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Found 7 papers, 4 papers with code
Breaking the Silence: the Threats of Using LLMs in Software Engineering
Large Language Models (LLMs) have gained considerable traction within the Software Engineering (SE) community, impacting various SE tasks from code completion to test generation, from program repair to code summarization.
ENCODE: Encoding NetFlows for Network Anomaly Detection
The first step for these machine learning pipelines is to pre-process the data before it is given to the machine learning algorithm.
Improving Test Case Generation for REST APIs Through Hierarchical Clustering
With the ever-increasing use of web APIs in modern-day applications, it is becoming more important to test the system as a whole.
Multi-objective Test Case Selection Through Linkage Learning-based Crossover
Test Case Selection (TCS) aims to select a subset of the test suite to run for regression testing.
Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering
To understand whether and how researchers in SE address these threats, we surveyed 45 recent papers related to three predictive tasks: defect prediction (DP), predictive mutation testing (PMT), and code smell detection (CSD).
Software Engineering
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
We present ReproducedPapers. org: an open online repository for teaching and structuring machine learning reproducibility.
Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case Generation
This study shows that performance-aware test case generation requires solving two main challenges: providing a good approximation of resource usage with minimal overhead and avoiding detrimental effects on both final coverage and fault detection effectiveness.
