Endnote: AI-Assisted Software Engineering
summary
Claims that AI excels at analysis tasks and struggles with creative code generation, demonstrated by a prototype CY diagnostic tool on GitHub that identifies compiler issues reliably but fixes them less often. Workflow uses GPT models to classify diagnostics, extract code entities, and propose JSON patches; reproducibility and strict machine-readable output remain brittle across model versions. A worked example builds a parallel std::unique using stenciled copy_if and an adjacent view/zip iterator approach to achieve one parallel pass with no O(n) temporaries, after many prompt-driven iterations.
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chapters (11)
- 0:00AI-assisted software engineering intro
- 1:05AI fundamentals and limits
- 4:17Software tasks and AI strengths
- 6:04Diagnosing compilation errors
- 12:31AI workflow for error diagnosis
- 23:07AI challenges in parallel code
- 30:34Iterative AI code solutions
- 41:08Refining AI parallel code
- 57:19Effective AI interaction techniques
- 1:02:58AI future in software workflows
- 1:04:44Q&A
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