We absolutely welcome research with industry, as it often conveys important lessons about software engineering in practice. We also fully understand that industry data may be subject to confidentiality constraints or legal requirements.

If you cannot share data, please state the reason in both the submission form and the paper. A typical wording would be: “The raw data obtained in this study cannot be shared because of confidentiality agreements.”

Whenever possible, sharing a subset of the data (for example, the data used for figures and tables, anonymized data, or data aggregated over the entire dataset), as well as analysis procedures or scripts, is strongly encouraged.

We absolutely welcome user studies. At the same time, we recognize that sharing raw data may be constrained by privacy concerns.

If you cannot share data, please state the reason in both the submission form and the paper. A typical wording would be: “The raw data obtained in this study cannot be shared because of privacy issues.”

Whenever possible, sharing a subset of the data (for example, anonymized or aggregated data, or the data used for figures and tables), as well as analysis procedures or scripts, is encouraged.

Best practices for qualitative research emphasize providing detailed arguments and rationale for qualitative approaches, procedures, and analyses in order to support reliability and credibility.

Authors are advised to provide as much transparency as possible, including clear explanations of:

• the context of the study;
• the participant selection process and its theoretical basis;
• data collection procedures;
• data analysis methods, including theoretical justification and relation to the research questions.

Authors should explain how themes and concepts were identified from the data and provide sufficient evidence (for example, numbered quotations and labeled sources) to bridge the gap between interpretation and collected evidence.

Similar to replicability in quantitative research, transparency allows methods to be inspected and interpreted. However, replicability is not the goal of qualitative research, as such methods are inherently interpretive and context-dependent.

When qualitative data is quantified and used in quantitative analyses, authors should report techniques used to assess rigor (such as inter-rater reliability or triangulation), or justify how rigor was achieved if such techniques were not used.

Please see the corresponding question under Double-Anonymous Submissions below.

Many ICSE tracks employ double-anonymous review to improve fairness and reduce potential biases, following strong community support.

For further background and evidence, see Claire Le Goues’s blog post advocating double-anonymous review for software engineering conferences, as well as empirical studies by Moritz Beller and Alberto Bacchelli.

Authors must make every reasonable effort to preserve anonymity, but they are not required to make their identity undiscoverable. The goal is not an adversarial identity-discovery process.

The guiding principle is to maximize the number of people who could plausibly be the authors, without changing any technical content. Reviewers should be able to evaluate the paper without knowing who the authors are.

1. Omit all authors’ names from the title page.
2. Refer to your own work in the third person. Do not rename tools, systems, or approaches, as this would compromise the technical content.
3. Do not rely on non-anonymous supplementary material (e.g., personal websites, GitHub repositories, YouTube channels, or technical reports) during review or rebuttal.

Additional guidance on anonymization is available from ACM.

If the earlier version was published in a non-peer-reviewed venue (e.g., arXiv or a departmental technical report), it does not need to be cited.

If the earlier version was published in a peer-reviewed venue, it should be cited in the third person so as not to reveal shared authorship.

It is acceptable to publish work arising from a thesis or dissertation. During double-anonymous review, the dissertation does not need to be cited, as it does not compromise novelty.

In the camera-ready version, the dissertation should be cited to acknowledge its contribution.

If the unpublished work is an earlier version of the same paper and is under review elsewhere, it must not be cited, as this would constitute double submission.

Otherwise, unpublished work may be cited in the third person and made available online, provided anonymity is preserved.

Yes. You may discuss the work, give talks, or publish it publicly (e.g., on arXiv), but you must not state that the work is under submission to ICSE 2027.

Please make a reasonable effort to anonymize the data set or tool. If anonymization is not possible, include a clear warning that accessing the resource may reveal author identity.

Yes. To reduce institutional status bias, authors should anonymize company and institution names, for both industry and academic affiliations.