Wrap-up

Overview

Teaching: 15 min
Exercises: 0 min

Questions

• Looking back at what was covered and how different pieces fit together

• Where are some advanced topics and further reading available?

Objectives

• Put the course in context with future learning.

Summary

As part of this course we have looked at a core set of established, intermediate-level software development tools and best practices for ensuring that your software is correct and usable in real life. The course teaches a selected subset of skills that have been tried and tested in collaborative research software development environments, although not an all-encompassing set of every skill you might need (check some further reading). It will provide you with a solid basis for writing industry-grade code, which relies on the same best practices taught in this course.

Things like unit testing, CI and profiling play an important part of software development in large teams, but also have benefits in solo development. We’ve looked at the benefits of a well-considered development environment, using practices, tools and infrastructure to help us write code more effectively in collaboration with others.

We’ve looked at the importance of being able to verify the correctness of software and automation, and how we can leverage techniques and infrastructure to automate and scale tasks such as testing to save us time - but automation has a role beyond simply testing: what else can you automate that would save you even more time? Once found, we’ve also examined how to locate faults in our software.

Reflection Exercise: Putting the Pieces Together

As a group, reflect on what aspects of your work can benefit from applying the techniques and tools you learned during this workshop. What would be the main issues preventing these workflow changes?

Solution

One way to think about these concepts is to make a list and try to organise them along two axes - ‘perceived usefulness of a concept’ versus ‘perceived difficulty or time needed to master a concept’, as shown in the table below (for the exercise, you can make your own copy of the template table for the purpose of this exercise). You then may think in which order you want to learn the skills and how much effort they require - e.g. start with those that are more useful but, for the time being, hold off those that are not too useful to you and take loads of time to master. You will likely want to focus on the concepts in the top right corner of the table first, but investing time to master more difficult concepts may pay off in the long run by saving you time and effort and helping reduce technical debt.

Further Resources

Below are some additional resources to help you continue learning:

• Foundations of Astronomical Data Science Carpentries Workshop
• A previous InterPython workshop materials, covering collaborative usage of GitHub, Programming Paradigms, Software Architecture and many more
• CodeRefinery courses on FAIR (Findable, Accessible, Interoperable, and Reusable) software practices
• Python documentation
• GitHub Actions documentation

Key Points

• Collaborative techniques and tools play an important part of research software development in teams.