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How can we improve the reproducibility of computer science research?

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Reproducibility is the ability to obtain the same results from a scientific experiment or analysis using the same data and methods. It is essential for validating and building on computer science research and publications. However, many studies have shown that reproducibility is often low or lacking in this field. In this article, we will explore some of the challenges and solutions for improving reproducibility in computer science.

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Ya Xu

Building GenAI at Google DeepMind

1 Define your research question and scope

The first step to ensure reproducibility is to clearly define your research question and scope. What problem are you trying to solve? What are your assumptions and hypotheses? What are the expected outcomes and implications? By specifying these aspects, you will help yourself and others to understand the purpose and context of your research. You will also avoid unnecessary or irrelevant experiments and analyses that may confuse or mislead your results.

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2 Document your data and methods

The next step is to document your data and methods in a transparent and detailed way. This means recording the sources, formats, preprocessing, and transformations of your data, as well as the tools, libraries, frameworks, and parameters of your methods. You should also provide any code, scripts, or workflows that you used to perform your experiments and analyses. By doing this, you will enable yourself and others to reproduce your data and methods exactly or with minimal modifications.

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Akanksha Srivastava

Product Manager @ LinkedIn | Building Feed at LinkedIn
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Quality research papers often include public ally accessible links to exact dataset, code repositories. This makes it easy for future researcher to validate and work on your research further. This leads to more citations and references which is always a win!

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Venkatesh Ramamoorthy, Ph.D

Computer and Network Security Researcher
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In my experience, if you are measuring performance of algorithms, it is extremely important to state the processor, RAM, and relevant hardware details. Note also that these items keep changing with time and the results can change, but the relative changes can be kept to as close as we possibly can. Also, arranging of data in memory to favour easy access (e.g. by ensuring word-boundary alignment) can significantly speed up performance. These items need to be explicitly stated in the methods section.

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3 Share your data and code

The third step is to share your data and code with the research community. This can be done by publishing them in public repositories, platforms, or journals that support data and code sharing. You should also provide appropriate licenses, metadata, and documentation for your data and code, so that others can access, use, and cite them properly. By sharing your data and code, you will increase the visibility and impact of your research, as well as foster collaboration and innovation.

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4 Use reproducible tools and environments

The fourth step is to use reproducible tools and environments for your research. This means choosing tools and environments that are stable, reliable, and compatible with your data and methods. You should also avoid using proprietary or closed-source software that may limit or prevent reproducibility. Some examples of reproducible tools and environments are open-source software, version control systems, containerization, virtualization, and cloud computing. By using these technologies, you will reduce the dependency and variability of your research on specific hardware or software configurations.

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Shu Wang

Software Engineer @ Google | ex-LinkedIn | Ph.D. in Computer Science
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Chameleon Cloud (https://www.chameleoncloud.org/) is an excellent platform for conducting reproducible experiments for academic purposes.

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5 Report your results and limitations

The final step is to report your results and limitations in a clear and honest way. This means presenting your results with appropriate statistics, graphs, tables, and figures that show the evidence and uncertainty of your findings. You should also acknowledge any limitations, assumptions, errors, or biases that may affect your results or their interpretation. By reporting your results and limitations, you will provide a realistic and comprehensive picture of your research, as well as identify areas for improvement or further investigation.

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Akanksha Srivastava

Product Manager @ LinkedIn | Building Feed at LinkedIn
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Agree, it is good to have a fellow colleague/researcher/supervisor verify your results by running through the exact steps. This would also help in catching any mistakes in your analysis that you may have overlooked.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ya Xu

Building GenAI at Google DeepMind
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It happens often that the data used in the research in its “raw” form cannot be shared publicly due to privacy concerns (among others). This would make it difficult to reproduce the results. This is one of the reasons why public benchmark datasets are very popular in AI research.

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How can we improve the reproducibility of computer science research?

1

2

3

4

5

6

1 Define your research question and scope

2 Document your data and methods

3 Share your data and code

4 Use reproducible tools and environments

5 Report your results and limitations

6 Here’s what else to consider

Computer Science

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How can we improve the reproducibility of computer science research?

1

2

3

4

5

6

1 Define your research question and scope

2 Document your data and methods

3 Share your data and code

4 Use reproducible tools and environments

5 Report your results and limitations

6 Here’s what else to consider

Computer Science

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