Teaching PHP new tricks with machine learning
summary
Uses the PHP tokenizer to extract comment tokens, converts counts of dollars, semicolons, operators and length into numeric features, trains a small neural net with the fann extension on 47 labeled examples to produce a tiny model file, then scores phpMyAdmin’s 14,000 comments in 68 ms. Initial pass flags ~14% (~2,000) as likely code; simple noise filters cut review set to ~800. Workflow is iterative: add reviewed true/false examples, retrain, and improve false positive rate.
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chapters (8)
- 0:00Introduction to the topic
- 2:38Supervised learning concepts
- 9:25Neural networks in PHP
- 14:11Data preparation and training
- 26:33Running and evaluating models
- 31:10Analyzing performance results
- 36:27Refinement and future tools
- 39:30Q&A and closing remarks
