The Impact of Large Language Models on ModernSoftware Development An Evidence-Based Analysis of Code Generation,Productivity, and Security<i></i>

Abstract

Large Language Models (LLMs) have emerged as
transformative tools in modern software development, funda-
mentally changing how developers design, implement, test, and
maintain software systems. Advanced AI systems such as code-
generation assistants and intelligent development agents are
increasingly integrated into software engineering workflows, en-
abling significant improvements in productivity, code quality, and
development efficiency. This article examines the impact of LLMs
on contemporary software engineering practices, focusing on
automated code generation, debugging support, software docu-
mentation, code review processes, and collaborative development
environments. To ground the discussion empirically, we conduct
a structured synthesis of published experimental evidence, aggre-
gating functional-correctness results on the HumanEval bench-
mark across eight representative models, controlled productivity
experiments with AI pair programmers, and security-focused
user studies. The synthesized evidence shows that state-of-the-
art models solve up to 67% of HumanEval problems on the first
attempt, that developers assisted by LLM-based tools completed
a standardized task approximately 55.8% faster in a controlled
experiment, and that roughly 40% of code suggestions produced
in security-sensitive scenarios contained exploitable weaknesses.
The study analyzes both the opportunities and challenges associ-
ated with adopting LLM-based tools, including concerns related
to code reliability, security vulnerabilities, intellectual property,
and developer overreliance. The findings suggest that while LLMs
are unlikely to replace software engineers, they are becoming
indispensable tools that augment hu