Blar i BI Open på forfatter "Moonen, Leon"
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CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering
Malik, Sehrish; Naqvi, Moeen; Moonen, Leon (Chapter, 2023)There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems due to their adoption in critical and highly dynamic environments. However, there is a lack of systematic evaluation ... -
A Comparative Study on Large Language Models for Log Parsing
Astekin, Merve; Hort, Max; Moonen, Leon (Chapter, 2024)Most software systems used in production generate system logs that provide a rich source of information about the status and execution behavior of the system. These logs are commonly used to ensure the reliability and ... -
An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code
Hort, Max; Grishina, Anastasiia; Moonen, Leon (Chapter, 2023)Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair. Large amounts of data for training such models benefit the models’ performance. ... -
Extending the range of bugs that automated program repair can handle
Al-Bataineh, Omar; Moonen, Leon; Vidziunas, Linas (Journal article; Peer reviewed, 2024)Modern automated program repair (APR) is well-tuned to finding and repairing bugs that introduce observable erroneous behavior to a program. However, a significant class of bugs does not lead to observable behavior (e.g., ... -
Extending the range of bugs that automated program repair can handle
Al-Bataineh, Omar; Moonen, Leon; Vidziunas, Linas (Journal article; Peer reviewed, 2023)Modern automated program repair (APR) is well-tuned to finding and repairing bugs that introduce observable erroneous behavior to a program. However, a significant class of bugs does not lead to observable behavior (e.g., ... -
Fully Autonomous Programming with Large Language Models
Liventsev, Vadim; Anastasiia, Grishina; Härmä, Aki; Moonen, Leon (Chapter, 2023)Current approaches to program synthesis with Large Language Models (LLMs) exhibit a “near miss syndrome”: they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics ... -
The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
Grishina, Anastasiia; Hort, Max; Moonen, Leon (Chapter, 2023)The use of modern Natural Language Processing (NLP) techniques has shown to be beneficial for software engineering tasks, such as vulnerability detection and type inference. However, training deep NLP models requires ...