Accepted Papers

	Title
	A Case Study of Building Shared Understanding of Non-Functional Requirements in a Remote Software Organization Research Papers Laura Okpara, Colin Werner, Adam R Murray, Daniela Damian
	A Divide & Concur Approach to Collaborative Goal Modeling with Merge in Early-RE Research Papers Kathleen R. Hablutzel, Anisha Jain, Alicia M. Grubb Pre-print
	Automated Detection of Typed Links in Issue Trackers Research Papers Clara Marie Lüders, Tim Pietz, Walid Maalej
	Automated Question Answering for Improved Understanding of Compliance Requirements: A Multi-Document Study Research Papers Sallam Abualhaija, Chetan Arora, Amin Sleimi, Lionel Briand
	Automatic Terminology Extraction and Ranking for Feature Modeling Research Papers Jianzhang Zhang, Sisi Chen, Jinping Hua, Nan Niu, Chuang Liu
	CADE: The Missing Benchmark in Evaluating Dataset Requirements of AI-enabled Software Research Papers Mona Rahimi, Hamed Barzamini
	Enriching Vision Videos with Text: An Eye Tracking Study Research Papers Melanie Schmedes, Maike Ahrens, Lukas Nagel, Kurt Schneider
	Hierarchical Assessment of Safety Requirements for Configurations of Autonomous Driving Systems Research Papers Yixing Luo, Xiaoyi Zhang, Paolo Arcaini, Zhi Jin, Haiyan Zhao, Linjuan Zhang, Fuyuki Ishikawa
	Legal Accountability as Software Quality: A U.S. Data Processing Perspective Research Papers Travis Breaux, Thomas Norton
	Mining User Feedback For Software Engineering: Use Cases and Reference Architecture Research Papers Jacek Dąbrowski, Emmanuel Letier, Anna Perini, Angelo Susi
	Narratives: the Unforeseen Influencer of Privacy Concerns Research Papers Ze Shi (Zane) Li, Manish Sihag, Nowshin Nawar Arony, Joao Bezerra Junior, Thanh Phan, Neil Ernst, Daniela Damian
	Requirements on Explanations: A Quality Framework for Explainability Research Papers Larissa Chazette, Verena Klös, Florian Herzog, Kurt Schneider
	RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers Research Papers Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh Chawla, Jane Cleland-Huang
	The Benefits of Pre-Requirements Specification Traceability Research Papers Julia Mucha, Andreas Kaufmann, Dirk Riehle, Martin Jung
	The Implications of "Soft Requirements" Research Papers Alistair Sutcliffe, Peter Sawyer, Nelly Bencomo
	What's Inside a Cluster of Software User Feedback: A Study of Characterisation Methods Research Papers Peter Devine, James Tizard, Hechen Wang, Yun Sing Koh, Kelly Blincoe

Call for Papers

explore the complementary strengths of disciplines from social sciences and engineering fields: We examine the pivotal role RE plays in building bridges between disciplines. RE is a multidisciplinary effort, involving a large set of stakeholders with differing characteristics, strengths, and skill sets. The successful development of large and complex systems requires to routinely cross boundaries of disciplines - from psychology to economics, from physics to mechanical engineering, from life sciences to software engineering.

envision and elaborate novel, innovative ideas, techniques, and processes that integrate and move beyond discipline-specific approaches to address grand societal challenges such as sustainability, equality, and fairness: Software is no longer a purely technical device or product. Its enormous power and potential continue to shape society, affecting human values and our ways to live our lives. How can we integrate approaches from other disciplines and apply them to this and to ethical issues, such as sustainability and gender?

Categories for Research Papers:

The RE 2022 Research Track invites original submissions of research papers in three categories: Technical Solution, Scientific Evaluation, and Perspective.

Technical solution papers present solutions for requirements-related problems that are novel or significantly improve on existing solutions. This includes new algorithms or theory, novel tools, modeling languages, infrastructures, or other technologies. All requirements-related activities, such as elicitation, prioritization, or analysis are in scope. These papers are mainly evaluated with regard to problem significance, novelty in comparison with existing work, clarity of presentation, technical soundness, and evidence for its benefits.

Scientific evaluation papers evaluate existing problem situations or real-world artifacts, or they validate or refute proposed solutions by scientific means. This includes experiments, case studies, and surveys reporting qualitative and quantitative data and findings. The papers are mainly evaluated with regard to the soundness of research questions and appropriateness and correctness of study design, data analysis, and threats to validity. Replications are welcome. Lessons learned can be particularly important to complement other empirical results.

Perspective papers explore the history, successes, and challenges of requirements related practices and research agendas, and outline research roadmaps for the future. Literature reviews are also included in this category and must distill novel knowledge, present new insights, and not be a mere compilation. These papers are evaluated based on the insights they offer to the reader and the corresponding arguments, and on their potential to shape future research.

Review Criteria

Papers submitted to the RE 2022 Research Track will be evaluated based on the following criteria:

Soundness: The extent to which the paper’s contributions and/or innovations address its research questions and are supported by rigorous application of appropriate research methods

Significance: The extent to which the paper’s contributions can impact the field of requirements engineering, and under which assumptions (if any)

Novelty: The extent to which the paper's contributions are sufficiently original with respect to the state-of-the-art

Related Work: The extent to which the paper's contributions are appropriately compared to related work

Verifiability and Transparency: The extent to which the paper includes sufficient information to understand how an innovation works; to understand how data was obtained, analyzed, and interpreted; and how the paper supports independent verification or replication of the paper’s claimed contributions

Presentation: The extent to which the paper’s quality of writing meets the high standards of the RE conference series, including clear descriptions, as well as adequate use of the English language, absence of major ambiguity, clearly readable figures and tables, and adherence to the provided formatting instructions.

The RE 2022 Research Track follows a double-blind review process.

Reviewers will carefully consider all of these criteria during the review process, and authors should take great care in clearly addressing them. The authors should clearly explain the claimed contributions, and how they are sound, significant, novel, and verifiable, as described above.

Open Science Policy

The RE 2022 Research Track has an open science policy with the steering principle that all research results should be accessible to the public and, if possible, empirical studies should be reproducible. In particular, we actively support the adoption of open data and open source principles and encourage all contributing authors to disclose (anonymized and curated) data to increase reproducibility and replicability. Note that sharing research data is not mandatory for submission or acceptance. However, sharing is expected to be the default, and non-sharing needs to be justified. We recognize that reproducibility or replicability is not a goal in qualitative research and that, similar to industrial studies, qualitative studies often face challenges in sharing research data. For guidelines on how to report qualitative research to ensure the assessment of the reliability and credibility of research results, see the Q&A page.

Upon submission to the research track, authors are asked

to make their data available to the program committee (via upload of supplemental material or a link to an anonymous repository) – and provide instructions on how to access this data in the paper; or

to include in the paper an explanation as to why this is not possible or desirable; and

to indicate if they intend to make their data publicly available upon acceptance.

Supplementary material can be uploaded via the EasyChair site or anonymously linked from the paper submission. Although PC members are not required to look at this material, we strongly encourage authors to use supplementary material to provide access to anonymized data, whenever possible. Authors are asked to carefully review any supplementary material to ensure it conforms to the double-anonymous policy (described above). For example, code and data repositories may be exported to remove version control history, scrubbed of names in comments and metadata, and anonymously uploaded to a sharing site to support review. One resource that may be helpful in accomplishing this task is this blog post.

Artifacts

The authors of accepted papers will have the opportunity to increase the visibility of their artifacts (software and data) and to obtain an artifact badge. Upon acceptance, the authors can submit their artifacts, which will be evaluated by a committee that determines their sustained availability and reusability.

Instructions for the Double-Blind Review Process

Important Policy Announcements

Papers submitted to RE 2022 must be original. They will be reviewed under the assumption that they do not contain plagiarized material and have not been published nor submitted for review elsewhere while under consideration for RE 2022.

RE 2022 follows the IEEE policies for cases of double submission and plagiarism

The format of your paper must strictly adhere to the IEEEtran Proceedings Format.

LaTeX users: please use the LaTeX class file IEEEtran v1.8 and the following configuration (without option ‘compsoc’ or ‘compsocconf’):

\documentclass[conference]{IEEEtran}

Word users: please use this Word template (official IEEE Templates page for more information).

Please make sure that your submission

does not exceed the respective page limit specified in the track call

is in PDF format,

is in letter page size,

does not have page numbers,

has all fonts embedded in the PDF file,

uses only scalable font types (like Type 1, TrueType) --- bit-mapped font types (like Type 3) are not acceptable,

has all figures embedded in vector graphics (if not possible, use a high-resolution bitmap format of at least 300 dpi; do not use JPG, but a lossless format like PNG or GIF),

has all text in figures and tables large enough and readable when printed,

has a caption for every figure or table,

has the title and all headings properly capitalized

has no orphans and widows (cf. Section Help), and

does not use footnote references in the abstract.

Empirical Studies and Sharing of Data

I am doing research with industry. What if I cannot share data from my research? We absolutely welcome research with industry, as it often conveys important lessons about requirements engineering in practice – and we perfectly understand that industry data may be subject to confidentiality issues or legal requirements. If you cannot share data, please state the reason in 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". Having said that, even sharing a subset of your data (for instance, the data used for figures and tables in the paper, an anonymized subset, or one that aggregates over the entire dataset), analysis procedures, or scripts, would be useful.

I am doing user studies. What if I cannot share data from my empirical study? We absolutely welcome user studies! However, we also perfectly understand that sharing raw data can be subject to constraints such as privacy issues. If you cannot share data, please state the reason in 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". Having said that, even sharing a subset of your data (for instance, the data used for figures and tables in the paper, an anonymized subset, or one that aggregates over the entire dataset), analysis procedures, or scripts, would be useful.

I am doing qualitative research. What information should I include to help reviewers assess my research results and the readers use my results? Best practices for addressing the reliability and credibility of qualitative research suggest providing detailed arguments and rationale for qualitative approaches, procedures, and analyses. Therefore, authors are advised to provide as much transparency as possible into these details of their study. For example, clearly explain details and decisions such as 1) context of study, 2) the participant-selection process and the theoretical basis for selecting those participants, 3) collection of data or evidence from participants, and 4) data analysis methods, e.g., justify their choice theoretically and how they relate to the original research questions, and make explicit how the themes and concepts were identified from the data. Further, provide sufficient detail to bridge the gap between the interpretation of findings presented and the collected evidence by, for example, numbering quotations and labeling sources. Similar to replicability in quantitative research, transparency aims to ensure a study’s methods are available for inspection and interpretation. However, replicability or repeatability is not the goal, as qualitative methods are inherently interpretive and emphasize context. As a consequence, reporting qualitative research might require more space in the paper; authors should consider providing enough evidence for their claims while being mindful with the use of space.

Finally, when qualitative data is counted and used for quantitative methods, authors should report the technique and results in assessing rigour in data analysis procedures, such as inter-reliability tests or triangulation over different data sources or methods, and justify how they achieved rigour if no such methods were used.

I can make my data set / my tool available, but it may reveal my identity. What should I do? See this question under "double-anonymous submissions", below.

Double-Blind Submissions

I previously published an earlier version of this work in a venue that doesn’t have double-anonymous. What should I do about acknowledging that previous work? If the work you are submitting for review has previously been published in a peer-reviewed venue or in a non-peer-reviewed venue (e.g., arXiv.org, or a departmental technical report), then it should be cited but in the third person so that it is not revealed that the cited work and the submitted paper share one or more authors.

Our submission makes use of work from a PhD or master’s thesis, dissertation, or report which has been published. Citing the dissertation might compromise anonymity. What should we do? It is perfectly OK to publish work arising from a PhD or master’s degree, and there is no need to cite it in a submission to the RE Research Track because prior dissertation publication does not compromise novelty. In the final post-review, camera-ready version of the paper, please do cite the dissertation to acknowledge its contribution, but in any submission to the RE Research Track, please refrain from citing the dissertation to increase anonymity. You need not worry whether or not the dissertation has appeared. Your job is to ensure that your submission is readable and reviewable, without the reviewers needing to know the identities of the submission’s authors. You do not need to make it impossible for the reviewers to discover the authors’ identities. The referees will be trying hard not to discover the authors’ identity, so they will likely not be searching the web to check whether there is a dissertation related to this work.

What if we want to cite some unpublished work of our own (as motivation for example)? If the unpublished paper is an earlier version of the paper you want to submit to the RE Research Track and is currently under review, then you have to wait until your earlier version is through its review process before you can build on it with further submissions (this would be considered double-submission and violates plagiarism policies and procedures). Otherwise, if the unpublished work is not an earlier version of the proposed submission, then you should simply make it available on a website, for example, and cite it in the third person to preserve anonymity, as you are doing with other work.

Can I disseminate a non-anonymized version of my submitted work by discussing it with colleagues, giving talks, publishing it at ArXiV, etc.? You can discuss and present your work that is under submission at small meetings (e.g., job talks, visits to research labs, a Dagstuhl or Shonan meeting), but you should avoid broadly advertising it in a way that reaches the reviewers even if they are not searching for it. Therefore, the title of your submission must be different from preprints on ArXiV or similar sites. During review, you must not publicly use the submission title. Under these conditions, you are allowed to put your submission on your home page and present your work at small professional meetings.

What if we want to make available a tool, a data set, or some other resource, but it may reveal my identity? Please refer to the Open Science Policy in the Call for Papers with guidelines on how to anonymize such content. If that is impossible, place a warning next to the link that this may reveal your identity.

Thank you Daniela Damian and Andreas Zeller for sharing ICSE 2022 FAQs.