Which feature location technique is better
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Which Feature Location Technique is Better? PowerPoint PPT Presentation

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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

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Which Feature Location Technique is Better?

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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



  • 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



  • 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



  • 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.

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