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Ducky Thesis Proposal Notes
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- 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.
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- 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)
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Givens (right word?):
Robillard et al. showed: |
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- 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.
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- H6: Patterns are similar across tools.
- H7: Patterns are similar across languages.
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> | I plan to do H1, H2, and H3. I hope to also do H4. |
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Literature Review |
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< | @@@ A presentation of the relevant literature and the theoretical framework. |
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- Robillard et al
- Murphy/Kersten/Findlater
- BSD et al (unpublished)
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- Jonathan Sillito's questions
- Andrew Ko ISCE05
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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 |
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- many individual logs of traces of a small number of developers either fixing one well-described bug or adding a well-defined feature, with the code available
- many individual logs of traces of a large number of developers working on unknown material, without the code available
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- 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
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< | We do not have data corresponding to the second part of Robillard et al's study, where professional programmers attempt to add a feature. I might need to run a user study replicating that part. |
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> | 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. |
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Proposed analysis methods |
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- eyeballs (better word?) -- I will examine the data visually, with filters as appropriate to change how the data is visualized
- protein-motif finding algorithm -- I will use a modified protein motif-finding algorithm to search for common patterns, and judgement to select interesting ones.
- data-visualization and mining tools, e.g. YALE -- I will use data mining and visualization tools to search through the patterns.
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- (potentially, tho hopefully not) write my own algorithm and/or modify an existing algorithm
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< | Having found patterns, I will write code to recognize those patterns.
@@@ not sure what to put for what statistical tests I will use |
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> | 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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