A test suite optimization approach that leverages symbolic classification, a greedy algorithm, and a similarity measure reduces MBT-generated test suites for embedded software by identifying and eliminating redundancy while minimizing its impact on the fault detection rate.
Muhammad Nouman Zafar, Wasif Afzal, Eduard Paul Enoiu et al.· Software quality journal· 0 citations
Code, configurations, seeds, results and figures for a Monte Carlo simulation study of composite multi-beat, multi-view transoesophageal echocardiographic measurement rules for tricuspid regurgitant jet dimensions (beat averaging, percentage beat-consistency windows, largest view mean, millimetre cross-view triggers) a...
Kardokh Kakabra· Zenodo (CERN European Organi...· 0 citations
Software development is moving away from writing code line by line. Instead of using AI only for simple autocomplete, developers now manage multiple coding agents across different parts of a codebase at once. While these tools make individual tasks faster, recent data shows that teams are generating far more code witho...
Passive Time Difference of Arrival (TDoA) localization conventionally requires multiple synchronized receivers to obtain spatially diverse observations. In this paper, we show that the periodic nature of the 5G NR downlink Positioning Reference Signals (PRS), combined with receiver motion, provides an alternative sourc...
Marco Tricco, Samuele Zanini, Giuseppe Bianchi et al.· Proceedings of the 20th ACM...· 0 citations
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Robotic vehicles, such as Unmanned Aerial Vehicles (UAVs), are deployed in safety-critical applications ranging from package delivery to search and rescue operations. In these cyber-physical systems, software vulnerabilities can cause physical consequences such as collisions and mission failures, giving them direct saf...
Ashwin Nambiar, Shafiq-us Saleheen, Antonio Bianchi· Proceedings of the 2026 8th...· 0 citations
FlowAgent is an AI agent deployed at Google to automatically repair test failures in the pre-submit outer-loop workflow inside continuous integration systems, using a ReAct-style generate-and-validate loop and rigorous pre-execution and post-execution abstention filters to ensure high-quality suggestions under strict l...
Celal Ziftci, Spencer Greene, Raymond Liu et al.· 0 citations
SIGMA is proposed, a data generation and training pipeline enabling alignment self-improvement that generalizes to out-of-distribution settings and shows that a Model Spec balancing harmlessness and helpfulness, test-time reasoning for safety deliberation, and high-quality rubrics from SIGMA's task designer agent are c...
Jing-Yu Zhang, Shruti Palaskar, Daniel Khashabi et al.· 0 citations
A case study of a production healthcare platform built through coding agents and governed by an operator without formal software-engineering training, which found that tests, monitors and reviewing agents used to supervise the system were fallible.
This work proposes Failure-Driven Prompt Refinement (FDPR), a methodology that analyzes recurring model failures to guide evidence-based prompt refinement, and shows that failure-driven refinement improves the reliability of LLM-based vulnerability analysis while yielding reusable prompt design principles.
OBJECTIVE
The use of cell free fetal DNA has expanded to include autosomal dominant single gene conditions (cfDNA-SGD). Our aim is to estimate the cost-effectiveness of a cfDNA-SGD panel in a general obstetric population as compared to offering this test only in the setting of anomalies.
METHODS
Using TreeAge softwar...
M. Bunnell, Claire Packer, Sophie Adams et al.· Prenatal Diagnosis· 0 citations
Objectives
This study aims to investigate the potential relationship between radiomic features extracted from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) images of patients with breast cancer and molecular markers including estrogen receptor (ER), progesterone receptor (...
Busra Aydur Puren, Semra Usta, Y. Salihoğlu et al.· Molecular Imaging and Radion...· 0 citations
Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, existing agents remain brittle on long-horizon workflows, where they must repeatedly reconstruct program state scattered across source files, configurations, tests, depende...
Hai-Bo Jin, Xin-Jie Li, Peng Kuang et al.· 0 citations
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
