cs.SE · 2026-08-10 · No. 80

Software Engineering, 2026-08-10.

5 new papers in cs.SE. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

5 entries
  1. 01

    Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools

    Afreen Alam, Evgenija Popchanovska, Ana Gjorgjevikj, Maryan Rizinski, Lubomir T. Chitkushev, Irena Vodenska, Dimitar Trajanov

    cs.SE · cs.AI · cs.CY

    Rapid adoption of large language models (LLMs) in enterprise settings has introduced operational, security, and governance risks. As generative AI applications move from pilot to production, manual harm identification and mitigation are becoming difficult to scale. Although many tools support model evaluation, adversarial testing, runtime guardrails, and observability, the tooling landscape remains fragmented. Tools are typically designed for...

    arxiv.org/abs/2608.07446 · PDF

  2. 02

    PACE: Primitive-Aware Code Evolution for Automated Algorithm Design

    Zhuoliang Xie, Ruihao Zheng, Xiang Xu, Genghui Li, Zhengkun Wang

    cs.SE · cs.AI

    Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs. While this whole-program perspective simplifies the search space, it fundamentally couples the useful local logic to its host program. Consequently, valuable code snippets vanish when the overall program is discarded, making it highly difficult to assess the contribution of individual algorithmic components.To address...

    arxiv.org/abs/2608.07395 · PDF

  3. 03

    Towards Assurance Closure in AI-Native Large-Scale Agile Software Development

    Ricardo Britto

    cs.SE · cs.AI

    The AI-Native Manifesto envisions large-scale agile software development in which humans increasingly govern intent, risk, and exceptions while agents execute more of the engineering process. Realizing that end-state requires more than better code generation: it requires assurance closure, meaning that the system can establish what must be true, determine and obtain appropriate evidence, judge the credibility of that evidence, preserve its...

    arxiv.org/abs/2608.07317 · PDF

  4. 04

    Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering

    Xiuwei Shang, Li Hu, Xiao Jiang, Jieke Shi, Junda He, Zhou Yang, Shaoyin Cheng, Guoqiang Chen, Weiming Zhang, David Lo

    cs.SE · cs.AI · cs.CR

    Human-Oriented Binary Reverse Engineering (HOBRE) aims to transform decompiled pseudocode into a more human-friendly representation, thereby reducing the cognitive burden of reverse analysis and improving efficiency. However, reliably evaluating HOBRE outputs remains a fundamental challenge: human evaluation is costly, time-consuming, and difficult to scale, while existing automated metrics either require executable test cases and runtime...

    arxiv.org/abs/2608.07038 · PDF

  5. 05

    Coupling Planning with Episodic Memory in LLM Agents for Software Issue Resolution

    Jiahao Zhang, Yifan Zhang, Yu Huang

    cs.SE · cs.AI

    Resolving a real software issue with a large language model (LLM) agent is a long repair episode, often tens to hundreds of steps spanning exploration, hypothesis, implementation, and verification. Success depends on both the base model's local reasoning and the agent's ability to maintain an evolving plan and remember observations across phases. Existing repository-level agents typically strengthen planning or memory in isolation, leaving...

    arxiv.org/abs/2608.06811 · PDF

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