Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.
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Build-bench is the first architecture-aware benchmark that evaluates LLMs on repairing cross-ISA build failures via iterative tool-augmented reasoning, with the best model reaching 63.19% success.
ClarifySTL uses LLM agents to interactively detect and resolve vagueness and ambiguity in natural language requirements via clarification queries before generating STL formulas, with evaluations on existing and new benchmarks showing effectiveness.
A dual-axis quality framework ranks DL mutation operators by statistical resistance and Jaccard-based realism to real faults, enabling up to 55.6% fewer mutants on held-out validation data without dropping baseline performance.
Ethics testing is introduced as a systematic approach to generate tests that identify software harms induced by unethical behavior in generative AI outputs.
LDMDroid applies LLMs in a state-aware process to trigger data manipulation functions and uses visual cues to detect errors, finding 17 bugs across 24 Android apps with 14 developer confirmations.
APIKG4Syn synthesizes API-oriented training data via knowledge graphs and Monte Carlo search to fine-tune a 7B model that reaches 25% pass@1 on HarmonyOS code generation, beating untuned GPT-4o at 17.59%.
This empirical baseline study characterizes generative AI usage across the software lifecycle in capstone projects, student-recommended responsible practices, and client expectations for understanding and quality.
Systematic literature review maps 13 privacy engineering dimensions into two recurrent cores mediated by modeling, with concentrations at specific lifecycle stages and domain variations.
A research proposal for three studies on multi-agent LLM pair programming that externalizes intent and uses automated validation to increase trustworthiness.