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20232026
most citedFlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair

19 citations · 27 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.SE2026

Cleaning Logs for Downstream Tasks (Registered Report)

Zahra G. Yazdi, Van-Hoang Le, Nyyti Saarimäki +3

Background: Software systems generate logs during execution to record critical events and runtime information for troubleshooting and monitoring. However, in practice, logs often c…

cs.SE2026

MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models

Pablo Valle, Shaukat Ali, Aitor Arrieta +1

Vision-Language-Action (VLA) models are emerging robotic control systems that integrate perception, language understanding, and action generation in a unified architecture. Existin…

cs.SE2026

SAGA: Detecting Security Vulnerabilities Using Static Aspect Analysis

Yoann Marquer, Domenico Bianculli, Lionel C. Briand

Python is one of the most popular programming languages; as such, projects written in Python involve an increasing number of diverse security vulnerabilities. However, existing sta…

cs.SE2024★ 5 cited

Testing CPS with Design Assumptions-Based Metamorphic Relations and Genetic Programming

Claudio Mandrioli, Seung Yeob Shin, Domenico Bianculli +1

Cyber-Physical Systems (CPSs) software is used to enforce desired behaviours on physical systems. To test the interaction between the CPS software and the system's physics, enginee…

cs.SE2024★ 3 cited

LLM meets ML: Data-efficient Anomaly Detection on Unstable Logs

Fatemeh Hadadi, Qinghua Xu, Domenico Bianculli +1

Most log-based anomaly detectors assume logs are stable, though logs are often unstable due to software or environmental changes. Anomaly detection on unstable logs (ULAD) is there…

cs.SE2023★ 19 cited

FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair

Sakina Fatima, Hadi Hemmati, Lionel Briand

Flaky tests are problematic because they non-deterministically pass or fail for the same software version under test, causing confusion and wasting development effort. While machin…