cs.SE · 2026-06-06 · No. 15

Software Engineering, 2026-06-06.

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

01 — The papers

4 entries
  1. 01

    Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution

    Liliana Hotsko, Yinxi Li, Yuntian Deng, Pengyu Nie

    cs.SE · cs.AI · cs.CL

    Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly at repository scale and brittle to evolving codebases. We introduce Code2LoRA, a hypernetwork framework that generates repository-specific LoRA adapters, effectively injecting repository...

    arxiv.org/abs/2606.06492 · PDF

  2. 02

    Towards the Readability of LLM-Generated Codes through Multitask Representation Engineering

    Huifan Gao, Liuhua He, Yinghui Pan, Shenbao Yu, Yifeng Zeng, Shengchao Qin, Weidi Sun

    cs.SE · cs.AI

    Correctness and readability are key measures of code quality, respectively ensuring functional fidelity and ease of comprehension. While most existing research focuses on improving the correctness of large language models~(LLMs) generated codes, readability remains under-addressed. Enhancing readability through targeted control is challenging due to its subjective nature. In this article, we employ representation engineering~(RepE) as the...

    arxiv.org/abs/2606.06214 · PDF

  3. 03

    TLA-Prover: Verifiable TLA+ Specification Synthesis via Preference-Optimized Low-Rank Adaptation

    Eric Spencer, Arslan Bisharat, Brian Ortiz, Khushboo Bhadauria, TaiNing Wang, George K. Thiruvathukal, Konstantin...

    cs.SE · cs.AI · cs.LG · cs.LO

    TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produce TLA+ specifications that fail the TLC model checker for semantic reasons. Across 25 LLMs, the best public baseline is 26.6% syntactic parse and 8.6% semantic model-check. We present TLA-Prover, a 20-billion-parameter model for TLA+ specification synthesis. Training combines supervised...

    arxiv.org/abs/2606.06133 · PDF

  4. 04

    Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning

    Helge Spieker, Jørn Eirik Betten, Arnaud Gotlieb

    cs.SE · cs.AI · cs.LG

    Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations. This is called the Rashomon effect of (explainable) machine learning, and it raises the question of which explanations, if any, are trustworthy. We propose a framework based on metamorphic testing that assesses explanation faithfulness without requiring ground-truth labels by exploring...

    arxiv.org/abs/2606.06056 · PDF

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