Mining ChallengeMSR 2024

Call for Mining Challenge Papers

Mining Challenge Presentation: https://github.com/NAIST-SE/DevGPT/files/12923358/2024.MSR.Challenge.pdf

Mining Challenge Video: https://youtu.be/0EhskEg7NxA?si=4DfrnxjT90mWnjVx

The emergence of large language models (LLMs) such as ChatGPT has disrupted the landscape of software development. Many studies are investigating the quality of responses generated by ChatGPT, the efficacy of various prompting techniques, and its comparative performance in programming contests, to name a few examples. Yet, we know very little about how ChatGPT is actually used by software developers.

This year, the mining challenge focuses on DevGPT, a curated dataset of developer-ChatGPT conversations that encompasses prompts with ChatGPT’s responses, including code snippets. This dataset is paired with corresponding software development artifacts, which range from source code, commits, issues, and pull requests to discussions and Hacker News threads. The purpose of DevGPT is to enable a comprehensive analysis of the context and implications of developer interactions with ChatGPT.

To create DevGPT, we leveraged a feature introduced by OpenAI in late May 2023, which allows users to share their interactions with ChatGPT through dedicated links. DevGPT is updated weekly by tracking mentions of ChatGPT sharing links on GitHub and Hacker News, starting from July 27, 2023. The snapshot 20230831 contains 2,891 shared ChatGPT links, sourced from 2,237 GitHub or Hacker News references.

Challenge

The challenge is open-ended: participants can choose the research questions that they find most interesting. Our suggestions include:

1. What types of issues (bugs, feature requests, theoretical questions, etc.) do developers most commonly present to ChatGPT?
2. Can we identify patterns in the prompts developers use when interacting with ChatGPT, and do these patterns correlate with the success of issue resolution?
3. What is the typical structure of conversations between developers and ChatGPT? How many turns does it take on average to reach a conclusion?
4. In instances where developers have incorporated the code provided by ChatGPT into their projects, to what extent do they modify this code prior to use, and what are the common types of modifications made?
5. How does the code generated by ChatGPT for a given query compare to code that could be found for the same query on the internet (e.g., on Stack Overflow)?
6. What types of quality issues (for example, as identified by linters) are common in the code generated by ChatGPT?
7. How accurately can we predict the length of a conversation with ChatGPT based on the initial prompt and context provided?
8. Can we reliably predict whether a developer’s issue will be resolved based on the initial conversation with ChatGPT?
9. If developers were to rerun their prompts with ChatGPT now and/or with different settings, would they obtain the same results?

Participants may combine the DevGPT data with mentions of links to ChatGPT shared on other platforms or websites. Participants are encouraged to “bring their own data” (BYOD) by integrating the DevGPT data with information from other public, readily available sources. We urge participants to thoroughly consider the ethical implications arising from using the DevGPT data in conjunction with other data sources. Sharing or using personally identifiable information is strictly prohibited.

How to Participate in the Challenge

First, familiarize yourself with the DevGPT infrastructure:

• The details about the DevGPT infrastructure are provided in our GitHub repositories.
• The dataset can be downloaded from either GitHub or Zenodo.

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 2024 in Lisbon, Portugal!

You can also join the DevGPT community, get support and find others to collaborate with. To do so:

• Join the live tutorial in September.
• Create new issues or discussions for problems, questions, or suggestions: https://github.com/NAIST-SE/DevGPT

Submission

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 DevGPT 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 2024]{MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories}{April 15–16, 2024}{Lisbon, Portugal}

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 2024.
• 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 2024. 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 2024 must not have been published elsewhere and must not be under review or submitted for review elsewhere whilst under consideration for MSR 2024. 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 2024 conference. All accepted contributions will be published in the electronic proceedings of the conference.

This year’s mining challenge and the data can be cited as:

@inproceedings{
title={DevGPT: Studying Developer-ChatGPT Conversations},
author={Xiao, Tao and Treude, Christoph and Hata, Hideaki and Matsumoto, Kenichi},
year={2024},
booktitle={Proceedings of the International Conference on Mining Software Repositories (MSR 2024)},
}

A preprint is available online.

Submission Site

Papers must be submitted through HotCRP: https://msr2024-challenge.hotcrp.com/

Important Dates

• Live tutorial and Kick-off session: September 2023
• Abstract Deadline: Dec 7, 2023
• Paper Deadline: Dec 11, 2023
• Author Notification: Jan 19, 2024
• Camera Ready Deadline: Jan 28, 2024

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 DevGPT 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.

Call for Mining Challenge Proposals

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Update: The MSR 24 Mining Challenge Paper is ‘‘DevGPT: Studying Developer-ChatGPT Conversations’’ by Tao Xiao, Christoph Treude, Hideaki Hata, and Kenichi Matsumoto!

DevGPT is a curated dataset which encompasses 16,129 prompts and ChatGPT’s responses including 9,785 code snippets, coupled with the corresponding software development artifacts—ranging from source code, commits, issues, pull requests, to discussions and Hacker News threads—to enable the analysis of the context and implications of these developer interactions with ChatGPT.

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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 2024 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 2024 organizing committee as Mining Challenge (co-)chair(s), who will manage the reviewing process (e.g., recruiting a Challenge PC, managing submissions and review 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 data set for consideration for the 2024 MSR Mining Challenge, please submit a short proposal (1-2 pages plus appendices, if needed) at https://msr-mc24.hotcrp.com/, 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 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).

Each submission must conform to the IEEE formatting instructions 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). For more information see here: https://www.ieee.org/conferences/publishing/templates.html

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 data set. This call will be published on the MSR website on September 1st. 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:

• Deadline for proposals: August 15, 2023
• Notification: August 24, 2023
• Call for Challenge Papers Published: September 1, 2023

Expected deadlines for Mining Challenge Papers:

• Live tutorial and Kick-off session: September 2023
• Abstract Deadline: Dec 7, 2023
• Paper Deadline: Dec 11, 2023
• Author Notification: Jan 19, 2024
• Camera Ready Deadline: Jan 28, 2024