Mining Version Histories to Guide Software Changes

1
Mining Version Histories to Guide Software
Changes Thomas Zimmerman Peter
Weisgerber Stephan Diehl Andreas Zeller
2
In this paper, we apply data mining to version
histories 'Programmers who changed these
functions also changed....' Just like the
Amazon.com feature helps the customer browsing
along related items, our ROSE tool guides the
programmer along related changes...
3
Agenda

• ROSE Overview
• CVS to ROSE
• Data Analysis
• Evaluation
• Paper Critique

4
ROSE Overview

• Aims
• Suggest and predict likely changes. Suppose a
  programmer has just made a change. What else does
  she have to change?
• Prevent errors due to incomplete changes. If a
  programmer wants to commit changes, but has
  missed a related change, ROSE issues a warning.
• Detect coupling undetectable by program analysis.
  As ROSE operates exclusively on the version
  history, it is able to detect coupling between
  items that cannot be detected by program
  analysis.

5
ROSE Overview (2)?
6
CVS to ROSE

• ROSE works in terms of changes in entities
• ex changes in directories, files, classes,
  methods, variables
• Every entity is a triple (c, i, p), where c is
  the syntactic category, i is the identifier, and
  p is the parent entity
• ex (method, initDefaults(), (class, Comp, ...))?
• Every change is expressed using predicates
• alter(e)?
• add_to(e)?
• del_from(e)?
• Each transaction from CVS is converted to a list
  of those changes

7
Data Analysis

• ROSE aims to mine rules from those alterations
• alter(field, fKeys, ...) is possibly followed
  by
• alter(method, initDefaults(), ...)?
• alter(file, plug.properties, ...)?
• The probability is measured by
• Support count. Determines the number of
  transactions the rule has been derived from.
• Confidence. The relative amount of the given
  consequences across all alternatives for a given
  antecedent.
• ex suppose fKeys was altered in 11
  transactions. 10 of those also alter()'ed
  initDefaults() and plug.properties. 10 is the
  support count, and 10/11 (or 0.909) is the
  confidence.

8
Data Analysis

• Other features
• add_to() and del_from() allow an abstraction from
  the name of an added entity to the name of the
  surrounding entity.
• The notation of entities allows varying
  granularities for mining data.
• Fine-granular mining. For source code of C-like
  languages, alter() is used for fields, functions,
  etc. add_to() is used for file entities.
• Coarse-granular mining. Regardless of file type,
  only alter() is used for file entities. add_to()
  and del_from() can be used to capture when a file
  has been added or deleted
• Coarse-granular rules have a higher support count
  and usually return more results. However they are
  less precise in location, and see limited use for
  guiding programmers.

9
Evaluation

• Usage Scenarios
• Navigation through source code. Given a change,
  can ROSE point to other entities that should
  typically be changed too?
• Error prevention. If a programmer has changed
  many entities but missed to change one, does ROSE
  find the missing one?
• Closure. When the transaction is finished, how
  often does ROSE erroneously suggest that a change
  is missing in the error prevention scenario?
• Evaluation on eight large open-source projects
• ECLIPSE
• GCC
• GIMP
• JBOSS
• JEDIT
• KOFFICE
• POSTGRES
• PYTHON

10
Evaluation (2)?

• Summary
• One can have precise suggestions or many
  suggestions, but not both.
• When given an initial item, ROSE makes
  predictions in 66 percent of all queries. On
  average, the predictions of ROSE contain 33
  percent of all items changed later in the same
  transaction, For those queries for which ROSE
  makes recommendations, in 70 of the cases, a
  correct location is within ROSE's topmost three
  suggestions.
• In 3 percent of the queries where one item is
  missing, ROSE issues a correct warning. An issued
  warning predicts on average 75 percent of the
  items that need to be considered.
• ROSE's warnings about missing items should be
  taken seriously Only 2 percent of all
  transactions cause a false alarm. In other words
  ROSE does not stand in the way.
• ROSE has its best predictive power for changes to
  existing entities.
• ROSE learns quickly A few weeks after a project
  starts, ROSE makes already useful suggestions.

11
Critique

• Likes
• The tool was applied and accordingly evaluated to
  8 projects, and conclusions were drawn depending
  on their varying natures.
• It's relevant to our assignment, thus it was easy
  to follow.
• Dislikes
• There is research value, but there is reason to
  be skeptical that the recall of such tools will
  reach practical levels (for the Navigation
  purposes). Intuitively, recommendations might
  break things if blindly followed, regardless of
  if the recommendation is correct. Ie there is no
  practical value if the recommendations are
  incomplete, which is more likely for complex
  applications where this really matters.
• I still don't know what ROSE stands for. p