

 

• Rapid prototyping and ideation: AI code generation tools can quickly transform natural language descriptions or examples into working code snippets or prototypes, enabling faster ideation and experimentation. Developers can explore different approaches, iterate rapidly, and validate concepts before investing significant effort in implementation. Github Copilot is another code completion tool developed by Github – unlike other Gen AI, Copilot offers contextual understanding with relevant suggestions. 

• Requirements elicitation: By training on domain-specific data and vocabularies, AI models can better understand the context and terminology used within a particular industry or application domain which can assist in generating user stories from epics, which are high-level descriptions of a large feature or product requirement

 

• Increased developer productivity: AI-assisted code completion and suggestion tools can significantly increase developer productivity by reducing the time spent on routine coding tasks and providing intelligent auto-completion suggestions based on context. Google’s Duet is a conversational AI code assistance. Duet democratises knowledge access. As a G-suite tool, it also offers seamless integration within existing workflows. These tools can help developers write code faster, with fewer bugs, and maintain consistent coding styles and practices

• Automating mundane tasks: AI tools can automate repetitive and time-consuming tasks, such as code refactoring, code formatting, and documentation generation, freeing up developers to focus on more complex and creative aspects of the development process

 

• Enhancing code quality: AI-powered code analysis and review tools can identify potential issues, code smells, security vulnerabilities, and performance bottlenecks, enabling developers to proactively address them and improve code quality. Take Amazon’s Code Whisperer for instance. It is another machine-learning/AI-based tool for engineers. It utilises Large Language Models (LLMs) and offers code remediation, and tailored code suggestions to enhance code quality. 

 

• Code standardisation: These tools can also suggest best practices, coding standards, and refactoring opportunities based on industry-wide knowledge and experience. Bringing in such standards reduces the technical debt of the entire code base

 

• Accelerating Quality Engineering (QE): AI-based test case generation tools can automatically create a comprehensive list of test scenarios including the edge cases, from the user stories thereby increasing code coverage. These tools reduce testing effort by optimising test execution by prioritising right test scripts for the scenarios to be tested. 

 

• Enabling low-code/no-code development: AI code generation tools, combined with visual programming interfaces and domain-specific languages, can enable citizen developers or non-technical users to create applications without extensive coding knowledge, democratising software development.

AI tools contribute to code development through various means, including:

• Code Generation: AI tools facilitate the generation of code directly from technical user stories, streamlining the development process and translating requirements into executable code efficiently


• Code completion: These tools offer autocomplete functionality, assisting developers by suggesting code snippets, completing partial code, providing contextual suggestions, enhancing productivity, and reducing errors


• Finding bugs in the code: AI algorithms within these tools can analyse code to detect logical bugs, potential vulnerabilities, and errors, enabling developers to identify and rectify issues early in the development cycle, thereby improving software quality and reliability


• Optimising the code: AI-driven optimisation techniques can enhance code quality by optimising it for various aspects such as security, performance, and adherence to nonfunctional requirements, ensuring that the resulting software meets high standards of efficiency and reliability


• Inline code documentation: These tools automate the process of adding inline documentation to the codebase, improving code readability, and maintainability, and facilitating collaboration among developers by providing clear explanations and context within the code itself


• Unit test cases generation: Gen AI tools can automatically generate unit test cases based on code logic and specifications, increasing the code coverage and ensuring comprehensive testing, thereby enhancing the overall quality and reliability of the software


• Reverse engineering: AI-powered reverse engineering capabilities provided by tools enable the conversion of legacy code into technical documentation or modernised tech stacks, facilitating the migration, maintenance, and evolution of existing software systems in alignment with current best practices and technologies

The impact of these AI-powered tools on programmers and coders can be significant, but it's essential to understand that they are meant to augment and assist human developers rather than replace them entirely. Here are some potential impacts:

• Increased productivity by automating tasks, such as code generation, documentation, and test case creation.


• Enhanced code quality by assisting in identifying bugs, optimising code, and generating unit tests.


• Reduces dependency on legacy skills by generating documentation for the old programming languages and converting them to modernised frameworks. 


• Continuous learning is a must as AI technologies continue to evolve, developers will need to adapt them and constantly learn new skills to effectively leverage these tools and stay relevant in the industry.

While AI-powered tools offer significant advantages in terms of productivity, code quality, and consistency, it's crucial to recognise their limitations and potential drawbacks. Some of the key limitations are Biases, lack of domain knowledge, security and privacy, explainability on what was the reasoning behind the generated output and unpredictable cost. Developers and organisations should carefully evaluate the trade-offs, address potential risks, and employ a balanced approach that leverages AI capabilities while maintaining human oversight, domain expertise, and critical thinking skills.

(The author is the Senior Director, Platform Engineering, Ascendion)

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