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Found 15 papers, 5 papers with code
Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation
Large language models (LLMs) have demonstrated notable proficiency in code generation, with numerous prior studies showing their promising capabilities in various development scenarios.
I came, I saw, I certified: some perspectives on the safety assurance of cyber-physical systems
In such contexts, detecting assurance deficits, relying on patterns to improve the structure of assurance cases, improving existing assurance case notations, and (semi-)automating the generation of assurance cases are key to develop compelling assurance cases and foster consumer acceptance.
Log-based Anomaly Detection of Enterprise Software: An Empirical Study
In addition, the studied open-source datasets are typically very large in size with logging statements that do not change much over time, which may not be the case with a dataset from an industrial service that is relatively new.
Assessing Evaluation Metrics for Neural Test Oracle Generation
We apply two different correlation analyses between these two different sets of metrics.
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents
In this research, we demonstrate that a "gray-box" approach for attacking a Deep RL-based trading agent is possible by trading in the same stock market, with no extra access to the trading agent.
Domain Adaptation for Deep Unit Test Case Generation
We compare our approach with (a) CodeT5 fine-tuned on the test generation without DA, (b) the A3Test tool, and (c) GPT-4, on 5 projects from the Defects4j dataset.
FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair
To address this gap, in this paper, we focus on predicting the type of fix that is required to remove flakiness and then repair the test code on that basis.
MDA: Availability-Aware Federated Learning Client Selection
Since many devices may be unavailable in cross-device FL, and communication between the server and all clients is extremely costly, only a fraction of clients gets selected for training at each round.
Improving the Performance of DNN-based Software Services using Automated Layer Caching
The proposed solution is an automated online layer caching mechanism that allows early exiting of a large model during inference time if the cache model in one of the early exits is confident enough for final prediction.
Test2Vec: An Execution Trace Embedding for Test Case Prioritization
In this paper, we hypothesize that execution traces of the test cases can be a good alternative to abstract their behavior for automated testing tasks.
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness
Therefore, to better understand the current quality status and challenges of these SOTA FL techniques in the presence of attacks and faults, we perform a large-scale empirical study to investigate the SOTA FL's quality from multiple angles of attacks, simulated faults (via mutation operators), and aggregation (defense) methods.
Robustness Analysis of Deep Learning Frameworks on Mobile Platforms
This requires frameworks to execute machine learning models (e. g., Deep Neural Networks) on mobile devices.
A Pragmatic Approach for Hyper-Parameter Tuning in Search-based Test Case Generation
To evaluate our approach, we exhaustively analyze 1, 200 hyper-parameter configurations of a well-known search-based test generation tool (EvoSuite) for 250 classes of 19 projects from benchmarks such as SF110 and SBST2018 tool competition.
Software Engineering
Deep State Inference: Toward Behavioral Model Inference of Black-box Software Systems
Many software engineering tasks, such as testing, and anomaly detection can benefit from the ability to infer a behavioral model of the software. Most existing inference approaches assume access to code to collect execution sequences.
Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems
Our comparison with several traditional time series change point detection techniques showed that our approach improves their performance by up to 102%, in terms of finding state change points, measured by F1 score.
