UCI Large-Scale Collection of Application Usage Data to Inform Software Development David M. Hilbert David F. Redmiles Information and Computer Science University of California, Irvine Irvine, California

UCI Incorporate results of review into design Deploy software with expectation agents Report results of usage data and feedback collection Design Design Support - Argo Usage Data and Feedback Collection Support - EDEM Review Group Memory and Awareness - Knowledge Depot Use Usage Data and Feedback Collection Support - EDEM Background

UCI Motivation Expectations influence designs, designs embody expectations Mismatches between expectations and how applications are actually used can lead to breakdowns. Identification and resolution of mismatches can help improve fit between design and use Identifying mismatches entails observing actual use and comparing it against expectations. Our research: techniques to enable large-scale incorporation of usage data and user feedback in development to help uncover mismatches and improve the design-use fit.

UCI The Internet On the positive side –cheap, rapid, large-scale distribution of software for evaluation –simple transport mechanism for usage information and feedback –use and development increasingly becoming concurrent –should make incorporating usage data and user feedback easier On the negative side –reduces opportunities for traditional user testing –increases variety and distribution of users and usage situations –lack of scalable tools and techniques for incorporating usage information and feedback on a large scale

UCI Problems Current approaches suffer from significant limitations –usability testing => scale (size, scope, location, duration) –beta testing => data quality (incentives, knowledge, detail) –monitoring => scale & data quality (abstraction, selection, reduction, context, evolution) The subjective feedback paradox –users not having problems provide feedback => problems –users having problems don’t provide feedback => no problems The impact assessment and resource allocation problem –what is the impact of suspected problems and proposed solutions? –where should development and evaluation effort be focused?

UCI Approach Developers –design applications and identify usage expectations –create agents to collect usage data and user feedback Agents –deployed over the Internet to run on user computers (via HTTP) –perform abstraction, selection, reduction, context-capture as needed to allow actual use to be compared against expectations –report data and feedback to developers (via ) Data and feedback –inform further evolution of expectations, application, and agents

UCI Approach Expectation-Driven Event Monitoring (EDEM)

UCI Usage Scenario (Expectations) A cargo query form

UCI Usage Scenario (Usage Data) Agents monitor use and collect data unobtrusively Agents may post messages (optional)

UCI Usage Scenario (User Feedback) Users may learn more about expectations (optional) Users may provide feedback (optional)

UCI Usage Scenario (Review) Data and feedback stored for later analysis and review

UCI Agent Authoring

UCI Architecture Agent Specs saved w/ URL Development Computer Java Virtual Machine EDEM Active Agents Application UI Components Top Level Window & UI Events Property Queries Property Values HTTP Server Development Computer Agent Specs EDEM Server Collected Data User Computer Java Virtual Machine EDEM Active Agents Application UI Components Top Level Window & UI Events Property Queries Property Values Agent Specs loaded via URL Agent Reports sent via

UCI Reference Architecture System Model of UI & App: Components Events Properties Methods Data Capture Abstraction, Selection, Context, Reduction Data Packaging Data Analysis Data Prep Data Transport Analyst Model of UI & App: Features, Dialogs, Controls, User-Supplied Values, User Tasks Mapping

UCI Instrumentation intertwined w/ app Reference Architecture (Word IV) System Model of UI & App: Components Events Properties Methods Data Capture Abstraction, Selection, Context, Reduction Data Packaging Data Analysis Data Prep Data Transport Analyst Model of UI & App: Features, Dialogs, Controls, User-Supplied Values, User Tasks Mapping

UCI Event monitoring infrastructure TestWizard Database of Office UI Reference Architecture (Office IV) System Model of UI & App: Components Events Properties Methods Data Capture Abstraction, Selection, Context, Reduction Data Packaging Data Analysis Data Prep Data Transport Analyst Model of UI & App: Features, Dialogs, Controls, User-Supplied Values, User Tasks Mapping

UCI Event monitoring infrastructure Expectation Agents Reference Architecture (EDEM) System Model of UI & App: Components Events Properties Methods Data Capture Abstraction, Selection, Context, Reduction Data Packaging Data Analysis Data Prep Data Transport Analyst Model of UI & App: Features, Dialogs, Controls, User-Supplied Values, User Tasks Mapping “Pluggable” Data Abstraction, Selection, Context-Capture, and Reduction

UCI Conclusions Usage expectations –help guide data collection –raise awareness of implications of design decisions Agent architecture –abstraction, selection, reduction in-context and prior to reporting –independent evolution of instrumentation and application Combined –higher quality data (v. beta tests) with less restrictions on evaluation size, scope, location, duration (v. usability tests)

UCI Possible Applications Use of long-term data about user and users’ behavior to support –adaptive UI and application behavior –“smarter” delivery of help/suggestions/assistance Support for monitoring of component-based systems in which –event and state information can be easily “tapped” –low-level data must be related to higher level concepts of interest –available information exceeds that which can practically be collected –data collection needs evolve over time more quickly than application

UCI Future Research Large-scale evaluation of research in practice –maintenance issues, how is data used, cost-benefit analysis Relationships to other requirements-related artifacts –e.g., use cases, cognitive walkthroughs, task analysis Other types of analysis –changes in usage over time –usage involving multiple cooperating users Reuse and adaptability of infrastructure –generalize to support monitoring of arbitrary software components

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