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Towards understanding programs through wear-based filtering

Towards understanding programs through wear-based filtering

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Towards understanding programs through wear-based filtering

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  1. Towards understanding programs through wear-based filtering Robert DeLine Amir Khella Mary Czerwinski George Robertson Microsoft Corporation SoftVis 2005

  2. Developers often change unfamiliar code • Today’s business makes it unavoidable. • Team membership is always changing • Separate teams create features and fixes • Security teams consult across products • Teams consume outsourced products • How could today’s tools be better for this task? • We studied programmers to find usability issues • We suggest an approach for improving tools

  3. A pilot study and a hunch • We ran a pilot study and watched participants... • Take lots of fruitless navigation steps • Navigate to sort through the results of searches • Get frequently lost in the program text • Hunch: one can learn the important parts of the code by watching code navigation • Hypothesis: navigation frequency correlates with importance ratings

  4. Formative, observational study • Participants run singly in two hour sessions • 10 min code exploration, 20 min per task • We logged navigation and videoed • Tasks followed by quiz and ratings questionnaire • Dependent measures • Code traversal paths • Subjective importance ratings (1 to 5) • Plus, task completion and quiz scores

  5. Study participants • 7 experienced programmers • All male, average age 36 • Average 18 years programming • All work outside Microsoft • We screened for… • Working in teams, on long-term projects • Debugging or modifying others’ code • Familiarity with Visual Studio, GUI programming

  6. Participants modified a Tetris game • Code selected for familiarity, size, and high rating • Two comprehension tasks, two modification tasks 1 Which method chooses the next game piece? 2 Which method causes pieces to fall faster? 3 Add the missing square game piece. 4 Add a command to make the piece fall immediately to the bottom.

  7. Importance ratings do correlate to navigation frequency • Pearson product moment correlation, r=0.79, p<0.01

  8. Study results • Poor completion of modification tasks • 7 / 7 completed tasks 1 and 2 • 1 / 7 completed task 3 • 1 / 7 completed task 4 (different participant) • Participants used different learning styles • All started by exploring control flow • 2 / 7 used top-down, hypothesis-driven approach • 5 / 7 used bottom-up approach and visited more code

  9. Usability problems • Participants wanted more overview information • Architectural diagrams, e.g. UML class diagrams • Descriptions of the relationships among components • Short summaries of important algorithmic steps • Participants got lost in the program text • Had difficulty managing many open documents • Needed extra navigation to explore search results • Wanted help in tracking exploration (bread crumbs) • Participants overused short-term memory • Difficulty recognizing previously visited code • Annoyance when they couldn’t use spatial memory

  10. How can we make the task easier? • Let’s use navigation data for social filtering • The more often code is visited, the greater its importance for a newcomer. • The more often parts of code are visited in succession, the more related they are. • Team Tracks extends Visual Studio • Filters file & class browsers to most visited items • Recommends related code based on context

  11. Demo of Team Tracks

  12. Conceptual design not yet implemented • UML class diagram with navigation heat map

  13. Closest related work • Read wear/edit wear Hill, Hollan, Wroblewski, McCandless ’92 • Introduced idea of tracking document usage • Focus on team awareness • Project Watcher Schneider, Gutwin, Penner, Paquette ’04 • Applied read wear/edit ware idea to software artifacts • Focus on team awareness • ROSE Zimmermann, Weißgerber, Diehl, Zeller ’04 • Find change patterns in source history • Focus on recommending related code • Mylar Kersten, Murphy ’05 • Track file-level navigation per individual user • Focus on managing user’s working set

  14. On-going work • Team Tracks evaluation submitted to VL/HCC ’05 1 Lab study with same setup as formative study 2 Longitudinal study with compiler team • Further research • Compare navigation data with source history • Scope navigation data by person, by time • Help newcomers learn process, not just code • research.microsoft.com/hip Human Interactions in Programming