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Palantír: Coordinating Distributed CMWorkspaces. Anita Sarma, André van der Hoek Institute for Software Research University of California, Irvine {asarma, andre} Pete’s workspace. Ellen’s workspace. A. B. C. D. E. C. A Typical Development Scenario. CM repository.

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Palantír: Coordinating Distributed CMWorkspaces

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Palantír: CoordinatingDistributed CMWorkspaces

Anita Sarma, André van der Hoek Institute for Software ResearchUniversity of California, Irvine{asarma, andre}

Pete’s workspace

Ellen’s workspace







A Typical Development Scenario



  • A CM workspace in reality provides two kinds of isolation:

    • Good isolation

      • Shields current work from others changes

    • Bad isolation

      • Hides knowledge of what artifacts other developers are changing

Break bad isolation, such that developers are aware of each other’s changes, but current work remains shielded from other people’s changes





The Solution

New situation:

Share information when others perform

CM operations, and not just when I

perform a CM operation

Old situation:

Information available only when I

carry out a CM operation or explicitly

request information

Many Difficult Questions

  • Which information must be shared?

  • How is the information presented?

  • How can information overload be avoided?

  • Can this approach scale?

  • Does it actually help developers coordinate better?

Goal: demonstrate feasibility of workspace awareness first!



Event wrapper

Event wrapper

Event wrapper

CM client

CM server

CM client

Pete’s workspace

Ellen’s workspace







Palantír Architecture




Internal State


Internal State


Populating a Workspace

Ellen populates

her workspace withdirectories & files

Making Changes in the Workspace

  • Ellen makes changes

  • edit – creates redo.c

  • write.c & dict.c

  • ‘?’ denotes artifacts are

  • undergoing changes

  • Green color denotes

  • changes by workspace

  • owner

Committing Changes

Ellen has finished

her changes and

committed them

‘?’ has changed to ‘!’

denoting changes are


Blue bars denote

Severity of changes

More Changes (by Other Developers)

Layers denote

concurrent changes

Other authors denoted

by shades of red color

Layers can be brought


Critical Feature: Pair-Wise Comparisons

Removing and Moving Artifacts

Icons denote CM activities

namely move and remove


Extensive metadata from

CM systems

Annotated with time of

event occurrence

Choice of author color

from palette

Back/ forward button

for easy traversal

Scalability & Information Overload

  • Application

    • Manage only relevant artifacts

      • Artifacts present in “my” workspace

      • Leverages event service filtering

    • Internal data structure versus visualization

  • User cognition

    • Pair-wise comparisons

    • Stack shows linear evolution in time

    • Filter data per user criteria

    • Sorting of artifacts per severity / date


  • Integration with two CM systems

    • CVS (optimistic)

    • RCS (pessimistic)

  • Relatively easy to implement

    • 500 lines of Java code each

    • Wraps each CVS/RCS command with a PalantirCVS/RCS command that invokes CVS/RCS and emits relevant events

    • Not complete, but the essence (~60%) is there

Related Work

  • Configuration Management

    • Coven

    • COOP/Orm

  • CSCW

    • MMM, ShrEdit

    • BSCW, “Edit wear and read wear”

  • Software Evolution Visualization

    • Code decay

    • 3D visualization


  • Palantír is a prototype that…

    • …brings awareness to distributed CM workspaces

    • …shows pair-wise conflict

    • …provides a simple measure of severity

  • Future Work

    • Examine change impact analysis for both atomic and compound artifacts

    • Additional visualizations

    • Case studies to determine effectiveness

Conflicts Do Happen!

  • Large systems, multiple developers lead to conflicting changes.

    • Perry & Votta: “Files that have high degrees of parallel changes also tend to have more defects.”

    • Perry & Votta: “Overlapping time schedule of successive releases suggest that features for different releases are being developed almost concurrently.”

  • Awareness of others changes helps in conflict resolution

    • Elvin’s success: “providing a way to gather and redistribute collaboration-focused information during everyday use.”

  • Conflicts

    • Direct Conflicts: Overlapping changes to the same artifact

    • Indirect Conflicts: Changes to one artifact modifying the behavior of another artifact

      • Implicit domain knowledge of developers.

      • Future Work: trace dependencies

    Agile Processes

    • Agile processes have fewer conflicts, but conflicts exist nonetheless

    • Increased awareness necessitated by higher number of check-ins

      • Need to synchronize workspace only for significant changes, and not for all changes in the workspace

    • A number of organizations, do not follow agile processes (NASA)

    Event Frequency

    • Event generated on check-in / check-out and other CM functions

      • Depending on the CM system in question.

    • Push Model: events generated when others perform CM operations.

    • Potential to leverage virtual file systems

      • Track smaller units of changes (save /edit)

        • Especially for severity calculations

      • Develop simple watch mechanisms

    Existing CM functionality

    • CVS watches

      • E-mail delivery mechanism is crude

      • Scaling problems

    • Coven softlocks

      • Need to specify intended changes beforehand, which is difficult to do

      • Only watches for direct conflicts




    Clients listening to events



    dev 2




    dev 1

    ws owner

    dev 4

    Pair-Wise Comparisons

    dev 3

    dev1-All: summarizes all comaprisons

    dev1-dev?: only those conflicts between dev1 and the other dev

    Visualization Features

    • Different views with different trade-offs

      • Amount of information versus level of intrusiveness

      • Scrolling marquee, fully graphical, tabular

    • Configurable

      • Selection of relevant developers, events, timeframes

    • Scalable

      • Internal data structure versus actual visualization

      • Pair-wise conflicts

      • Filter data on user criteria

      • Sorting per severity or change impact

    • Extensive metadata

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