software-engineering

ContextSniper is a token-efficient code memory layer for repository-level program repair using LLM agents. It reduces token usage by up to 51.5% and cost by up to 36.4% while maintaining similar resolution rates on SWE-bench Lite.

This paper introduces Hecate, the first tool that quantifies complexity across both prompt and code layers in LLM-integrated applications, using a Prompt-as-Specification formalism rooted in Hoare logic and evaluating 52 candidate metrics on open-source repositories to identify those that capture structural breadth beyond traditional code-only metrics.

This paper introduces Mastermind, a dual-loop framework that learns reusable vulnerability-reproduction strategies for repository-scale tasks, achieving an 84.5% pass rate with a frozen executor by separating strategy learning from execution.

Agent4cs is a multi-agent framework that summarizes large, hierarchical codebases in a bottom-up fashion using specialized agents for summarization, keyword extraction, and quality assurance, achieving up to 8% improvement in semantic consistency and 38% gain in keyword coverage over baselines.

Gergely Orosz argues that experienced software engineers remain valuable because they can discern good code from bad, something AI code generators still cannot reliably do.

This project adapts the engineering principles of Clean Code to JavaScript, providing bad/good comparisons for each principle to help developers write readable, reusable, and refactorable code, solving the pain of code rot in team collaboration.

A community discussion on lobste.rs asks developers for tools and techniques that effectively convey and grow mental models, contrasting them with exhaustive documentation.

Kent Beck argues that software engineering is not going away even as AI advances, because coding involves building trust, understanding, and connections that cannot be automated.

The article discusses common misconceptions about the true purpose of code review in software development, emphasizing it's more about knowledge sharing and team collaboration than just finding bugs.

The author emphasizes that the key to making AI agents write good code lies in following classic software engineering practices from 1990s textbooks: writing tests, doing CI/CD properly, top-down design, and modular decoupling.

A discussion on where to place guardrails to prevent AI coding agents from making unauthorized changes, exploring friction points at various stages of the deployment workflow.

Senior SWE-Bench is an open-source benchmark that evaluates AI agents on software engineering tasks requiring senior-level skills.

Criticizes the Vibe Coding style, pointing out that the imprecision of natural language requirements causes AI Agent outputs to require extensive rework, and advocates for spec-driven development to ensure safety and production quality.

Senior SWE-Bench is a new open-source benchmark designed to evaluate AI agents on realistic, underspecified software engineering tasks, emphasizing skills like intent alignment and code quality rather than overly detailed specifications.

A podcast interview with Kent Beck covers his career from early programming to Agile and TDD, emphasizing the importance of human skills in software engineering.

SWE-rebench leaderboard updated with new models (GLM-5.2, Qwen3.6, Gemma 4 31B, etc.) and an improved UI, showing performance rankings on software engineering tasks.

Explores the debate over whether AI will eventually replace human software developers, examining current capabilities and future trends.

An article discussing how software engineering best practices are evolving with the integration of LLM-based coding tools, offering guidance for developers.

Article comparing old C++ performance tricks with modern compiler capabilities, showing that compilers can now optimize naive code better than hand-tuned hacks. Includes benchmarks on AMD Zen 5 with Clang 21.

The author argues that ORMs are more detrimental than beneficial and that developers should focus on learning SQL directly to avoid issues like attribute creep and inefficient queries.