Emerging Results, Vision and Reflection Papers TrackESEIW 2023

Call for Papers

The International Symposium on Empirical Software Engineering and Measurement (ESEM) emerging results, vision and reflection papers track features submissions that describe current work in progress from research or practice. Papers should clearly state the longer-term objectives and outline a plan for working towards those objectives.

Emerging Results papers should communicate initial research results for which there is not yet a complete evaluation. The primary purpose of such papers is the communication of new ideas to obtain early feedback from the empirical software engineering community.

The track also welcomes Vision papers, which concern long-term challenges and opportunities in empirical software engineering research and practice that are outside of current mainstream topics. The track further welcomes Reflection papers, which focus on studies published in a partnered journal (TSE, IST, EMSE, JSS, TOSEM) from between 3 and 10 years ago (i.e., 2013-2020), with the intent of discussing their current impact and implications.

Please note:

• Make sure the paper follows the standard IEEE template in conference mode (see https://www.ieee.org/conferences/publishing/templates.html).
• Make sure your paper follows the double-blind instructions and does not reveal the authors’ identities.

General Scope of Submissions

Submissions should not be under consideration for publication or presentation elsewhere. In addition to the specific scope of this track, submissions may address any aspect of software engineering but must tackle the problem from an empirical perspective and using a rigorous empirical method, including:

• Empirical studies using qualitative, quantitative, and mixed methods
• Cross- and multi-disciplinary methods and studies
• Formal experiments and quasi-experiments
• Case studies, action research, ethnography and field studies
• Survey research
• Simulation studies
• Artifact studies
• Data mining using statistical and machine learning approaches
• Secondary and tertiary studies including
  • Systematic literature reviews and rapid reviews, that include a strong synthesis part
  • Meta-analyses, and qualitative, quantitative or structured syntheses of studies
• Replication of empirical studies and families of studies

Topics commonly addressed using an empirical approach include, but are not limited to:

• Evaluation and comparison of software models, tools, techniques, and practices
• Modeling, measuring, and assessing product or process quality and productivity
• Continuous software engineering
• Software verification and validation, including analysis and testing
• Engineering of software systems which include machine learning components and data dependencies
• Applications of software engineering to different types of systems and domains (e.g. IoT, Industry 4.0, Context-awareness systems, Cyber-physical systems)
• Human factors, teamwork, and behavioral aspects of software engineering

We welcome submissions on these research meta-topics:

• Development, evaluation, and comparison of empirical approaches and methods
• Infrastructure for conducting empirical studies
• Techniques and tools for supporting empirical studies
• Empirically-based decision making

We also welcome submissions that:

• Demonstrate multi-disciplinary work,
• Transfer and apply empirical methods from other disciplines,
• Replication studies, and
• Studies with negative findings.
  

Important Dates AoE (UTC-12h)

(All dates are end of the day, anywhere on earth)

Abstract: April 24, 2023
Submission: May 2, 2023
Notification: June 16, 2023
Camera-ready: July 7, 2023

Submission Link: Easychair

How to Submit

Submissions to this track are limited to 6 pages plus one page with references and must be submitted through EasyChair by selecting the track “Emerging Results, Vision and Reflection Papers”. All submissions must be written in English and must be submitted via EasyChair https://easychair.org/my/conference?conf=esem2023 in the PDF format, and they must be formatted according to the standard IEEE template in conference mode (which can be found at https://www.ieee.org/conferences/publishing/templates.html or in Overleaf at https://www.overleaf.com/gallery/tagged/ieee-official. The submission must also comply with the IEEE ethics guidelines IEEE ethics guidelines. In particular, it must not have been published elsewhere and must not be under review elsewhere while under review for ESEM.

The ESEM 2023 Emerging Results, Vision and Reflection Papers track will employ a double-blind review process. Thus, submissions may not reveal its authors’ identities. The authors must make an acceptable effort to honor the double-blind 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. More details on author ethics and peer review can be found at https://conferences.ieeeauthorcenter.ieee.org/.

All submissions will be peer-reviewed by at least three experts from the international program committee of the track and will receive an additional meta-review. Any papers that are outside the scope of the symposium, exceed the maximum number of pages for the respective category, or do not follow the formatting guidelines will be desk rejected without review. The PC members’ bidding information may be used to assess what is considered out of scope.

Finally, please note that each accepted contribution must have a minimum of one author registered by the deadline for the camera-ready submission for their respective paper type. Also, each paper must be presented by one of the authors. Failure to meet these criteria will result in the paper’s removal from the proceedings.

Open Science Policy

Openness in science is key to fostering progress via transparency, reproducibility and replicability. While all submissions will undergo the same review process independent of whether or not they disclose their tools, data, and code, we expect authors to include a data availability statement in their submissions that either provides links to the open data / replication package or that explains that why data cannot be disclosed (e.g. due to the sensitivity of the data or due to existing non-disclosure agreements).

Authors who can make their data available are strongly encouraged to do so already upon submission (either privately or publicly), but they can also disclose it upon acceptance (publicly).

• We expect authors to include a data availability statement in the submission (for instance, at the end of the introduction) explaining whether and where the data and related material is available and under which conditions the data/material can be accessed. If the authors cannot disclose industrial or otherwise non-public data, they should provide an explicit (short) explanation in the statement.
• For submissions based on open data sources, the publication of any cleaned or filtered data is mandatory.
• Where reasonable, we ask authors to provide elaborate explanations on how to navigate the data source and how to use it. It must be explained how the provided data, code, and tools are used in the steps of the method described in the paper. This includes:
  • Study protocols, coding and transcription schemas, and further relevant information used in qualitative studies.
  • Information to the code (incl. version information) or the data input/output relevant to every step of data cleaning and labeling, feature engineering, model training and evaluation for quantitative analysis and/or machine learning studies.

Finally, we further ask the authors to follow the FAIR principles in open science when sharing their tools, data, and code, and recommend following the principles as outlined in the book chapter “Open Science in Software Engineering” https://link.springer.com/chapter/10.1007/978-3-030-32489-6_17.

Track Co-Chairs

Silvia Abrahão, Universitat Politècnica de València, Spain
Sebastian Baltes, SAP SE, Germany, and University of Adelaide, Australia