XSnippet: Mining For Sample Code - PowerPoint PPT Presentation

About This Presentation
Title:

XSnippet: Mining For Sample Code

Description:

XSnippet: Mining For Sample Code Naiyana Tansalarak and Kajal Claypool Presented by: Shan Li CISC864 Topics Overall of Research Purposes Contributions Approaches ... – PowerPoint PPT presentation

Number of Views:105
Avg rating:3.0/5.0
Slides: 22
Provided by: IBM7196
Category:

less

Transcript and Presenter's Notes

Title: XSnippet: Mining For Sample Code


1
XSnippet Mining For Sample Code
  • Naiyana Tansalarak and Kajal Claypool
  • Presented by Shan Li
  • CISC864

2
Topics
  • Overall of Research
  • Purposes
  • Contributions
  • Approaches
  • Detail in Approaches

3
Overall of Research
  • Purposes
  • To provide sample codes for new developers to
    learn tech. quickly
  • Approaches
  • Mining sample codes from existing software
    systems

4
Overall of Research cont.
  • Steps in Approaches
  • Range of Queries generalized / specialized?
  • Ranking Heuristics for context-sensitive /
    context-independence
  • Such as constructor function / constructor
    function of DOM
  • Mining Algorithms
  • BFSMINE Alg. , restricts inside a scope of a
    method
  • Extensions to BFSMINE Alg.

5
Approaches the Snippet Mining Processes
Figure1 A high-level view of the snippet Mining
Process
6
Approaches cont.
  • The goal of the snippet mining is to mine from a
    given code sample repository all code snippets
    that satisfy a given user query Q,
  • SelectionAgent pre-selects a set of code model
    instances cmi on B tree index defined on all
    types declared or referred to in the code sample
    repository.
  • The MiningAgent invokes the BFSMINE algorithm for
    every code model instance

7
Approaches cont.
  • BFSMINE algorithm
  • traverses a code model instance and produces
    as output a set of paths P that represent the
    final code snippets returned to the user.
  • On completion of the BFSMINE phase, the
    MiningAgent passes the collection of the paths P,
    to the PruningAgent.

8
Approaches cont.
  • Queries
  • The query retun all snippets s, containing codes
    that instantiate a type tq
  • (1) all codes that instantiate tq
  • (2) instantiation of tq is dependent of the code
    context, i.e. via a static method
  • The following example

9
Approaches cont.
10
Approaches cont.
  • Queries
  • A type-based instantiation query
  • tq is instantiated from any type from the
    context CT(m)
  • T (s) the lexically visible types in the code
    snippet s and CT (m) denotes the type context of
    the method
  • CT (m) all set of inherited types, visible
    types in a scope of method, all types for local
    fields

11
Approaches cont.
12
Approaches cont.
  • Queries
  • Parent-based in instantiation query
  • s denotes a snippet, CP (s) the parent context of
    the snippet, CP (m) the parent context of the
    method m.
  • CP (m) The parent context of a method m, denoted
    as CP (m), is a set containing the superclass
    extended by its containing source class C, as
    well as all interfaces implemented by its
    containing source class C.

13
Approaches cont.
14
Approaches cont.
  • Source Code Model
  • A graphic representation of the structure of
    source codes. Nodes a type node, an object node,
    a method node
  • Edges inheritance, implement, composite, method,
    assignment or parameter edge.

15
Approaches cont.
  • BFSMINE Algorithm
  • Given a user query , The goal of the BFSMINE
    algorithm is to determine for all such instances
    nq, types and eventually code segments that
    instantiate the node nq and hence the query type
    tq. Domain(nq) tq

16
Approaches cont.
17
Approaches cont.
Extension-BFSMIN
18
Approaches cont.
Extension-BFSMIN
19
Approaches cont.
20
Personal Comments
  • Strengths
  • User defined queries
  • Results from a context-independent retrieval to
    various degrees of context-sensitive retrieval
  • BFSMIN Algorithm based on a graph that represents
    a source code model allows mining across method
    boundaries
  • Ranking heuristic (length, frequency, context )
    for providing best-fit code snippets
  • Multiple sample codes with the same query
  • context-independent retrieval (length / frequency
    )
  • context-sensitive retrieval (context)

21
Personal Comments
  • Potential weakness
  • Results
  • Is it possible to provide semantic ranking ?
  • Why? Probably, the return code snippets do not
    have logic among them, just only a chunk of codes
  • Validation approaches
  • To prove that snippet codes is helpful for
    developers, authors use group test. Two groups
    with the same condition except that one uses
    snippet codes, other do not.
  • Limited ?
Write a Comment
User Comments (0)
About PowerShow.com