software development

How AI Generated Code Compounds Technical Debt

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AI-generated code is rapidly increasing technical debt due to rising code duplication and declining quality. GitClear's research reveals an 8-fold rise in duplicated code and a decrease in code reuse, leading to redundant systems. Despite perceived productivity gains, developers are spending more time debugging and addressing security issues. Long-term, this unchecked AI code generation risks overwhelming maintainability, increasing operational costs, and complicating testing due to duplicated code. Emphasis on long-term sustainability is crucial to prevent indefinite maintenance burdens as the trend persists.

https://leaddev.com/software-quality/how-ai-generated-code-accelerates-technical-debt

Practical Alloy

Practical Alloy is a guide to formal software design using the Alloy modeling language, focusing on developing models for software systems to explore design alternatives and validate requirements. It covers structural and behavioral modeling, with practical examples, making it suitable for both simple applications and complex distributed protocols.

https://practicalalloy.github.io/

Product Development Processes You Might Not Have Heard Of

Explore alternative product development methodologies beyond Scrum, Kanban, and Scrumban; these include:

  1. ShapeUp: A 6-week cycle focusing on shaping solutions before committing to builds through phases: Shaping, Betting Table, and Building.
  2. Plan > Build > Ship: A lighter waterfall approach emphasizing complete ownership of features from planning to shipping.
  3. Get Shit Done (GSD): Emphasized by Shopify, this method promotes rapid problem-solving through Think, Explore, and Build phases, supported by tracking tools.

These alternatives cater to different organizational needs and should be considered when structuring product processes.

https://www.departmentofproduct.com/blog/product-development-processes-you-might-not-have-heard-of/

Coding’s New Dawn: How AI Assistants Are Reshaping Software Development

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AI assistants like GitHub Copilot, Claude 3.5, GPT-4, and Google’s Gemini are transforming software development, enabling users to code quickly using natural language commands. These tools provide rapid solutions, reducing the learning curve for beginners while improving efficiency for seasoned developers. Despite outstanding performance in benchmarks, real-world application presents challenges, highlighting the need for human expertise in complex projects. The rise of AI coding tools is democratizing development, leading to innovative new products and inspiring investment, but also raising concerns about over-reliance on AI-generated code. Ultimately, a balance between AI capabilities and human insight is essential for future software creation.

https://cybernews.com/ai-news/ai-assistants-reshaping-software-development/

Software Development Topics I’ve Changed My Mind on After 10 Years in the Industry

After 10 years in software development, I've changed my views on several topics:

  • Simplicity requires effort.
  • Managing complexity lacks pride.
  • Typed languages benefit diverse teams.
  • Java's stability is valuable.
  • REPLs are for exploration, not design.
  • Code should follow thorough planning.
  • Frontend development is overly complex.
  • Elegance isn't a useful measure.
  • Effective management is crucial.
  • DynamoDB is good for specific workloads.
  • Object-oriented programming has its place.

New insights include the importance of communication, allowing junior developers to learn through mistakes, and the futility of ORMs. I still believe in the relevance of monoliths over microservices and caution against unnecessary scaling.

Looking forward to reevaluating these in another five years.

https://chriskiehl.com/article/thoughts-after-10-years

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