BB
B.J. Brockbernd
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Asynchronous programming is often a difficult and non-trivial task. To make asynchronous programming more straightforward, languages are continuously introducing new syntax and patterns, making it easier to think about and develop solutions for concurrent problems.
JetBrains introduced coroutines for Kotlin in 2018. Although Kotlin coroutines promise safe execution and concise code, it is not immune to concurrency bugs such as deadlocks. Particular runBlocking deadlocks are common when working with Kotlin coroutines. While other languages have made various advancements in detecting deadlocks, Kotlin lags behind.
In this work, we present two static analysis techniques that help developers detect and prevent deadlocks. The first technique, focused on the runBlocking problem, successfully identified dangerous patterns in open source repositories, leading to their resolution. Additionally, this technique has been integrated into JetBrains flagship IDE: IntelliJ IDEA. The second technique, aimed at general deadlock detection, has been developed and tested as a prototype. By using existing modeling techniques combined with novel approaches we have been able to accurately predict deadlocks in a controlled environment. Overall, this study tackled a common problem in Kotlin coroutines and made the first steps toward general deadlock detection. ...
JetBrains introduced coroutines for Kotlin in 2018. Although Kotlin coroutines promise safe execution and concise code, it is not immune to concurrency bugs such as deadlocks. Particular runBlocking deadlocks are common when working with Kotlin coroutines. While other languages have made various advancements in detecting deadlocks, Kotlin lags behind.
In this work, we present two static analysis techniques that help developers detect and prevent deadlocks. The first technique, focused on the runBlocking problem, successfully identified dangerous patterns in open source repositories, leading to their resolution. Additionally, this technique has been integrated into JetBrains flagship IDE: IntelliJ IDEA. The second technique, aimed at general deadlock detection, has been developed and tested as a prototype. By using existing modeling techniques combined with novel approaches we have been able to accurately predict deadlocks in a controlled environment. Overall, this study tackled a common problem in Kotlin coroutines and made the first steps toward general deadlock detection. ...
Asynchronous programming is often a difficult and non-trivial task. To make asynchronous programming more straightforward, languages are continuously introducing new syntax and patterns, making it easier to think about and develop solutions for concurrent problems.
JetBrains introduced coroutines for Kotlin in 2018. Although Kotlin coroutines promise safe execution and concise code, it is not immune to concurrency bugs such as deadlocks. Particular runBlocking deadlocks are common when working with Kotlin coroutines. While other languages have made various advancements in detecting deadlocks, Kotlin lags behind.
In this work, we present two static analysis techniques that help developers detect and prevent deadlocks. The first technique, focused on the runBlocking problem, successfully identified dangerous patterns in open source repositories, leading to their resolution. Additionally, this technique has been integrated into JetBrains flagship IDE: IntelliJ IDEA. The second technique, aimed at general deadlock detection, has been developed and tested as a prototype. By using existing modeling techniques combined with novel approaches we have been able to accurately predict deadlocks in a controlled environment. Overall, this study tackled a common problem in Kotlin coroutines and made the first steps toward general deadlock detection.
JetBrains introduced coroutines for Kotlin in 2018. Although Kotlin coroutines promise safe execution and concise code, it is not immune to concurrency bugs such as deadlocks. Particular runBlocking deadlocks are common when working with Kotlin coroutines. While other languages have made various advancements in detecting deadlocks, Kotlin lags behind.
In this work, we present two static analysis techniques that help developers detect and prevent deadlocks. The first technique, focused on the runBlocking problem, successfully identified dangerous patterns in open source repositories, leading to their resolution. Additionally, this technique has been integrated into JetBrains flagship IDE: IntelliJ IDEA. The second technique, aimed at general deadlock detection, has been developed and tested as a prototype. By using existing modeling techniques combined with novel approaches we have been able to accurately predict deadlocks in a controlled environment. Overall, this study tackled a common problem in Kotlin coroutines and made the first steps toward general deadlock detection.
This research proposes a novel method to classify cognitive behavior based on eye-movement data. Most state-of-the-art approaches use conventional machine learning techniques needing manual feature extraction. This experiment explores the possibility of applying deep learning algorithms to cognitive activity recognition for feature extraction and classification of eye-movement data. Convolutional neural networks will be explored in particular. Two neural networks are proposed and optimized using hyperparameter tuning. This research shows that convolutional neural networks can indeed perform cognitive activity recognition. Some neural networks significantly outperform the state-of-the-art methods for known subjects. However, further research is needed to improve performance in classifying activities for unknown subjects.
...
This research proposes a novel method to classify cognitive behavior based on eye-movement data. Most state-of-the-art approaches use conventional machine learning techniques needing manual feature extraction. This experiment explores the possibility of applying deep learning algorithms to cognitive activity recognition for feature extraction and classification of eye-movement data. Convolutional neural networks will be explored in particular. Two neural networks are proposed and optimized using hyperparameter tuning. This research shows that convolutional neural networks can indeed perform cognitive activity recognition. Some neural networks significantly outperform the state-of-the-art methods for known subjects. However, further research is needed to improve performance in classifying activities for unknown subjects.