Mik Kersten

1. Focusing Knowledge Work with Task Context • Mik Kersten Thesis defenseDecember 15, 2006

4. Problems • Information overload • Many knowledge work tasks cut across system structure • Browsing and query tools overload users with irrelevant information • Context loss • Tools burden users with finding artifacts relevant to the task • Context is lost whenever a task switch occurs • Users to waste time repeatedly recreating their context

5. Thesis • A model of task context that automatically weights the relevance of system information to a task by monitoring interaction can focus a programmer's work and improve productivity. • This task context model is robust to both structured and semi-structured information, and thus applies to other kinds of knowledge work.

6. Approach • Memory • Episodic memory: one-shot, only single exposure required • Semantic memory: multiple exposures required • Our approach • Leverage episodic memory, offload semantic memory • Tasks: episodes • Context: weighting of relevant semantics to task

7. Related Work • Memory • Episodic memory: one-shot, only single exposure required • Semantic memory: multiple exposures required • Our approach • Leverage episodic memory, offload semantic memory • Tasks: episodes • Explicit tasks (UMEA, TaskTracer): flat model, lack of fine-grained structure • Context: weighting of relevant semantics • Slices (Weiser) and searches (MasterScope): structure only • Interaction-based (Wear-based filtering): no explicit tasks

10. Model & Operations

11. Model • Interaction • Task context • Degree-of-interest (DOI) weighting • Frequency and recency of interaction with element • Both direct and indirect interaction interest

12. Topology • Task context graph • Edges added for relations between elements • Scaling factors determine shape, e.g. decay rate • Thresholds define interest levels • [l, ∞] Landmark • (0, ∞] Interesting • [-∞, 0] Uninteresting

13. Operations • Once task context is explicit • Can treat subsets relevant to the task as a unit • Can project this subset onto the UI • Perform operations on these subsets • Composition • See context of two tasks simultaneously • Slicing • Unit test suite can be slow to run on large project • Find all interesting subtypes of TestCase c c d a b b T T

14. More operations • Propagation • Interacting with method propagatesto containing elements • Prediction • Structurally related elements of potentialinterest automatically added to task context • Only interaction stored

15. Implementation: programming

16. Implementation: knowledge work

17. Validation

18. Validation • Questions • Does task context impact the productivity of programmers? • Does it generalize to other kinds of knowledge work? • Problems • Knowledge work environment hard to reproduce in the lab • No evidence that non-experts are a good approximation of experts • Measure long-term effects to account for diversity of tasks • Approach • Longitudinal field studies • Voluntary use of prototypes • Monitoring framework for observation

19. Study 1: feasibility • Productivity metric • Approximate productivity with edit ratio (edits /selections) • Programmers are more productive when coding than when browsing, searching, scrolling, and navigating • Subjects • Six professional programmers at IBM • Results • Promising edit ratio improvement

20. Study 2: programmers • Subjects • Advertised study at EclipseCon 2005 conference • 99 registered, 16 passed treatment threshold • Method and study framework • User study framework sent interaction histories to UBC server • Baseline period of 1500 events (approx 2 weeks) • Treatment period of 3000 events, to address learning curve

21. Study 2: results • Statistically significant increase in edit ratio • Within-subjects paired t-test of edit ratio (p = 0.003) • Model accuracy • 84% of selections were of elements with a positive DOI • 5% predicted or propagated DOI • 2% negative DOI • Task activity • Most subjects switched tasks regularly • Surprises • Scaling factors roughly tuned for study, but still unchanged

22. Study 3: knowledge workers • Subjects • 8 total, ranged from CTO to administrative assistant • Method and study framework • Same framework as previous, monitor interaction with files and web • No reliable measure of productivity, gathered detailed usage data

23. Study 3: results • Task Activity • Users voluntarily activate tasks when provided with task context • Activations/day ranged from 1 to 11, average is 5.8 • Task context contents • Long paths are common • Density over system structure is low • Tagging did not provide a reliable mechanism for retrieval • Task context sizes • Non-trivial sizes, some large (hundreds of elements) • Many tasks had both file and web context • Model accuracy • Decay works, most elements get filtered

24. Summary

25. Contributions • Generic task context model • Weighted based on the frequency and recency of interaction • Supports structured and semi-structured data • Weighting is key to reducing information overload • Capturing per-task reduces loss of context when multi-tasking • Task context operations • Support growing and shrinking the model to tailor to activities • Integrate model with existing tools • Instantiation of the model • For Java, XML, generic file and web document structure • Can be extended to other kinds of domains and application platforms • Monitoring and framework • Reusable for studying knowledge work

26. Conclusion • Tools’ point of view has not scaled • Complexity continues to grow, our ability to deal with it doesn’t • Task context takes users’ point of view • Offloads semantic memory, leverages episodic memory • Impact on researchers • University of Victoria group extended it to ontology browsing • Users • “Mylar is the next killer feature for all IDEs” Willian Mitsuda • Industry • “…it’ll ultimately become as common as tools like JUnit, if not more so.” Carey Schwaber, Forrester analyst