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A User-friendly and Powerful R Analysis of Large-scale Datasets

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A Large-Scale Collection Of (Non-)Actionable Static Code Analysis Reports.

Dávid Kószó¹, Tamás Aladics^2,3, Rudolf Ferenc¹

¹University of Szeged, Szeged, Hungary.

Scientific Data

|November 28, 2025

Summary

Static Code Analysis (SCA) tools create too many alerts, causing alert fatigue. We introduce a new method and dataset (NASCAR) to filter non-actionable SCA warnings for better code quality.

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Area of Science:

Software Engineering
Computer Science
Machine Learning

Background:

Static Code Analysis (SCA) tools are essential for identifying software defects but generate numerous non-actionable warnings.
This alert overload leads to alert fatigue, diminishing developer productivity and code quality.
Existing datasets for training machine learning models to filter SCA warnings are scarce, especially for Java.

Purpose of the Study:

To address the scarcity of actionable SCA warning data.
To develop a methodology for collecting and categorizing SCA warnings, differentiating actionable from non-actionable alerts.
To create a large-scale dataset of Java SCA warnings to aid research and improve SCA tool usability.

Main Methods:

Developed a novel methodology for collecting and categorizing SCA warnings.
Applied the methodology to distinguish between actionable and non-actionable warnings.
Generated a dataset of over 1 million Java source code warnings, named NASCAR (Non-)Actionable Static Code Analysis Reports.

Main Results:

Successfully created a large-scale dataset (NASCAR) containing over 1 million Java SCA warnings.
The dataset categorizes warnings as actionable or non-actionable, providing valuable data for machine learning.
The methodology and dataset are publicly available to support further research in SCA and alert fatigue mitigation.

Conclusions:

The developed methodology effectively distinguishes actionable from non-actionable SCA warnings.
The NASCAR dataset provides a crucial resource for training models to combat alert fatigue in SCA.
Publicly releasing the dataset and tools will accelerate advancements in SCA tool accuracy and developer productivity.