What You See Is What It Does: A Structural Pattern for Legible Software

• URL: http://arxiv.org/abs/2508.14511v2
• Date: Wed, 27 Aug 2025 21:45:23 GMT
• Title: What You See Is What It Does: A Structural Pattern for Legible Software
• Authors: Eagon Meng, Daniel Jackson,
• Abstract summary: Software today is often "illegible" - lacking a direct correspondence between code and observed behavior.<n>A new structural pattern offers improved legibility and modularity.<n>A domain-specific language for synchronizations allows behavioral features to be expressed in a granular and declarative way.
• Score: 0.29434930072968585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The opportunities offered by LLM coders (and their current limitations) demand a reevaluation of how software is structured. Software today is often "illegible" - lacking a direct correspondence between code and observed behavior - and insufficiently modular, leading to a failure of three key requirements of robust coding: incrementality (the ability to deliver small increments by making localized changes), integrity (avoiding breaking prior increments) and transparency (making clear what has changed at build time, and what actions have happened at runtime). A new structural pattern offers improved legibility and modularity. Its elements are concepts and synchronizations: fully independent services and event-based rules that mediate between them. A domain-specific language for synchronizations allows behavioral features to be expressed in a granular and declarative way (and thus readily generated by an LLM). A case study of the RealWorld benchmark is used to illustrate and evaluate the approach.

Related papers

• Modular Layout Synthesis (MLS): Front-end Code via Structure Normalization and Constrained Generation [18.154715745625328]
  Automated front-end engineering drastically reduces development cycles and minimizes manual coding overhead.<n>Current solutions often produce monolithic scripts, failing to support modern ecosystems like React, Vue, or Angular.<n>We introduce Modular Layout Synthesis (MLS), a hierarchical framework that merges visual understanding with structural normalization.<n>MLS significantly outperforms existing baselines, ensuring superior code reusability and structural integrity across multiple frameworks.
  arXiv  Detail & Related papers  (2025-12-22T03:24:11Z)
• Consistency-Aware Editing for Entity-level Unlearning in Language Models [53.522931419965424]
  We introduce a novel consistency-aware editing (CAE) framework for entity-level unlearning.<n>CAE aggregates a diverse set of prompts related to a target entity, including its attributes, relations, and adversarial paraphrases.<n>It then jointly learns a low-rank update guided by a consistency regularizer that aligns the editing directions across prompts.
  arXiv  Detail & Related papers  (2025-12-19T15:18:07Z)
• On Code-Induced Reasoning in LLMs [21.875805779552564]
  We construct parallel instruction datasets in ten programming languages.<n>We apply controlled perturbations that selectively disrupt structural or semantic properties of code.<n>Across 3,331 experiments, our results show that LLMs are more vulnerable to structural perturbations than semantic ones.
  arXiv  Detail & Related papers  (2025-09-25T19:57:36Z)
• Data Dependency-Aware Code Generation from Enhanced UML Sequence Diagrams [54.528185120850274]
  We propose a novel step-by-step code generation framework named API2Dep.<n>First, we introduce an enhanced Unified Modeling Language (UML) API diagram tailored for service-oriented architectures.<n>Second, recognizing the critical role of data flow, we introduce a dedicated data dependency inference task.
  arXiv  Detail & Related papers  (2025-08-05T12:28:23Z)
• Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification [10.964060011243234]
  This paper presents a study of using large language models (LLMs) in modifying existing code.<n>"prompting" serves as the primary interface for developers to communicate intents to LLMs.<n>This study investigates two prompting strategies for LLM-assisted code modification.
  arXiv  Detail & Related papers  (2025-08-02T23:52:49Z)
• CORE: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks [12.465309397733249]
  Large language models (LLMs) have been widely adopted across diverse software engineering domains.<n>These applications require understanding beyond surface-level code patterns.<n>Existing benchmarks primarily evaluate end-to-end outcomes, such as whether code is correctly repaired or generated.
  arXiv  Detail & Related papers  (2025-07-03T01:35:58Z)
• Training Language Models to Generate Quality Code with Program Analysis Feedback [66.0854002147103]
  Code generation with large language models (LLMs) is increasingly adopted in production but fails to ensure code quality.<n>We propose REAL, a reinforcement learning framework that incentivizes LLMs to generate production-quality code.
  arXiv  Detail & Related papers  (2025-05-28T17:57:47Z)
• CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation [10.438717413104062]
  We introduce CodeIF-Bench, a benchmark for evaluating the instruction-following capabilities of Large Language Models.<n>CodeIF-Bench incorporates nine types of verifiable instructions aligned with the real-world software development requirements.<n>In both textitStatic Conversation and textitDynamic Conversation settings, we evaluate the performance of 7 state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-03-05T09:47:02Z)
• CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation [20.013757490442064]
  We introduce CodeIF, the first benchmark designed to assess the abilities of Large Language Models (LLMs) to adhere to task-oriented instructions.<n>CodeIF encompasses a broad range of tasks, including function synthesis, algorithmic instructions, and code explanation.<n>We conduct extensive experiments with LLMs, analyzing their strengths and limitations in meeting the demands of these tasks.
  arXiv  Detail & Related papers  (2025-02-26T14:19:49Z)
• Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs [57.27982780697922]
  Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequential nature of their decoding process. This paper introduces Lexical Unit Decoding, a novel decoding methodology implemented in a data-driven manner.
  arXiv  Detail & Related papers  (2024-05-24T04:35:13Z)
• Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective [85.48043537327258]
  We propose MANGO (comMents As Natural loGic pivOts), including a comment contrastive training strategy and a corresponding logical comment decoding strategy. Results indicate that MANGO significantly improves the code pass rate based on the strong baselines. The robustness of the logical comment decoding strategy is notably higher than the Chain-of-thoughts prompting.
  arXiv  Detail & Related papers  (2024-04-11T08:30:46Z)
• Contrastive Instruction Tuning [61.97704869248903]
  We propose Contrastive Instruction Tuning to maximize the similarity between semantically equivalent instruction-instance pairs. Experiments on the PromptBench benchmark show that CoIN consistently improves LLMs' robustness to unseen instructions with variations across character, word, sentence, and semantic levels by an average of +2.5% in accuracy.
  arXiv  Detail & Related papers  (2024-02-17T00:09:32Z)
• Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation [22.219645213202178]
  This paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT. We show that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.
  arXiv  Detail & Related papers  (2023-10-16T05:09:58Z)
• Contrastive Learning for Source Code with Structural and Functional Properties [66.10710134948478]
  We present BOOST, a novel self-supervised model to focus pre-training based on the characteristics of source code. We employ automated, structure-guided code transformation algorithms that generate functionally equivalent code that looks drastically different from the original one. We train our model in a way that brings the functionally equivalent code closer and distinct code further through a contrastive learning objective.
  arXiv  Detail & Related papers  (2021-10-08T02:56:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.