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Reproducibility in the computational sciences has been stymied because of the complex and rapidly changing computational environments in which modern research takes place. While many will espouse reproducibility as a value, the challenge of making it happen (both for themselves and testing the reproducibility of others work) often outweigh the benefits. There have been a few reproducibility solutions designed and implemented by the community. In particular, the authors are contributors to ReproZip, a tool to enable computational reproducibility by tracing and bundling together research in the environment in which it takes place (e.g. ones computer or server). In this white paper, we introduce a tool for unpacking ReproZip bundles in the cloud, ReproServer. ReproServer takes an uploaded ReproZip bundle (.rpz file) or a link to a ReproZip bundle, and users can then unpack them in the cloud via their browser, allowing them to reproduce colleagues work without having to install anything locally. This will help lower the barrier to reproducing others work, which will aid reviewers in verifying the claims made in papers and reusing previously published research.
Can we simplify explanations for software analytics? Maybe. Recent results show that systems often exhibit a keys effect; i.e. a few key features control the rest. Just to say the obvious, for systems controlled by a few keys, explanation and control
This paper investigates the reproducibility of computational science research and identifies key challenges facing the community today. It is the result of the First Summer School on Experimental Methodology in Computational Science Research (https:/
Motivation: Automatically testing changes to code is an essential feature of continuous integration. For open-source code, without licensed dependencies, a variety of continuous integration services exist. The COnstraint-Based Reconstruction and An
Machine learning (ML) has been widely used in the literature to automate software engineering tasks. However, ML outcomes may be sensitive to randomization in data sampling mechanisms and learning procedures. To understand whether and how researchers
Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domai