Open Science Policy - ESEIW 2026

Openness in science is key to fostering progress via transparency, reproducibility, and replicability. After more than a decade of open science trials, pioneered by ESEM, that culminated in the SIGSOFT open science policies, ESEM 2026 is making the next step in pushing the boundaries of openness. ESEM 2026 is open by default.

Open access: The proceedings will be fully open access, meaning that there is no expectation that authors self-archive pre- and postprints anymore–but they are free to do so, if they choose to. Potential benefits of publishing a self-archived version include making the paper publicly available earlier or benefiting from the announcement mailing list of arXiv.

Open data: Authors must disclose their data openly and describe how to access it in the paper itself. Alternatively, authors must have reasonable justification for not sharing artifacts/data and state it in the paper itself. This is enabled in a mandatory, distinct section titled “Data Availability”.

Data availability section

The “Data Availability” section is mandatory and is counted in the additional pages for references in relation to the page limits. The absence of this section may constitute grounds for desk rejection.

The Data Availability section must describe the availability of the research artifacts (data, code, analysis scripts/notebooks, qualitative protocols/coding schemas and transcripts, etc.) associated with the paper. In case authors cannot disclose the data, such as due to non-disclosure agreements or other legal or ethical restrictions, authors must instead provide a reasonable justification. The open data should also include information on the data-processing steps/pipelines and documentation (README) describing how to run the analysis. Authors must describe their artifacts under one of the following conditions:

Open Artifacts: A permanent, distinct identifier (e.g., DOI from Zenodo or Figshare) or a stable repository link must be provided.
Partially open artifacts: If some of the data cannot be shared at all or only in parts, e.g., due to Non-Disclosure Agreements (NDA) or industrial privacy/confidentiality restrictions, authors must explicitly state this and provide a brief justification. We encourage authors to think about which partial artifacts they are allowed to share and to do so.

To submit your tools, data, and code while still following the double-anonymous review process, please refer to these guidelines. To aid authors in understanding what the expectations regarding the contents of the section are, we provide exemplary positive and negative statements.

Examples of acceptable positive supporting statements

Example 1: Full disclosure, quantitative study

Data Availability: The dataset supporting this study, including the mining scripts, raw repository data, and analysis notebooks, is openly available on Zenodo at https://doi.org/10.5281/zenodo.XXXXXXX under a CC-BY 4.0 license. The replication package contains all materials necessary to reproduce our findings.

Example 2: Partial disclosure, interview study with shareable artifacts

Data Availability: Due to participant confidentiality agreements, interview transcripts cannot be shared publicly. However, the following materials are available on Zenodo at https://doi.org/10.5281/zenodo.XXXXXXX: (1) the interview protocol with all questions, (2) the complete coding schema with code definitions and examples, (3) anonymized illustrative quotes organized by theme, and (4) the inter-rater reliability calculations. Researchers seeking access to redacted transcripts for verification purposes may contact the corresponding author; access will be granted subject to ethics board approval and a data use agreement.

Example 3: Partial disclosure, industrial case study with mixed data

Data Availability: This study combines publicly available and proprietary data. The open-source repository analysis scripts and aggregated metrics are available at https://doi.org/10.5281/zenodo.XXXXXXX. The proprietary defect data from Company X were obtained under a research agreement that permits publication of aggregated findings but prohibits disclosure of raw data. We provide: (1) the data collection instruments, (2) the statistical analysis scripts (which can be applied to similar datasets), and (3) summary statistics sufficient for meta-analysis inclusion.

Examples of acceptable negative supporting statements

Example 1: Industry NDA

Data Availability: The data supporting this study were obtained under non-disclosure agreements with three enterprise software companies. These agreements prohibit sharing of source code, internal documents, meeting recordings, and derived artifacts. We have provided detailed methodological descriptions in Section 3 to support conceptual replication in other organizational contexts. The interview protocol is available in Appendix A. Researchers interested in conducting similar studies in industry settings may contact the corresponding author for guidance on negotiating research access agreements.

Example 2: Participant privacy, sensitive population

Data Availability: This study involved interviews with software developers discussing workplace conflicts, mental health challenges, and multiple problems of private matters. Due to the sensitive nature of disclosures and the small, identifiable population of senior engineers at the studied companies, we determined that even heavily redacted transcripts pose unacceptable re-identification risks. Following consultation with our ethics board, we do not share the raw data. We provide: (1) the complete study protocol, (2) the theoretical framework and coding schema, and (3) detailed descriptions of findings with carefully anonymized composite vignettes.

Example 3: Proprietary tooling and metrics, commercial sensitivity

Data Availability: This study analyzed code quality metrics and developer productivity data from a proprietary development environment at Company Y. The raw data contain trade secrets regarding internal tooling, performance benchmarks, and staffing levels that the company considers commercially sensitive. Data sharing was explicitly excluded from our research agreement. We report aggregated statistics and effect sizes to enable comparison with future studies. The survey instrument (Appendix B) and our analysis methodology (Section 4) are fully documented to support replication with similar data from other organizations.