DuckyThesisProposalNotes < Main

---+ Ducky Thesis Proposal Notes
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---++ Problem Statement

I propose to study which techniques developers 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 via a user study that captures the questions that developers ask about the code and what IDE interactions they take to answer them.


---+++ 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?):
Robillard et al. showed:
   * 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   * BSD et al (unpublished)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
   1 protein-motif finding algorithm -- I will use a modified protein motif-finding algorithm to search for common patterns, and judgement to select interesting ones.
   1 data-visualization and mining tools, e.g. YALE -- I will use data mining and visualization tools to search through the patterns.
   1 (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?
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Topic revision: r18 - 2007-03-02 - TWikiGuest