Research Software Group

MANAGING OPEN SOURCE PROJECTS ON GITHUB — SUCCESS FACTORS AND PERFORMANCE INDICATORS

As a part of my, Julia Seeger’s, MSc Dissertation in the School of Computer Science at the University of St Andrews I am looking for volunteers for an interview. This interview is a part of a research project focussed on success factors and performance indicators of managing open source projects hosted on GitHub.

I am looking for core contributors to open source projects hosted on GitHub. Ideally, the project should have configured and make use of Travis CI, and should have a history of pull requests before and after the configuration of Travis CI.

I would firstly be interested in your opinion about success factors and performance indicators that I have identified by analysing the public GitHub repository of your project with the help of the GitHub API. I will ask if, as a core contributor of the project, you would agree or disagree with my findings. Secondly, I am interested in your personal experience in managing a repository of an open source project on GitHub, and the factors and managing techniques you identified to be important for a successful project.

The interview will take place in a form of a video or an audio call via Skype for Business or Microsoft Teams. The interview will take place during July 2020, consists of 6 questions and will last around 25 minutes. If you agree to participate, questions will be given to you at least three days in advance.

If you are willing to participate, please get in touch using the contact details below. You will then be given a Participant Information Sheet that further details my research, and will have the opportunity to ask questions, before being asked whether you consent to participate.

Contact Details

Researcher: Julia Seeger
js433@st-andrews.ac.uk

Supervisor: Dr. Alexander Konovalov
alexander.konovalov@st-andrews.ac.uk

As a part of the Love Data Week, Alexander Konovalov will give a talk on Tuesday 11 February, 3pm, Physics Lecture Theatre C.

Abstract: Have you been frustrated by trying to use someone else’s code which is non-trivial to install? Have you tried to make supplementary code for your paper to be easily accessible for the reader? If so, you certainly know that this may require non-trivial efforts. I will demonstrate some tools that may help to create reproducible computational experiments, and will explain which skills will be needed to use these tools. The talk will demonstrate examples in Python and R runnable in Jupyter notebooks. You are welcome to bring your laptop to try these examples online. No prior knowledge of programming is required.

Links:

• Templates for reproducible experiments in GAP, Python and R
• Code4REF guidance on recording research software in Pure

References:

• The Laser Interferometer Gravitational-Wave Observatory (LIGO) demos
• Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks PLOS Computational Biology paper by Adam Rule, Amanda Birmingham, Cristal Zuniga, Ilkay Altintas, Shih-Cheng Huang, Rob Knight, Niema Moshiri, Mai H. Nguyen, Sara Brin Rosenthal, Fernando Pérez and Peter W. Rose
• Hidden REF announcement

RESCHEDULED: please note the changed date and a non-standard time!

Abstract: Prest is a recently published piece of open-source software for computational revealed preference analysis that provides novel ways to estimate decision makers’ preferences over choice alternatives by analysing their observable choice behaviour. This software is informed by classic as well as recent developments in economic revealed preference theory. Some of the recent developments take the form of models that are computationally complex. This complexity currently hinders the inclusion of these models in the Prest toolkit. The presentation will first aim to describe the primary ideas underpinning Prest and illustrate them with examples from its existing toolkit. It will then proceed with a discussion of some of the challenges pertaining to the expansion of that toolkit with more models and operations. The presentation will be self-contained and no prior background in economics will be necessary.

Speaker Bio: Georgios is a Reader in Economics at the University of St Andrews, working mainly on decision theory and revealed preference analysis. In the latter research programme, Georgios’ work aims to improve our understanding of people’s decision processes and preferences through theoretical, experimental/empirical as well as computational methods. Georgios co-developed the Prest software program for computational revealed preference analysis (https://prestsoftware.com/).