Mining ChallengeMSR 2026

Accepted Papers

Call for Mining Challenge Papers

Mining challenge PDF available here
AIDev dataset preprint available here

Update 2025-12-30: Author Notification moved to Jan 19, 2026, and Camera Ready to Jan 26, 2026.

Update 2025-11-06: We updated the Zenodo link for the dataset and added a new FAQ section.

Update 2025-10-28: Paper Deadline is now Dec 23, 2025 (AoE) to avoid the holiday period.

Update 2025-10-14: The submission deadlines have been extended to provide authors with more time to prepare submissions.

Update 2025-09-29: Accepted papers will be published as Short Papers in the ACM Digital Library.

AI coding agents are rapidly reshaping the landscape of software engineering by autonomously developing features, fixing bugs, and writing tests. These tools, such as Claude Code, Cursor, Devin, GitHub Copilot, and OpenAI Codex, are no longer just assisting developers; they are becoming active AI teammates in the software development process. Yet, despite their growing presence, the research community lacks a comprehensive, large-scale understanding of how AI coding agents collaborate with developers in real-world projects: how they propose code changes, how developers respond, and what kinds of collaboration patterns emerge.

This year’s MSR Mining Challenge invites the global research community to explore unprecedented questions and present their insights using AIDev, the first large-scale, openly available dataset capturing agent-authored pull requests (Agentic-PRs) from real-world GitHub repositories:

• Scale: 932,791 Agentic-PRs
• Breadth: 116,211 repositories and 72,189 developers, across five AI agents (Claude Code, Cursor, Devin, GitHub Copilot, OpenAI Codex)
• Depth: 33,596 curated Agentic-PRs from 2,807 popular repositories (over 100 stars), enriched with comments, reviews, commits, and related issues

Challenge

The AIDev dataset opens up rich and timely research directions around AI adoption, code quality, testing, review dynamics, risks, and human-AI collaboration in software engineering. Example research questions include (but are not limited to):

1) Adoption and Practices

i. Who adopts Coding Agents on GitHub (e.g., newcomers vs. experienced developers)?
ii. How do adoption patterns vary across repositories and ecosystems?
iii. What practices (e.g., PR size, task type, and commit granularity) correlate with the quality of Agentic-PRs?
iv. How can these practices inform concrete guidelines for developers to work with Agentic-PRs?

2) Code Patch Characteristics

i. How do Agentic-PRs change code (e.g., additions, deletions, files touched)?
ii. How consistent are their descriptions with the actual code changes?
iii. To what extent do Agentic-PRs introduce original code versus reusing existing snippets?
iv. What are the implications for maintainability?

3) Testing Behavior

i. How frequently do Coding Agents contribute tests? What types (e.g., unit, integration, end-to-end) are most common?
ii. What is the test-to-code churn ratio across ecosystems?
iii. When tests are missing in initial Agentic-PRs, do developers intervene to ensure reliable software testing (via follow-up commits or related PRs)?

4) Review Dynamics

i. What aspects of Agentic-PRs (e.g., correctness, style, security, testing) receive the most attention during review?
ii. To what extent do Coding Agents address review comments?
iii. Which comment types are challenging for agents to resolve?

5) Failure Patterns and Risks

i. What common failure patterns and code quality issues appear in Agentic-PRs? Why do they occur?
ii. How can we leverage these insights to reduce failure rates, optimize human–AI collaboration, and improve AI model training that prioritizes learning from mistakes?
iii. How well can early signals (e.g., PR description, touched paths, and patch characteristics) predict Agentic-PRs rejection or review effort?
iv. How frequently do Agentic-PRs introduce or mitigate security vulnerabilities?

We also suggest checking our preprint paper for more research questions and ideas: https://arxiv.org/abs/2507.15003

How to Participate in the Challenge

First, familiarize yourself with the AIDev dataset:

• The details about the AIDev infrastructure and the data are provided in our preprint.
• The dataset can be downloaded from either Hugging Face or Zenodo.
• GitHub (example code & notebooks): https://github.com/SAILResearch/AI_Teammates_in_SE3.
• An example Jupyter notebook demonstrating how to load and analyze the dataset is available here, you can also open it directly in Google Colab.

Use the dataset to answer your research questions, and report your findings in a four-page challenge paper that you submit to our challenge. If your paper is accepted, present your results at MSR 2026 in Rio de Janeiro, Brazil!

Submission

IMPORTANT: Accepted papers in Mining Challenge will be published as Short Papers in the ACM Digital Library. Starting 2026, all articles published by ACM will be made Open Access. This is greatly beneficial to the advancement of computer science and leads to increased usage and citation of research.

• Most authors will be covered by ACM OPEN agreements by that point and will not have to pay Article Processing Charges (APC). Check if your institution participates in ACM OPEN.
• Authors not covered by ACM OPEN agreements may have to pay APC; however, ACM is offering several automated and discretionary APC Waivers and Discounts.

A challenge paper should describe the results of your work by providing an introduction to the problem you address and why it is worth studying, the version of the dataset you used, the approach and tools you used, your results and their implications, and conclusions. Make sure your report highlights the contributions and the importance of your work. See also our open science policy regarding the publication of software and additional data you used for the challenge.

To ensure clarity and consistency in research submissions:

• When detailing methodologies or presenting findings, authors should specify which snapshot/version of the AIDev dataset was utilized.
• Given the continuous updates to the dataset, authors are reminded to be precise in their dataset references. This will help maintain transparency and ensure consistent replication of results.

All authors should use the official “ACM Primary Article Template”, as can be obtained from the ACM Proceedings Template page. LaTeX users should use the sigconf option, as well as the review (to produce line numbers for easy reference by the reviewers) and anonymous (omitting author names) options. To that end, the following LaTeX code can be placed at the start of the LaTeX document:

\documentclass[sigconf,review,anonymous]{acmart}
\acmConference[MSR 2026]{MSR '26: Proceedings of the 23rd International Conference on Mining Software Repositories}{April 2026}{Rio de Janeiro, Brazil}

Submissions to the Challenge Track can be made via the submission site by the submission deadline. We encourage authors to upload their paper info early (the PDF can be submitted later) to properly enter conflicts for anonymous reviewing. All submissions must adhere to the following requirements:

• Submissions must not exceed the page limit (4 pages plus 1 additional page of references). The page limit is strict, and it will not be possible to purchase additional pages at any point in the process (including after acceptance).
• Submissions must strictly conform to the ACM formatting instructions. Alterations of spacing, font size, and other changes that deviate from the instructions may result in desk rejection without further review.
• Submissions must not reveal the authors’ identities. The authors must make every effort to honor the double-anonymous review process. In particular, the authors’ names must be omitted from the submission and references to their prior work should be in the third person. Further advice, guidance, and explanation about the double-anonymous review process can be found in the Q&A page for ICSE 2026.
• Submissions should consider the ethical implications of the research conducted within a separate section before the conclusion.
• The official publication date is the date the proceedings are made available in the ACM or IEEE Digital Libraries. This date may be up to two weeks prior to the first day of the ICSE 2026. The official publication date affects the deadline for any patent filings related to published work.
• Purchases of additional pages in the proceedings are not allowed.

Any submission that does not comply with these requirements is likely to be desk rejected by the PC Chairs without further review. In addition, by submitting to the MSR Challenge Track, the authors acknowledge that they are aware of and agree to be bound by the following policies:

• The ACM Policy and Procedures on Plagiarism and the IEEE Plagiarism FAQ. In particular, papers submitted to MSR 2026 must not have been published elsewhere and must not be under review or submitted for review elsewhere whilst under consideration for MSR 2026. Contravention of this concurrent submission policy will be deemed a serious breach of scientific ethics, and appropriate action will be taken in all such cases (including immediate rejection and reporting of the incident to ACM/IEEE). To check for double submission and plagiarism issues, the chairs reserve the right to (1) share the list of submissions with the PC Chairs of other conferences with overlapping review periods and (2) use external plagiarism detection software, under contract to the ACM or IEEE, to detect violations of these policies.
• The authorship policy of the ACM and the authorship policy of the IEEE.

Upon notification of acceptance, all authors of accepted papers will be asked to fill a copyright form and will receive further instructions for preparing the camera-ready version of their papers. At least one author of each paper is expected to register and present the paper at the MSR 2026 conference. All accepted contributions will be published in the electronic proceedings of the conference.

The AIDev dataset can be cited as:

@article{li2025aidev,
title={{The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering}}, 
author={Li, Hao and Zhang, Haoxiang and Hassan, Ahmed E.},
journal={arXiv preprint arXiv:2507.15003},
year={2025}
}

A preprint is available online: https://arxiv.org/abs/2507.15003

Submission Site

Papers must be submitted through HotCRP: https://msr2026-challenge.hotcrp.com/

Important Dates (AoE)

• Abstract Deadline: Dec 18, 2025 (Optional, but encouraged to help us plan the review process)
• Paper Deadline: Dec 23, 2025
• Author Notification: Jan 19, 2026
• Camera Ready Deadline: Jan 26, 2026

Open Science Policy

Openness in science is key to fostering progress via transparency, reproducibility and replicability. Our steering principle is that all research output should be accessible to the public and that empirical studies should be reproducible. In particular, we actively support the adoption of open data and open source principles. To increase reproducibility and replicability, we encourage all contributing authors to disclose:

• the source code of the software they used to retrieve and analyze the data
• the (anonymized and curated) empirical data they retrieved in addition to the AIDev dataset
• a document with instructions for other researchers describing how to reproduce or replicate the results

Already upon submission, authors can privately share their anonymized data and software on archives such as Zenodo or Figshare (tutorial available here). Zenodo accepts up to 50GB per dataset (more upon request). There is no need to use Dropbox or Google Drive. After acceptance, data and software should be made public so that they receive a DOI and become citable. Zenodo and Figshare accounts can easily be linked with GitHub repositories to automatically archive software releases. In the unlikely case that authors need to upload terabytes of data, Archive.org may be used.

We recognise that anonymizing artifacts such as source code is more difficult than preserving anonymity in a paper. We ask authors to take a best effort approach to not reveal their identities. We will also ask reviewers to avoid trying to identify authors by looking at commit histories and other such information that is not easily anonymized. Authors wanting to share GitHub repositories may want to look into using https://anonymous.4open.science/ which is an open source tool that helps you to quickly double-blind your repository.

We encourage authors to self-archive pre- and postprints of their papers in open, preserved repositories such as arXiv.org. This is legal and allowed by all major publishers including ACM and IEEE and it lets anybody in the world reach your paper. Note that you are usually not allowed to self-archive the PDF of the published article (that is, the publisher proof or the Digital Library version). Please note that the success of the open science initiative depends on the willingness (and possibilities) of authors to disclose their data and that all submissions will undergo the same review process independent of whether or not they disclose their analysis code or data. We encourage authors who cannot disclose industrial or otherwise non-public data, for instance due to non-disclosure agreements, to provide an explicit (short) statement in the paper.

Best Mining Challenge Paper Award

As mentioned above, all submissions will undergo the same review process independent of whether or not they disclose their analysis code or data. However, only accepted papers for which code and data are available on preserved archives, as described in the open science policy, will be considered by the program committee for the best mining challenge paper award.

Best Student Presentation Award

Like in the previous years, there will be a public voting during the conference to select the best mining challenge presentation. This award often goes to authors of compelling work who present an engaging story to the audience. Only students can compete for this award.

FAQ

Q1. Can we augment AIDev with additional data for the challenge?

Yes. You are welcome and encouraged to “bring your own data” (BYOD) by integrating the AIDev dataset with information from other public, readily available sources (e.g., GitHub REST/GraphQL APIs, repository clones, ecosystem registries). Please document all sources and extraction steps. We urge participants to thoroughly consider the ethical implications of merging the AIDev dataset with other sources. The share or use of personally identifiable information (PII) is strictly prohibited.

Q2. Some PRs seem to have missing patch content. What happened and what should we do?

The dataset has been updated to include all available patches provided by GitHub API. However, the GitHub API may omit content for large patches. If you need the exact patch for these cases, you may need to clone the source repositories and obtain the commit diffs locally. When using patches, verify and comply with the original repository licenses.

Q3. Where is the data dictionary/table?

A data dictionary is available here: https://huggingface.co/datasets/hao-li/AIDev/blob/main/data_table.md

Q4. What do the commit_stats_additions/deletions vs. additions/deletions fields mean in pr_commit_details?

• commit_stats_additions, commit_stats_deletions: Totals per commit (sum across all files touched in that commit).
• additions, deletions: Per-file counts within that commit (for the specific file row).

Call for Mining Challenge Proposals

The International Conference on Mining Software Repositories (MSR) has hosted a mining challenge since 2006. With this challenge, we call upon everyone interested to apply their tools to a common dataset. The challenge is for researchers and practitioners to bravely use their mining tools and approaches on a dare.

One of the secret ingredients behind the success of the International Conference on Mining Software Repositories (MSR) is its annual Mining Challenge, in which MSR participants can showcase their techniques, tools, and creativity on a common data set. In true MSR fashion, this data set is a real data set contributed by researchers in the community, solicited through an open call. There are many benefits of sharing a data set for the MSR Mining Challenge. The selected challenge proposal explaining the data set will appear in the MSR 2026 proceedings, and the challenge papers using the data set will be required to cite the challenge proposal or an existing paper of the researchers about the selected data set. Furthermore, the authors of the data set will join the MSR 2026 organizing committee as Mining Challenge (co-)chair(s), who will manage the reviewing process (e.g., recruiting a Challenge PC, managing submissions, and reviewing assignments). Finally, it is not uncommon for challenge data sets to feature in MSR and other publications well after the edition of the conference in which they appear!

If you would like to submit your dataset for consideration for the 2026 MSR Mining Challenge, prepare a short proposal (1-2 pages plus appendices, if needed) containing the following information:

1. Title of data set.
2. High-level overview:
   • Short description, including what types of artifacts the data set contains.
   • Summary statistics (how many artifacts of different types).
3. Internal structure:
   • How are the data structured and organized?
   • (Link to) Schema, if applicable
4. How to access:
   • How can the data set be obtained?
   • What are the recommended ways to access it? Include examples of specific tools, shell commands, etc, if applicable.
   • What skills, infrastructure, and/or credentials would challenge participants need to effectively work with the data set?
5. What kinds of research questions do you expect challenge participants could answer?
6. A link to a (sub)sample of the data for the organizing committee to pursue (e.g., via GitHub, Zenodo, Figshare).

Submissions must conform to the IEEE conference proceedings template, specified in the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt type, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf options). Submit your proposal here.

The first task of the authors of the selected proposal will be to prepare the Call for Challenge Papers, which outlines the expected content and structure of submissions, as well as the technical details of how to access and analyze the dataset. This call will be published on the MSR website on September 2nd. By making the challenge data set available by late summer, we hope that many students will be able to use the challenge data set for their graduate class projects in the Fall semester.

Important dates:

Submission site: https://msr2026-miningchallenge.hotcrp.com/

Deadline for proposals: August 20, 2025

Notification: August 28, 2025

Call for Challenge Papers Published: September 15, 2025