Ducky Thesis Proposal Notes

Problem Statement

I propose to study which techniques use when developing code, how that varies from person to person, and how success at programming tasks correlates with choice of techniques. I will do so by replicating part or all of Robillard et al's work.

Problems:

• P1: We (software engineering researchers) do not know what different low-level techniques developers use when developing code using an IDE.
  • P1.1: We do not have a shared vocabulary for discussing different techniques that developers use when developing code with IDEs. (?)
• P2: We do not know which techniques are the most productive.
• P3: We do not know how to teach/train developers how to be more productive.
• P4: Coding video is expensive, annoying, and it is difficult to be consistent in coding (especially when one doesn't know what one is looking for)

Givens (right word?):

• G1: Different people use different techniques for locating relevant pieces of code.
• G2: Charactaristic interaction patterns reflecting those techniques can be discovered by analyzing coded transcripts of video of users navigating code.
• G3: Success at finding relevant pieces of code correlates with what technique(s) the developer uses.

Hypotheses:

• H1: These characteristic interaction patterns can be discovered by analyzing interaction telemetry of navigation tasks.
• H2: Software can recognize those patterns in navigation tasks.
• H3: Data mining software can discover interesting interaction patterns in navigation tasks.
• H4: Data mining software can discover interesting interaction patterns in more general code-development tasks.
• H5: Success in coding tasks correlates with which interaction patterns the developer uses.
• H6: Patterns are similar across tools.
• H7: Patterns are similar across languages.

I plan to do H1, H2, and H3. I hope to also do H4.

Literature Review

• Robillard et al
• Murphy/Kersten/Findlater
• BSD et al (unpublished)
• Jonathan Sillito's questions
• Andrew Ko ISCE05

Proposed data-gathering methods

I will use data collected by BSD which contains a replication of the first part of Robillard et al's study, where professional programmers search for specific interesting methods in the code.

For further work, I have access to

• from the Mylar bugzilla: many individual compressed logs of traces of a small number of developers either fixing one well-described bug or adding a well-defined feature, with the context
• from the glob: many individual logs of traces of a large number of developers working on unknown material, without the code available

We do not have data corresponding to the second part of Robillard et al's study, where a small number of professional programmers all do the same task. I might need to run a user study replicating that part.

Proposed analysis methods

I will use three techniques to analyze the data:

1. eyeballs (better word?) -- I will examine the data visually, with filters as appropriate to change how the data is visualized
2. protein-motif finding algorithm -- I will use a modified protein motif-finding algorithm to search for common patterns, and judgement to select interesting ones.
3. data-visualization and mining tools, e.g. YALE -- I will use data mining and visualization tools to search through the patterns.
4. (potentially, tho hopefully not) write my own algorithm and/or modify an existing algorithm

Having found patterns, I will write code to

• recognize those patterns
• (optionally?) to generate reports with
  • what patterns were found
  • description of the pattern distribution compared with the broader population's distribution
  • ?proscriptive advice on more effective techniques for coding?