Which extreme-programming techniques are appropriate to use in a research environment?

Question

Which (if any) extreme-programming techniques would be appropriate to use in a research environment - where the goal is to produce prototypes, patentable, and/or publishable work?

Answer 1

• You have to be Agile to do research programming.

• You have to be willing to throw away a lot of prototypes.

• You have to be willing to think outside the box, so software patterns are not going to help you that much.

• I think you have to be willing to learn new languages, and even create some new ones.

Other than that, research programming is basically the same as any other. :) You still have to write unit tests. You still have to write documentation. And you still have a boss.

Your deadlines may be a bit more fluid.

Answer 2

Speaking from a background of algorithm research:

• Keep a long backlog of ideas
• Re-prioritize aggressively and frequently (e.g. every day)
  • Mark down backlog items that are no longer viable
• Maintain an up-to-date picture of inter-dependencies between backlog items
  • Unlike regular software development, there is a lot more dependencies in research work.
• Always measure, visualize and track algorithm performance (accuracy, etc)
• Don't work alone.
• Discuss, collaborate and share frequently.
• Keep a wiki, and spend lots of effort to extract "wisdom" from your work.
• Use version control. However, keep good algorithm candidates in the current system, even if they are not actively used.
  • It allows you to tinker with an older algorithm at the spur of the moment.
• Stale performance data could be error-prone.
  • For example, the old data may be based on a less accurate metric
  • To get fresh performance data, re-run the algorithm(s).
• Prefer dynamic typing and flexibility.
• Use the right language.
  • If almost all successful researchers in the field use one particular language, then use it. Don't fight the wisdom of the crowd.
    • Instead, find ways to integrate smaller components into that language, if the smaller components can be developed in a language suitable for computation such as C/C++, or if existing open source code is available.
• Ask fellow researchers for their source code.
  • Many researcher are actually quite friendly to such requests with proper credits and data sharing.
  • This will save a lot of trouble because their published papers will only cover the high level picture, yet the devil is in the details.
• Always push yourself, but don't timebox.
  • Timebox don't work because of unpredictability in research work.

An example of how to use backlog in research: Suppose in the beginning there are items A, B, C, ..., X, Y, Z.

• A
• B
• C
• ...

Over time, you worked on a number of items, and you have a sense of how promising each item is, not just the items you have worked but also those you don't. The updated backlog becomes:

• A (promising: 90, progress: 70% done)
• B (promising: 70, progress: 60% done)
• Z (promising: 65, not started)
• ...
• C (seems it won't work, don't bother)

Notice how item C sinked to the bottom because of research insights gained from working on A and B. Also notice how Z floats to the top. Learning about what other researchers are doing will also help floating items to the top.

At the end of one semester, do a backlog cleanup.

• A (done, working)
• B (done, working)
• Z (done, some bugs)
• -----
• Y (50% coded, kept in the system, not actively used)
• X (10% coded, removed from the system in revision 123)
• -----
• C (dropped)

The ones that are working will be the result you publish.

Answer 3

XP/Agile is compatible with the scientific method (observe - theorize - experiment)