cs.SE · 2026-06-17 · No. 26

Software Engineering, 2026-06-17.

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

01 — The papers

6 entries
  1. 01

    All Smoke, No Alarm: Oracle Signals in Agent-Authored Test Code

    Dipayan Banik, Kowshik Chowdhury, Shazibul Islam Shamim

    cs.SE · cs.AI

    Software practitioners increasingly use AI coding agents that generate test code alongside production code in open source pull requests (PRs). Recent studies report more than 932,000 agent-authored PRs across more than 116,000 repositories, yet whether their test files contain meaningful verification logic remains underexplored. Test files lacking explicit assertions execute code without verifying behavior, so quality gates based on test-file...

    arxiv.org/abs/2606.18168 · PDF

  2. 02

    A Framework for Evaluating Agentic Skills at Scale

    Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby

    cs.SE · cs.AI · cs.CL

    Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill. In this work, we present an evaluation framework that lets a skill author construct realistic tasks to rigorously assess the aspects of a...

    arxiv.org/abs/2606.17819 · PDF

  3. 03

    Position: Coding Benchmarks Are Misaligned with Agentic Software Engineering

    Maria I. Gorinova, Macey Baker, Amy Heineike, Maksim Shaposhnikov, Rob Willoughby, Dru Knox

    cs.SE · cs.AI · cs.CL

    Coding agents have become a major mode of software engineering, but the benchmarks we use to compare them were designed in a pre-agent era: they collapse model, harness, and environment into a single end-to-end score, typically computed against one reference solution, with no component-level signal for iteration. We argue that current coding benchmarks are misaligned with agentic software engineering. A coding agent in practice is not a...

    arxiv.org/abs/2606.17799 · PDF

  4. 04

    FacProcessTwin: An LLM-Based System for Process Twin Development

    Yash Pulse, Yong-Bin Kang, Abhik Banerjee, Prem Prakash Jayaraman

    cs.SE · cs.AI

    Process twins provide real-time representations of entire production processes. By capturing how process steps interact, rather than monitoring a single machine in isolation as an asset-based digital twin does, they have the potential to drive efficiency gains across the whole process. However, developing a process twin is costly. It requires accurately modelling the entire production process: its process steps, the equipment and...

    arxiv.org/abs/2606.17666 · PDF

  5. 05

    Understanding LLMs in Title-Abstract Screening: From Disagreements to Recommendations

    Mika Mäntylä, Patricia Matsubara, Katia Romero Felizardo, Miikka Kuutila, Marco Gerosa, Savio de Sousa Sampaio,...

    cs.SE · cs.AI

    Several studies have examined the use of large language models (LLMs) for title-abstract screening in systematic reviews (SRs), reporting mixed accuracy. However, questions of reliability remain largely unaddressed. In this study, we go beyond quantitative LLM-human agreement metrics and qualitatively investigate how and why LLMs fail. We also propose actionable recommendations. We analyzed disagreements between LLMs and researchers across...

    arxiv.org/abs/2606.17588 · PDF

  6. 06

    Unlocking LLM Code Correction with Iterative Feedback Loops

    Le Zhang, Suresh Kothari

    cs.SE · cs.AI

    Large Language Models have shown remarkable capabilities in code generation. However, most existing evaluations focus only on single-attempt accuracy and overlook the iterative refinement process that is central to real-world programming. This study presents a systematic investigation of LLMs' ability to rectify their own code through execution feedback. Using real-world programming problems across four models and two major programming...

    arxiv.org/abs/2606.17514 · PDF

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