Uddin, GiasSivapiran, Sanjeepan2026-07-242026-07-242026-04-022026-07-24https://hdl.handle.net/10315/43884Large Language Models (LLMs) generate functionally correct code that often lacks software standard qualities such as security, readability, and maintainability. This thesis investigates complementary approaches to bridge this gap. The first study examines training-time alignment using DPO and BoNBoN across five LLMs and their instruction-tuned variants. Results show non-functional alignment achieves consistent improvements (10.6\% average) while functional alignment proves unreliable (4.9\% average), with effectiveness varying by model family and pathway. The second study introduces POSec, an inference-time framework combining automatic prompt optimization with Selective Prompt Anchoring for secure code generation. Evaluated across 8 LLMs, 5 languages, and 3 optimizers, POSec achieves +20–33\% security improvements at Pass@10 without model retraining. Together, these studies demonstrate that improving LLM-generated code quality requires multi-level interventions, with training-time alignment offering breadth and inference-time optimization offering targeted depth.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceComputer engineeringElectronic Thesis or Dissertation2026-07-24LLM code generationNon-functional requirementsLLM alignmentAI4SESecurityPrompt optimization