TL;DR
It is shown how island grammars can be used to generate robust parsers that combine the accuracy of syntactical analysis analysis with the speed, flexibility and tolerance usually only found in lexical analysis.
The growing adoption of Large Language Models (LLMs) in Software Engineering has reinforced the expectation that coding activities can be largely automated. However, this perception may represent yet another historical search for a solution capable of eliminating the inherent challenges of software development. This article discusses the transition from a code-centered paradigm to Specification-Driven Development. We argue that artificial intelligence reduces some of the effort associated with writing source code, but it does not eliminate the complexity of developing professional software systems. Instead, it shifts this complexity toward domain understanding, requirements elicitation, specification development, validation, maintenance, and software evolution. Building on this perspective, we discuss the renewed centrality of Requirements Engineering, considering its implications for productivity and software quality, as well as risks associated with automation bias, ambiguity propagation, Specification Overfitting, and the accumulation of Specification Debt. Finally, we propose the Specification Paradox: the more capable artificial intelligence systems become at automatically generating software, the greater the dependence on correct, complete, verifiable, and explainable human-produced specifications. We conclude that the future of Software Engineering will depend not only on machines'ability to generate code, but also on humans'ability to correctly specify, evaluate, and evolve what is intended to be built.
Complex source code analysis tasks for program understanding, verification, and compliance with regulatory frameworks and standards require appropriate in-memory models of the source code being analyzed. We present a flexible, multi-language pipeline supporting such analyses. By leveraging configurable front-end parsers and corresponding EBNF grammars, the pipeline automatically identifies the source language, provisions the appropriate parsers and AST factories, and dynamically generates in-memory Abstract Syntax Trees (ASTs) tailored for source code analysis rather than traditional compilation. Individual ASTs generated per source file are linked into a unified structure representing the entire application, with semantic linking that resolves references to functions and variables while respecting scoping. We demonstrate the approach on compliance analyses that trace sensitive data across Java and C++ modules and detect violations of NIST 800-53 controls, and show that it is tractable with respect to parsing time and the size of the ASTs generated.
This systematic review presents an analysis of the "Vibe Coding" methodology — a contemporary approach to the iterative software development process using Large Language Models (LLMs). Code generation tools are transforming software development by enabling programmers to formulate tasks and describe the desired behavior of software in natural language, while LLMs generate source code corresponding to these requests. The review systematizes current methodologies for the use of LLMs, highlights application examples, evaluates the effectiveness of generated code, discusses emerging challenges, and outlines future development trends of the technology. The aim of this work is to provide a comprehensive understanding of the capabilities and limitations of Vibe Coding as a transformational methodology in software engineering.