An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction
Rudolf Ferenc, P\'eter Gyimesi, G\'abor Gyimesi, Zolt\'an T\'oth,, Tibor Gyim\'othy

TL;DR
This paper introduces BugHunter, an automatically generated bug dataset capturing buggy and fixed code states over narrow timeframes, and demonstrates its effectiveness in building high-accuracy bug prediction models.
Contribution
The paper presents a novel, automatically created bug dataset that captures bug states over narrow timeframes, improving bug prediction model training.
Findings
Achieved F-measure over 0.74 in bug prediction models
Introduced a new dataset capturing bug and fix states over narrow timeframes
Validated the dataset's usefulness in bug prediction tasks
Abstract
Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning. We present the \emph{BugHunter Dataset}: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and…
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