Evaluating SZZ Implementations: An Empirical Study on the Linux Kernel
Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo

TL;DR
This study evaluates the effectiveness of six SZZ algorithms on a large, developer-reviewed dataset from the Linux kernel, revealing significant recall decline, the prevalence of ghost commits, and proposing an improved tracing method called TC-SZZ.
Contribution
The paper provides the first large-scale evaluation of SZZ algorithms on Linux kernel data and introduces TC-SZZ, a new method that improves bug-inducing commit identification.
Findings
SZZ algorithms' recall declined by 13.8% on Linux data.
17.47% of bug-fixing commits are ghost commits.
TC-SZZ identified 17.7% of failure cases excluding ghosts.
Abstract
The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as "ghost commits". The evaluation of how these ghost commits impact the SZZ algorithm remains limited. Moreover, these algorithms have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the corresponding bug-inducing commit(s) as a standard practice. As of v6.1-rc5, 76,046 pairs of bug-fixing patches and bug-inducing commits are available. This provides a unique opportunity to evaluate the SZZ algorithm on a large…
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Taxonomy
TopicsSoftware Engineering Research · Software Testing and Debugging Techniques · Software Reliability and Analysis Research
