The podcast discusses the impact and reality of generative AI in software development, including advancements in AI-assisted coding, the significance of writing sequel queries correctly, understanding AI-generated code, differences between AI-generated code and traditional programming frameworks, coding assistance and TDD ping pong style, frustrations with the misunderstanding of pair programming, and the usefulness of AI-assisted coding tools.
AI-assisted coding allows for a more unstructured and informal code generation process, aligning better with how humans think and write code.
AI-assisted coding tools offer inline assistance and auto-completion-like suggestions, but developers still need to understand and validate the generated code for correctness and security.
Deep dives
AI-assisted software development: Code generation and coding assistance
AI-assisted software development, particularly in the context of code generation and coding assistance, offers two main advantages. Firstly, it allows for a more unstructured and informal code generation process compared to traditional code generators, enabling developers to write code in natural language and have it translated into code with minimal effort. This approach aligns better with how humans think and write code. Secondly, AI-assisted coding does not aim to simply raise the abstraction level and eliminate the need for customizations. Instead, it provides assistance on all levels of code generation, from low-code and no-code applications to Java code and DSL code. This versatility allows developers to leverage large language models to create code at different abstraction levels, making it a useful tool for a wide range of software development needs. Overall, AI-assisted software development combines the power of large language models with human control and decision-making, providing developers with powerful code suggestions and productivity enhancements.
The potential and limitations of AI-assisted coding
AI-assisted coding tools, such as GitHub co-pilot, TabNine, Codeium, and Cursor, offer inline assistance and chat components within the IDE. These tools provide auto-completion-like suggestions, offering context-specific code snippets and even generating multiple lines of code at once. However, it is important to note that AI-assisted coding has certain limitations. While the tools can enhance productivity and provide high-quality suggestions, they do not guarantee the correctness or security of the generated code. Developers still need to understand and validate the generated code, ensuring correctness, performance, and other critical aspects. Additionally, the rapidly evolving nature of AI-assisted coding tools requires developers to stay updated and experiment responsibly, trying out different tools and approaches to understand their potential and determine the optimal ways to incorporate them into their development workflow.
AI-assisted coding and its impact on software development practices
AI-assisted coding has the potential to impact various software development practices, including test-driven development (TDD). While coding assistants can provide suggestions for writing code and offer alternative implementations, their effectiveness in the TDD process may vary. Some developers have experimented with deleting their full implementation and letting the coding assistant regenerate code based on their tests, while others have found success in using the assistants as a complement to their TDD workflow. Each developer should explore how AI-assisted coding fits within their own development practices and experiment with different approaches to find the most effective usage patterns.
Exploring the possibilities and embracing responsible experimentation
AI-assisted coding is a rapidly evolving field with new tools and features being introduced regularly. Developers are encouraged to embrace responsible experimentation and explore the possibilities that AI-assisted coding offers in their development workflows. Trying out different tools, understanding their strengths and limitations, and combining AI-assisted coding with other practices like low-code/no-code development can lead to productivity improvements and open up new avenues in software development. It is important to remember that AI-assisted coding tools, while powerful, are not a replacement for human expertise and critical thinking. Developers should use these tools to augment their skills, validate and understand the generated code, and consider the larger implications, such as maintaining code readability, extensibility, and adherence to testing practices. By regularly testing and adapting their approach, developers can unlock the full potential of AI-assisted coding in their software development journey.
Generative AI appears to be making an impact in a huge range of fields, but one that we're particularly interested in at Thoughtworks is its use in software development.
In recent months, there's been a lot of talk in the industry around issues like whether AI might boost developer productivity and if it can be used for pair programming, but in this episode of the Technology Podcast we try to get beneath the hype to explore the reality of generative AI and software development — how is it actually being used today? What works? And what doesn't?
To dive deeper into all this, Chief of AI Mike Mason and Global Lead for AI-Assisted Software Delivery Birgitta Böckeler join hosts Prem Chandrasekaran and Neal Ford, discussing everything from the current tooling to the way GenAI is shaping developer practices and workflows.
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