which feature location technique is better
Download
Skip this Video
Download Presentation
Which Feature Location Technique is Better?

Loading in 2 Seconds...

play fullscreen
1 / 9

Which Feature Location Technique is Better? - PowerPoint PPT Presentation


  • 107 Views
  • Uploaded on

Which Feature Location Technique is Better?. Emily Hill , Alberto Bacchelli , Dave Binkley, Bogdan Dit , Dawn Lawrie , Rocco Oliveto. Motivation: Differentiating FLTs. Precision = 0.20. Precision = 0.20. Totally unrelated. In vicinity. Example. Developer works down ranked list

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Which Feature Location Technique is Better?' - justis


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
which feature location technique is better

Which Feature Location Technique is Better?

Emily Hill, Alberto Bacchelli, Dave Binkley, BogdanDit, Dawn Lawrie, Rocco Oliveto

motivation differentiating flts
Motivation: Differentiating FLTs

Precision = 0.20

Precision = 0.20

Totally unrelated

In vicinity

example
Example
  • Developer works down ranked list
  • At each item can explore or not
  • When exploring structure, can bail at any time
proposed approach rank topology
Proposed Approach: Rank Topology
  • Use evaluation measures that consider the likelihood of a developer finding fix locations
  • Use textual information to approximate developer’s interest (i.e., likelihood) of following “trail” in structural topology, starting from ranked list
  • Rank topology = inverse of the number of hops in topology
example1
Example
  • 3rd rank result + 4 structural hops = 7 total hops
  • Rank topology metric = 1 / 7
  • Developer works down ranked list
  • At each item can explore or not
how smart is the user
How “smart” is the user?
  • Omniscient: makes no wrong choices, exploring only those ranks and structural hops that lead to a bug
  • No discrimination: explores everything
  • Semi-intelligent: only follows a structural hop if the next method exhibits textual clues
    • Rank topology uses VSM cosine similarity (tf-idf)
    • Structural edge added if both methods > median scores for query
    • Supported by user studies of information foraging theory [Lawrance, et al TSE 2013]
preliminary study distinguish qlm from random
Preliminary Study: Distinguish QLM from Random

Ranked list of results all have same bug fixes at exactly the same ranks

conclusion
Conclusion
  • Rank topology differentiates between randomly ordered lists and a state of the art IR technique (QLM) with relevant results at the exact same ranks
  • Future work
    • How well does rank topology mimic developer behavior in practice?
    • How closely can/should we model user behavior?
  • Our question: Does the research community need to revise how we evaluate FLTs?
preliminary study
Preliminary Study
  • Effect of program structure on the rank topology metric for each JabRef bug used in the case study.
ad