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Rationale Dataset and Analysis for the Commit Messages of the Linux Kernel Out-of-Memory Killer

Mouna Dhaouadi, Bentley James Oakes, Michalis Famelis

TL;DR

The creation of a labelled dataset to analyze the code commit messages of the Linux Kernel Out-Of-Memory Killer component is detailed and aspects of rationale information, such as presence, temporal evolution, and structure are studied.

Abstract

Code commit messages can contain useful information on why a developer has made a change. However, the presence and structure of rationale in real-world code commit messages is not well studied. Here, we detail the creation of a labelled dataset to analyze the code commit messages of the Linux Kernel Out-Of-Memory Killer component. We study aspects of rationale information, such as presence, temporal evolution, and structure. We find that 98.9% of commits in our dataset contain sentences with rationale information, and that experienced developers report rationale in about 60% of the sentences in their commits. We report on the challenges we faced and provide examples for our labelling.

Rationale Dataset and Analysis for the Commit Messages of the Linux Kernel Out-of-Memory Killer

TL;DR

The creation of a labelled dataset to analyze the code commit messages of the Linux Kernel Out-Of-Memory Killer component is detailed and aspects of rationale information, such as presence, temporal evolution, and structure are studied.

Abstract

Code commit messages can contain useful information on why a developer has made a change. However, the presence and structure of rationale in real-world code commit messages is not well studied. Here, we detail the creation of a labelled dataset to analyze the code commit messages of the Linux Kernel Out-Of-Memory Killer component. We study aspects of rationale information, such as presence, temporal evolution, and structure. We find that 98.9% of commits in our dataset contain sentences with rationale information, and that experienced developers report rationale in about 60% of the sentences in their commits. We report on the challenges we faced and provide examples for our labelling.
Paper Structure (22 sections, 9 figures, 6 tables)

This paper contains 22 sections, 9 figures, 6 tables.

Figures (9)

  • Figure 1: Distribution of the sentences in our OOM dataset
  • Figure 2: Most frequent words per category, without overlap
  • Figure 3: Commit message size versus rationale density
  • Figure 4: Commits per author vs average rationale density
  • Figure 5: Monthly evolution of the average rationale density
  • ...and 4 more figures