43 citations · 73 across the 11 of their papers we have counts for
11 papers
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…
Towards Safety-Aware Mutation Testing for Autonomous Driving Systems
Donghwan Shin
Simulation-based testing is essential for ensuring the safety of Autonomous Driving Systems (ADS), yet the community lacks a systematic criterion for determining when we can safely…
Towards a Taxonomy of Software Log Smells
Nyyti Saarimäki, Donghwan Shin, Zahra G. Yazdi +1
Logging is an important part of modern software projects; logs are used in several tasks such as debugging and testing. Due to the complex nature of logging, it remains a difficult…
Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs
Ziyu Li, Donghwan Shin
Large Language Models (LLMs) have shown remarkable capabilities in processing both natural and programming languages, which have enabled various applications in software engineerin…
Impact of Log Parsing on Deep Learning-Based Anomaly Detection
Zanis Ali Khan, Donghwan Shin, Domenico Bianculli +1
Software systems log massive amounts of data, recording important runtime information. Such logs are used, for example, for log-based anomaly detection, which aims to automatically…
Systematic Evaluation of Deep Learning Models for Log-based Failure Prediction
Fatemeh Hadadi, Joshua H. Dawes, Donghwan Shin +2
With the increasing complexity and scope of software systems, their dependability is crucial. The analysis of log data recorded during system execution can enable engineers to auto…
Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search
Sepehr Sharifi, Donghwan Shin, Lionel C. Briand +1
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such bou…
Rigorous Assessment of Model Inference Accuracy using Language Cardinality
Donato Clun, Donghwan Shin, Antonio Filieri +1
Models such as finite state automata are widely used to abstract the behavior of software systems by capturing the sequences of events observable during their execution. Neverthele…
Literature Review to Collect Conceptual Variables of Scenario Methods for Establishing a Conceptual Scenario Framework
Young-Min Baek, Esther Cho, Donghwan Shin +1
Over recent decades, scenarios and scenario-based software/system engineering have been actively employed as essential tools to handle intricate problems, validate requirements, an…
PRINS: Scalable Model Inference for Component-based System Logs
Donghwan Shin, Domenico Bianculli, Lionel Briand
Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, qu…
Can Offline Testing of Deep Neural Networks Replace Their Online Testing?
Fitash Ul Haq, Donghwan Shin, Shiva Nejati +1
We distinguish two general modes of testing for Deep Neural Networks (DNNs): Offline testing where DNNs are tested as individual units based on test datasets obtained without invol…