Various fields of science face a reproducibility crisis. For quantum software engineering as an emerging field, it is therefore imminent to focus on proper reproducibility engineering from the start. Yet the provision of reproduction packages is almost universally lacking. Actionable advice on how to build such packages is rare, particularly unfortunate in a field with many contributions from researchers with backgrounds outside computer science. In this article, we argue how to rectify this deficiency by proposing a 1-2-3~approach to reproducibility engineering for quantum software experiments: Using a meta-generation mechanism, we generate DOI-safe, long-term functioning and dependency-free reproduction packages. They are designed to satisfy the requirements of professional and learned societies solely on the basis of project-specific research artefacts (source code, measurement and configuration data), and require little temporal investment by researchers. Our scheme ascertains long-term traceability even when the quantum processor itself is no longer accessible. By drastically lowering the technical bar, we foster the proliferation of reproduction packages in quantum software experiments and ease the inclusion of non-CS researchers entering the field.
With the advent of open source software, a veritable treasure trove of previously proprietary software development data was made available. This opened the field of empirical software engineering research to anyone in academia. Data that is mined from software projects, however, requires extensive processing and needs to be handled with utmost care to ensure valid conclusions. Since the software development practices and tools have changed over two decades, we aim to understand the state-of-the-art research workflows and to highlight potential challenges. We employ a systematic literature revi…
Over the recent years the importance of numerical experiments has gradually been more recognized. Nonetheless, sufficient documentation of how computational results have been obtained is often not available. Especially in the scientific computing and applied mathematics domain this is crucial, since numerical experiments are usually employed to verify the proposed hypothesis in a publication. This work aims to propose standards and best practices for the setup and publication of numerical experiments. Naturally, this amounts to a guideline for development, maintenance, and publication of numer…
Reproducibility and comparability of empirical results are at the core tenet of the scientific method in any scientific field. To ease reproducibility of empirical studies, several benchmarks in software engineering research, such as Defects4J, have been developed and widely used. For quantum software engineering research, however, no benchmark has been established yet. In this position paper, we propose a new benchmark -- named QBugs -- which will provide experimental subjects and an experimental infrastructure to ease the evaluation of new research and the reproducibility of previously publi…
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 Analysis (COBRA) Toolbox is a suite of open-source code for computational modelling with dependencies on licensed software. A novel automated framework of continuous integration in a semi-licensed environment is required for the development of the COBRA Toolbox and related tools of the COBRA community. Results: ARTENOLIS is a general-purpose infrastruc…
The authors have uploaded their artifact to Zenodo, which ensures a long-term retention of the artifact. The artifact allows to re-run the experiments very smoothly, and the dependencies are well documented. The process to regenerate data for the figures and tables in the paper completes, and all results are reproducible. This paper can thus receive the Artifacts Available badge. The software in the artifact runs correctly with no trouble, and is relevant to the paper, thus deserving the Artifacts Evaluated -- Functional badge. Given the successful reproduction of all figures and tables, the R…
Moscow Institute of Physics and Technology（莫斯科物理技术学院）
In this paper we explore the challenges of automating experiments in data science. We propose an extensible experiment model as a foundation for integration of different open source tools for running research experiments. We implement our approach in a prototype open source MLDev software package and evaluate it in a series of experiments yielding promising results. Comparison with other state-of-the-art tools signifies novelty of our approach.
ReproScore: Separating Readiness from Outcome in Research Software Reproducibility Assessment
Chemnitz University of Technology（开姆尼茨工业大学）；FIZ Karlsruhe — Leibniz Institute for Information Infrastructure（卡尔斯鲁厄FIZ——莱布尼茨信息基础设施研究所）；Friedrich Schiller University, Jena（耶拿弗里德里希·席勒大学）
Repeatability, Reproducibility, Replicability, Reusability (4R) in Journals' Policies and Software/Data Management in Scientific Publications: A Survey, Discussion, and Perspectives
Research processes often rely on high-performance computing (HPC), but HPC is often seen as antithetical to "reproducibility": one would have to choose between software that achieves high performance, and software that can be deployed in a reproducible fashion. However, by giving up on reproducibility we would give up on verifiability, a foundation of the scientific process. How can we conciliate performance and reproducibility? This article looks at two performance-critical aspects in HPC: message passing (MPI) and CPU micro-architecture tuning. Engineering work that has gone into performance…
Ascertaining reproducibility of scientific experiments is receiving increased attention across disciplines. We argue that the necessary skills are important beyond pure scientific utility, and that they should be taught as part of software engineering (SWE) education. They serve a dual purpose: Apart from acquiring the coveted badges assigned to reproducible research, reproducibility engineering is a lifetime skill for a professional industrial career in computer science. SWE curricula seem an ideal fit for conveying such capabilities, yet they require some extensions, especially given that ev…
The evidence-based approach has increasingly been employed to synthesize empirical findings from the primary research in software engineering. Nevertheless, the reproducibility of evidence-based software engineering (EBSE) studies seems to be underemphasized. In our investigation into the automatic search of 311 sample studies, more than 50% of the search strings are not reusable; about 87.5% of the search activities (e.g., search field settings) are unrepeatable; and more than 95% of the whole automatic search implementations are unreproducible. Considering that searching is a cornerstone of …
Recent developments in the commercial open source community have catalysed the use of Linux containers for scalable deployment of web-based applications to the cloud. Scientific software can be containerized with dependencies, configuration files, post-processing tools and even simulation results, referred to as containerized computing. This new approach promises to significantly improve sustainability, productivity and reproducibility. We present our experiences, technology, and future plans for open source containerization of software used to model particle and radiation beams. Vagrant is ce…
Software developers attempt to reproduce software bugs to understand their erroneous behaviours and to fix them. Unfortunately, they often fail to reproduce (or fix) them, which leads to faulty, unreliable software systems. However, to date, only a little research has been done to better understand what makes the software bugs non-reproducible. In this paper, we conduct a multimodal study to better understand the non-reproducibility of software bugs. First, we perform an empirical study using 576 non-reproducible bug reports from two popular software systems (Firefox, Eclipse) and identify 11 …
How many times have you tried to re-implement a past CAV tool paper, and failed? Reliably reproducing published scientific discoveries has been acknowledged as a barrier to scientific progress for some time but there remains only a small subset of software available to support the specific needs of the research community (i.e. beyond generic tools such as source code repositories). In this paper we propose an infrastructure for enabling reproducibility in our community, by automating the build, unit testing and benchmarking of research software.
University of Wuppertal（伍珀塔尔大学）；FIZ Karlsruhe - Leibniz Institute for Information Infrastructure（FIZ卡尔斯鲁厄——莱布尼茨信息基础设施研究所）；National Institute of Informatics（国立信息学研究所）
Small to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence is twofold. First, subsequent researchers must spend significant work hours building upon the proposed hypotheses or experimental framework. In the worst case, others cannot reproduce the experiment and reuse the findings for subsequent research. Second, suppose the ad-hoc research software fails during often long-running computationally expensive experiments…
Many researchers have criticized the field of Software Complexity metrics for the lack of testing, verification, and reproducibility of many metrics and case studies that utilized those metrics. This document describes SMF, a tool that can help address some of these concerns, namely by enabling verification of metrics, reproducibility of experiments, and ease of implementation for new metrics. The tool in question is the Software Metric Framework; an extensible set of scripts, tools, and standards that allow others to implement metrics in a way that allows automated data collection and analysi…
Containers as the unit of application delivery are the 'next big thing' in the software development world. They enable developers to create an executable image containing an application bundled with all its dependencies which a user can run inside a controlled environment with virtualized resources. Complex workflows for business-critical applications and research environments require a high degree of reproducibility which can be accomplished using uniquely identified images as units of computation. It will be shown in this thesis that the most widely used approaches to create an image from pr…
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 in SE address these threats, we surveyed 45 recent papers related to three predictive tasks: defect prediction (DP), predictive mutation testing (PMT), and code smell detection (CSD). We found that less than 50% of the surveyed papers address the threats related to randomized data sampling (via multiple repetitions); only 8% of the papers address …
Is a scientific research paper based on (i) public, online observational data files and (ii) providing free-licensed software for reproducing its results easy to reproduce by the same author a decade later? This paper attempts to reproduce a cosmic topology observational paper published in 2008 and satisfying both criteria (i) and (ii). The reproduction steps are defined formally in a free-licensed git repository package "0807.4260" and qualitatively in the current paper. It was found that the effort in upgrading the Fortran 77 code at the heart of the software, interfaced with a C front end, …
Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studies
fortiss and Blekinge Institute of Technology（fortiss 和 莱比锡技术学院）；University of the Bundeswehr Munich（联邦国防军大学穆尔海姆分校）；Blekinge Institute of Technology（莱比锡技术学院）；Technical University of Munich（慕尼黑技术大学）
On the Replicability and Reproducibility of Deep Learning in Software Engineering
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；PengCheng Laboratory（鹏城实验室）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Monash University（莫纳什大学）；Singapore Management University（新加坡管理大学）
In computational science and in computer science, research software is a central asset for research. Computational science is the application of computer science and software engineering principles to solving scientific problems, whereas computer science is the study of computer hardware and software design. The Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research software has to be reusable for advancing science. Thus, we need proper research software engineering for obtaining reusable and sustainable research software. This way, so…
We introduce Repro, an open-source library which aims at improving the reproducibility and usability of research code. The library provides a lightweight Python API for running software released by researchers within Docker containers which contain the exact required runtime configuration and dependencies for the code. Because the environment setup for each package is handled by Docker, users do not have to do any configuration themselves. Once Repro is installed, users can run the code for the 30+ papers currently supported by the library. We hope researchers see the value provided to others …
QUT Centre for Robotics, Queensland University of Technology（昆士兰科技大学机器人中心）；George St, Brisbane, QLD 4001, Australia（澳大利亚布里斯班 QLD 4001 地址）；School of Natural Sciences, Macquarie University（麦考瑞大学自然科学学院）；Fondazione Istituto Italiano Di Tecnologia (Italian Institute of Technology), Genova, Italy（意大利技术研究院（意大利理工学院））；Smart Robotics（智能机器人技术）
The reproducibility crisis in scientific computing constrains robotics research. Existing studies reveal that up to 70% of robotics algorithms cannot be reproduced by independent teams, while many others fail to reach deployment because creating shareable software environments remains prohibitively complex. These challenges stem from fragmented, multi-language, and hardware-software toolchains that lead to dependency hell. We present Pixi, a unified package-management framework that addresses these issues by capturing exact dependency states in project-level lockfiles, ensuring bit-for-bit rep…
We report on the state-of-the-art of software visualization. To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013-2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and m…
Context: Software Engineering research makes use of collections of software artifacts (corpora) to derive empirical evidence from. Goal: To improve quality and reproducibility of research, we need to understand the characteristics of used corpora. Method: For that, we perform a literature survey using grounded theory. We analyze the latest proceedings of seven relevant conferences. Results: While almost all papers use corpora of some kind with the common case of collections of source code of open-source Java projects, there are no frequently used projects or corpora across all the papers. For …
The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and integrating them into the ML-based system safely. Although most of these tools use bugs' lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses. In this study, we firstly investigate the reproducibility and verifiability of the bugs in ML-based systems and show the most importan…
