ENHANCING REPRODUCIBILITY AND EFFICIENCY IN DATA-DRIVEN RESEARCH USING THE MISE-EN-PLACE AUTOMATION TOOL

ABSTRACT

Reproducibility is a persistent challenge in data-driven research, where complex computational environments often hinder consistent results. This study explores Mise-en-Place, an automation tool aimed at standardizing environment configurations. Experiments with multi-language projects showed that Mise-en-Place reduced setup time by up to 77% and eliminated version mismatch errors. The results highlight its potential to streamline workflows, minimize configuration errors, and enhance the reproducibility of scientific experiments.

АННОТАЦИЯ

Воспроизводимость данных является постоянной проблемой в исследованиях, основанных на данных, где сложные вычислительные среды часто препятствуют получению согласованных результатов. В данном исследовании рассматривается Mise-en-Place, инструмент автоматизации, направленный на стандартизацию конфигураций среды. Эксперименты с мультиязычными проектами показали, что Mise-en-Place сокращает время настройки на 77 % и устраняет ошибки несоответствия версий. Полученные результаты подчеркивают его потенциал для оптимизации рабочих процессов, минимизации ошибок конфигурации и повышения воспроизводимости научных экспериментов.

Keywords: reproducibility, environment management, automation, multi-language projects, Mise-en-Place, mise, Infrastructure as Code

Ключевые слова: воспроизводимость, управление средой, автоматизация, мульти-языковые проекты, Mise-en-Place, mise, Infrastructure as Code

Introduction

Reproducibility remains a foundational principle in scientific research, yet it continues to pose significant challenges, particularly in computational fields. Disparate workflows, inconsistent environment configurations, and the absence of standardized tools contribute to these difficulties. Prior studies, such as Repeatability, Reproducibility, Replicability, Reusability (4R) in Computer Science [1] and Computational reproducibility of Jupyter notebooks from biomedical publications [2], underscore the importance of addressing these issues through standardized solutions. Furthermore, A Backend Platform for Supporting the Reproducibility of Computational Experiments [3] emphasizes the need for backend platforms to support consistent experiment replication.

In addition to technical interventions, recent work by Obadage et al. [4] explores the potential of using downstream citation contexts as signals of reproducibility. By applying sentiment analysis to citation texts from Machine Learning Reproducibility Challenges, their study offers a novel, community-driven perspective on the reproducibility landscape, highlighting the critical importance of addressing reproducibility issues in the field. This approach complements traditional technical solutions, underscoring that reproducibility challenges are not only technical but also deeply embedded in the scientific communication process.

This study evaluates the effectiveness of Mise-en-Place, a tool designed to simplify and automate environment configurations in multi-language projects [5]. We hypothesize that Mise-en-Place reduces setup complexity and time, while also minimizing version mismatches and dependency-related errors.

Materials and Methods

The evaluation involved 30 participants, comprising senior-year students and faculty members from technical disciplines. They worked on three project types: a Python data analysis project using Pandas and Matplotlib, a Go microservice for data processing, and a multilingual project integrating Python and Go.

Each project was configured using two approaches. In the first approach, participants manually installed and configured environments for each language and dependency. In the second, Mise-en-Place automated the entire setup process using a single mise.toml configuration file. This automation aimed to reduce both the time required and the likelihood of errors in setting up complex environments.

Key metrics included setup time, measured from tool installation to the first successful experiment run, the number of errors related to version mismatches and dependencies, and the overall complexity of the setup process, assessed based on the number of languages and dependencies involved.

Results and Discussion

The results demonstrated a significant reduction in setup time across all projects. For the Python data analysis project, setup time decreased from 35 to 8 minutes, a reduction of 77%. The Go microservice project showed a 75% reduction, while the multilingual project saw a 70% decrease.

Moreover, Mise-en-Place eliminated all errors related to version mismatches and dependency issues. In the manual setup, participants encountered five language version errors and fifteen dependency-related problems. With Mise-en-Place, these errors were entirely absent, highlighting the tool's ability to ensure consistent and reproducible environments.

Table 1

Comparison of project setup times with and without Mise-en-Place

Table 2.

Number of configuration errors when configuring manually and using Mise-en-Place

Complexity and Scalability

Mise-en-Place also significantly reduced the complexity of managing multi-language projects. In manual setups, complexity grew exponentially as the number of languages and dependencies increased. In contrast, Mise-en-Place maintained a linear growth pattern, ensuring scalability through automation and standardization. This is particularly valuable in projects involving multiple programming languages, where managing compatibility between different environments can be challenging.

Figure 1. Complexity comparison between manual setup and Mise-en-Place

Comparative Analysis and Limitations

The tool's integration with modern CI/CD pipelines further enhances its utility, aligning with Infrastructure as Code (IaC) practices to standardize and automate environment setups [6]. Such integration ensures that research workflows are not only reproducible but also easily maintainable and scalable across different projects and teams. This approach resonates with insights from A Backend Platform for Supporting the Reproducibility of Computational Experiments [3], which emphasizes backend-driven reproducibility.

Compared to other environment management tools like MaPS, which relies on complex technical features such as Linux namespaces and Bubblewrap, Mise-en-Place offers a simpler, more accessible solution. While MaPS provides robust isolation capabilities, its complexity can be a barrier for researchers with limited technical expertise. Mise-en-Place, on the other hand, focuses on usability and ease of integration, making it suitable for a broader range of users.

However, Mise-en-Place is not without limitations. It requires users to familiarize themselves with the mise.toml configuration structure, which may present a learning curve for those new to such tools. Additionally, while it effectively handles common multi-language setups, it may lack support for niche applications or highly specialized environments.

Conclusion

Mise-en-Place has proven to be a transformative tool for improving reproducibility and efficiency in data-driven research. By significantly reducing setup time and minimizing configuration errors, it simplifies the management of complex multi-language projects. The tool's alignment with modern infrastructure management practices and its ability to integrate seamlessly into CI/CD pipelines make it a valuable asset for researchers seeking to enhance the reproducibility of their work.

Future research should focus on expanding Mise-en-Place's capabilities to address domain-specific challenges and refining its integration into diverse research workflows. Such advancements contribute not only to resolving the reproducibility crisis but also to fostering more collaborative and transparent scientific workflows.

References:

1. Hernández J. A.,  Colom M., Repeatability, Reproducibility, Replicability, Reusability (4R) in Journals' Policies and Software/Data Management in Scientific Publications: A Survey, Discussion, and Perspectives / arXiv 2312.11028  2023
2. ​​Sheeba Samuel, Daniel Mietchen, Computational reproducibility of Jupyter notebooks from biomedical publications / GigaScience, Volume 13, 2024
3. Lázaro Costa, Susana Barbosa, Jácome Cunha, A Backend Platform for Supporting the Reproducibility of Computational Experiments / arXiv 2308.00703  2023
4. Rochana R. Obadage, Sarah M. Rajtmajer, Jian Wu, Can citations tell us about a paper’s reproducibility? A case study of machine learning papers / In Proceedings of the 2nd ACM Conference on Reproducibility and Replicability (ACM REP '24). Association for Computing Machinery
5. Mise-en-place automation tool web site https://mise.jdx.dev/
6. Mikhelson O.Yu., Infrastructure as Code: review and application  / Actual researches №20 (150) 57-59 2023.