Call for papers, Special Issue for the RENE@SANER Track 2026

The Reproducibility Studies and Negative Results (RENE)@SANER SI would like to encourage researchers to (1) reproduce results from previous papers and (2) publish studies with important and relevant negative or null results (results which fail to show an effect yet demonstrate the research paths that did not pay off). We would also like to encourage the publication of the negative results or reproducible aspects of previously published work (in the spirit of journal-first submissions).

Reproducibility studies. The papers in this category must go beyond simply re-implementing an algorithm and/or re-running the artifacts provided by the original paper. Such submissions should at least apply the approach to new data sets (open-source or proprietary). Particularly, reproducibility studies are encouraged to target techniques that previously were evaluated only on proprietary or open-source systems. A reproducibility study should report on results that the authors could reproduce and on the aspects of the work that were irreproducible. We encourage reproducibility studies to follow the ACM guidelines on reproducibility (different team, different experimental setup): “The measurement can be obtained with stated precision by a different team, a different measuring system, in a different location on multiple trials. For computational experiments, this means that an independent group can obtain the same result using artifacts, which they develop completely independently.”

Negative results papers. We seek papers that report on negative results. We seek negative results for all types of software engineering research in any empirical area (qualitative, quantitative, case study, experiment, among others). For example, did your controlled experiment on the value of dual monitors in pair programming not show an improvement over a single monitor? Even if negative, results are still valuable when they are not obvious or disprove widely accepted wisdom. As Walter Tichy writes, “Negative results, if trustworthy, are extremely important for narrowing down the search space. They eliminate useless hypotheses and thus reorient and speed up the search for better approaches.”

The topics of the submissions should be of direct interest to the software analysis, evolution, and reengineering community (including researchers, practitioners, educators). Topics of interest include, but are not limited to:

  • AI for Software Engineering and Software Engineering for AI (see note below);
  • Generative AI and LLM applied to analysis, evolution and reengineering of software;
  • Software Analysis, Parsing, and Fact Extraction;
  • Software Maintenance and Evolution, Evolution Analysis;
  • Software Reverse Engineering and Reengineering;
  • Program Comprehension;
  • Software Architecture Recovery and Reverse Architecting;
  • Program Transformation and Refactoring;
  • Mining Software Repositories and Software Analytics;
  • Software Visualization;
  • Software Reconstruction and Migration;
  • Program Repair;
  • Software Release Engineering, Continuous Integration and Delivery;
  • Software Security, Safety, Reliability and Quality Analysis;
  • Software Tools for Software Evolution and Maintenance;
  • Human factors and legal aspects in the context of Software Analysis, Evolution and Reengineering
  • Empirical studies in the context of Software Analysis, Evolution and Reengineering;
  • Education and Training in the context of Software Analysis, Evolution and Reengineering;

Deadline

Submission deadline: 15 August 2026

How to Submit

Opening date for submissions: 15 April 2026

Please submit via Springer (link TBA).

Editors

  • Sebastian Proksch, S.Proksch@tudelft.nl
  • Georgia Kapitsaki,
  • Apostolos Ampatzoglou