arxiv: 2605.10890 · v1 · submitted 2026-05-11 · 💻 cs.SE

Recognition: no theorem link

CppPerf: An Automated Pipeline and Dataset for Performance-Improving C++ Commits

Tommy Ho , Khashayar Etemadi , Zhendong Su

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:43 UTC · model grok-4.3

classification 💻 cs.SE

keywords C++ performancebug repairbenchmark creationcommit miningpatch datasetautomated pipelinesoftware maintenance

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The pith

An automated pipeline mines performance-improving commits from C++ projects to create a benchmark of 347 verified patches.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a configurable pipeline for locating commits that reduce the execution time of C++ code in open-source projects. It uses structural analysis of changes, classification by a language model, and isolated execution in containers to verify improvements and generate reusable images. Applying this process yields a collection of 347 manually confirmed patches drawn from dozens of established projects, with many spanning multiple files. This resource supports testing of tools designed to automatically fix performance issues at the repository level. Early evaluation shows that one such tool resolves only a modest share of the cases, highlighting the difficulty of the task in practice.

Core claim

By combining structural commit filtering with language-model-based classification and containerized build and test stages, the pipeline produces a benchmark of 347 manually verified performance-improving patches from 42 C++ repositories, 39 percent of which are multi-file, and demonstrates that current automated repair approaches succeed on only 13.5 percent of these patches.

What carries the argument

The automated mining pipeline that applies structural filtering, language-model classification of commits, and containerized execution to identify and package genuine performance improvements.

If this is right

Repository-level repair tools can now be evaluated on a set of real multi-file C++ changes rather than synthetic examples.
The low fix rate on the collected patches indicates that performance bug repair for C++ codebases remains difficult for existing systems.
The open availability of the pipeline and dataset allows other researchers to extend the collection or apply it to related problems.
The containerized format ensures that each patch can be reproduced and tested consistently across different environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar mining techniques could be developed for other programming languages to create comparable benchmarks.
Tools that succeed on this dataset may need specialized handling for performance metrics and multi-file edits.
Expanding the dataset over time could track improvements in repair capabilities as new methods emerge.

Load-bearing premise

The combination of automated filters, classification, and manual review accurately identifies commits that genuinely improve performance without including many false positives or missing representative cases.

What would settle it

Independent re-measurement of execution times for the patches outside their original containers shows no improvement or inconsistent results for a substantial number of them.

Figures

Figures reproduced from arXiv: 2605.10890 by Khashayar Etemadi, Tommy Ho, Zhendong Su.

**Figure 1.** Figure 1: An overview of the CppPerf-Mine workflow. the functionality of the program. Fifth, we run the tests on the latest version of the project to confirm that they build and pass successfully. After selecting the repositories that meet the requirements, CppPerfMine examines their commit histories and filters commits according to three structural criteria. First, the commit should be from the userdefined period… view at source ↗

read the original abstract

Recent progress in automated repair of performance bugs demands realistic, executable benchmarks. However, existing C++ performance benchmarks are largely built from competitive programming submissions, and recent real-world benchmarks predominantly target Python and .NET. To fill this gap, we present CppPerf-Mine, a configurable pipeline that mines execution-time-improving patches from open-source C++ repositories on GitHub by combining structural commit filtering, an LLM-based commit classifier, and a containerized build & test stage that produces fully reproducible Docker images for each patch. Using CppPerf-Mine, we build CppPerf-DB, a benchmark comprising 347 manually verified patches from 42 mature C++ repositories, 39% of which are multi-file, enabling the evaluation of repository-level repair tools. In our preliminary study, OpenHands correctly fixes only 13.5% of the patches in CppPerf-DB, confirming that real-world C++ performance repair remains an open challenge. CppPerf-Mine and CppPerf-DB are open-source and publicly available at: https://doi.org/10.5281/zenodo.20097425. In addition, a demonstration video is available at: https://www.youtube.com/watch?v=nixlupIgSdM.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This supplies a needed real-world C++ performance patch dataset, but the performance measurement validation is underspecified.

read the letter

This paper's key offering is a new dataset of 347 manually verified C++ patches that improve execution time, mined from 42 real GitHub repositories using their CppPerf-Mine pipeline. It stands out for targeting multi-file changes in mature codebases rather than single-file or contest problems. The pipeline does a solid job combining commit structure filters, LLM-based classification of performance fixes, and containerized execution to confirm speedups before manual review. Releasing the Docker images and the full dataset publicly is helpful for others who want to build on it. Their preliminary result with OpenHands fixing only 13.5% of the cases gives a useful baseline that real-world C++ performance repair is still challenging. The soft spot is the lack of detail on how performance improvements were measured. Without information on run counts, standardized inputs, or statistical checks to separate real gains from timing noise, it is possible that some patches reflect measurement artifacts. Manual verification of the commits would not necessarily catch this since it follows the timing step. This is a moderate issue that affects trust in the benchmark's quality. The work shows clear thinking in identifying the gap and building something executable and open. There are no signs of circular arguments or weak citations in what is described. This is aimed at researchers in automated repair and benchmarking for software engineering. Anyone evaluating tools on realistic C++ performance bugs would get concrete value from the dataset. It deserves a serious referee because the contribution is new and the motivation is sound. I recommend sending it to peer review, with feedback focused on adding the missing measurement details and perhaps more on the verification process.

Referee Report

1 major / 2 minor

Summary. The paper introduces CppPerf-Mine, a configurable pipeline that mines GitHub for execution-time-improving C++ commits via structural filtering, LLM-based commit classification, and a containerized build-and-test stage that emits reproducible Docker images per patch. Applying the pipeline yields CppPerf-DB, a benchmark of 347 manually verified patches drawn from 42 mature repositories (39% multi-file). A preliminary evaluation shows that OpenHands correctly repairs only 13.5% of the patches, which the authors interpret as evidence that real-world C++ performance repair remains an open challenge. The pipeline and dataset are released publicly.

Significance. If the patches are confirmed to be genuine, reproducible performance improvements, the work supplies a valuable real-world benchmark that moves beyond competitive-programming or synthetic C++ examples and directly supports evaluation of repository-level repair tools. The explicit production of containerized, reproducible environments and the public release of both pipeline and dataset are concrete strengths that facilitate follow-on research and reproducibility. The reported 13.5% success rate, if reliable, usefully quantifies the current gap for automated tools on authentic C++ performance changes.

major comments (1)

[§3.2] §3.2 (containerized build & test stage): the manuscript provides no details on the performance-measurement protocol—number of repeated runs, input standardization, warm-up procedures, variance-reduction techniques, or statistical criteria (e.g., minimum speedup threshold or significance test) used to declare a commit performance-improving. Because timing measurements are inherently noisy, the absence of these controls leaves open the possibility that some fraction of the 347 accepted patches reflect measurement artifacts rather than true optimizations. This directly affects the validity of CppPerf-DB and the interpretation of the 13.5% OpenHands result.

minor comments (2)

[§4.1] §4.1 (manual verification): the description of the manual verification process would benefit from explicit reporting of inter-rater agreement statistics or the exact criteria used to confirm that a patch indeed improves performance.
[Table 1] Table 1 (dataset statistics): adding a column or footnote that reports the number of commits filtered at each pipeline stage would help readers assess selection bias.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and positive review. The feedback on the performance-measurement protocol is well-taken, and we address it directly below. We will revise the manuscript accordingly.

read point-by-point responses

Referee: [§3.2] §3.2 (containerized build & test stage): the manuscript provides no details on the performance-measurement protocol—number of repeated runs, input standardization, warm-up procedures, variance-reduction techniques, or statistical criteria (e.g., minimum speedup threshold or significance test) used to declare a commit performance-improving. Because timing measurements are inherently noisy, the absence of these controls leaves open the possibility that some fraction of the 347 accepted patches reflect measurement artifacts rather than true optimizations. This directly affects the validity of CppPerf-DB and the interpretation of the 13.5% OpenHands result.

Authors: We agree that the current description in §3.2 is insufficiently detailed regarding the performance-measurement protocol. In the revised manuscript we will add an explicit subsection that documents the exact protocol used: the number of repeated runs performed for each test case, the standardization of inputs (fixed test cases drawn from each repository’s own test suite), warm-up procedures, variance-reduction steps (e.g., median timing and outlier discarding), and the statistical acceptance criteria (minimum speedup threshold together with the significance test employed). These parameters were applied consistently during dataset construction and will now be reported for full reproducibility. We also note that every patch in CppPerf-DB was subsequently subjected to manual verification by the authors, which included both diff inspection and execution inside the emitted Docker images; this human confirmation provides an independent safeguard against measurement noise. The 13.5 % OpenHands result is therefore based on a manually vetted set rather than solely on automated thresholds. revision: yes

Circularity Check

0 steps flagged

No significant circularity in dataset construction or evaluation chain.

full rationale

The paper constructs CppPerf-DB by applying structural commit filtering, LLM-based classification, containerized build/test execution, and manual verification to external GitHub data from 42 repositories. The resulting benchmark is then used to measure OpenHands success at 13.5%. No step reduces by construction to its own inputs via self-definition, fitted parameters renamed as predictions, or load-bearing self-citations. The chain depends on independent external sources and processes without circular equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the reliability of the mining pipeline and manual verification; no free parameters are described, and the work relies on standard assumptions about open-source commit data and LLM classification.

axioms (1)

domain assumption GitHub commit history contains identifiable performance-improving patches that can be extracted via structural and LLM-based filters
Invoked to justify the mining approach and dataset construction.

pith-pipeline@v0.9.0 · 5527 in / 1311 out tokens · 43869 ms · 2026-05-12T03:43:47.054982+00:00 · methodology

discussion (0)

Reference graph

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