GOMS Analysis Automating Usability Assessment Melody Y Ivory
![GOMS Analysis & Automating Usability Assessment Melody Y. Ivory SIMS 213, UI Design & GOMS Analysis & Automating Usability Assessment Melody Y. Ivory SIMS 213, UI Design &](https://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-1.jpg)
GOMS Analysis & Automating Usability Assessment Melody Y. Ivory SIMS 213, UI Design & Development March 19, 2002
![Why Automated Usability Assessment Methods? Why Automated Usability Assessment Methods?](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-2.jpg)
Why Automated Usability Assessment Methods?
![GOMS Analysis Outline GOMS at a glance Model Human Processor Original GOMS (CMN-GOMS) Variants GOMS Analysis Outline GOMS at a glance Model Human Processor Original GOMS (CMN-GOMS) Variants](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-3.jpg)
GOMS Analysis Outline GOMS at a glance Model Human Processor Original GOMS (CMN-GOMS) Variants of GOMS in practice Summary
![GOMS at a glance Proposed by Card, Moran & Newell in 1983 – apply GOMS at a glance Proposed by Card, Moran & Newell in 1983 – apply](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-4.jpg)
GOMS at a glance Proposed by Card, Moran & Newell in 1983 – apply psychology to CS • employ user model (MHP) to predict performance of tasks in UI – task completion time, short-term memory requirements – applicable to • user interface design and evaluation • training and documentation – example of • automating usability assessment
![Model Human Processor (MHP) Card, Moran & Newell (1983) – most influential model of Model Human Processor (MHP) Card, Moran & Newell (1983) – most influential model of](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-5.jpg)
Model Human Processor (MHP) Card, Moran & Newell (1983) – most influential model of user interaction • used in GOMS analysis – 3 interacting subsystems • cognitive, perceptual & motor • each with processor & memory – described by parameters » e. g. , capacity, cycle time • serial & parallel processing Adapted from slide by Dan Glaser
![MHP (continued) Card, Moran & Newell (1983) – principles of operation • subsystem behavior MHP (continued) Card, Moran & Newell (1983) – principles of operation • subsystem behavior](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-6.jpg)
MHP (continued) Card, Moran & Newell (1983) – principles of operation • subsystem behavior under certain conditions – e. g. , Fitts’s Law, Power Law of Practice • ten principles
![MHP Subsystems Perceptual processor – sensory input (audio & visual) – code info symbolically MHP Subsystems Perceptual processor – sensory input (audio & visual) – code info symbolically](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-7.jpg)
MHP Subsystems Perceptual processor – sensory input (audio & visual) – code info symbolically – output into audio & visual image storage (WM buffers)
![MHP Subsystems Cognitive processor – input from sensory buffers – access LTM to determine MHP Subsystems Cognitive processor – input from sensory buffers – access LTM to determine](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-8.jpg)
MHP Subsystems Cognitive processor – input from sensory buffers – access LTM to determine response • previously stored info – output response into WM
![MHP Subsystems Motor processor – input response from WM – carry out response MHP Subsystems Motor processor – input response from WM – carry out response](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-9.jpg)
MHP Subsystems Motor processor – input response from WM – carry out response
![MHP Subsystem Interactions Input/output Processing – serial action • pressing key in response to MHP Subsystem Interactions Input/output Processing – serial action • pressing key in response to](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-10.jpg)
MHP Subsystem Interactions Input/output Processing – serial action • pressing key in response to light – parallel perception • driving, reading signs & hearing
![MHP Parameters Based on empirical data – word processing in the ‘ 70 s MHP Parameters Based on empirical data – word processing in the ‘ 70 s](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-11.jpg)
MHP Parameters Based on empirical data – word processing in the ‘ 70 s Processors have – cycle time ( ) Memories have – storage capacity ( ) – decay time of an item ( ) – info code type ( ) • physical, acoustic, visual & semantic
![Perceptual Subsystem Parameters Processor – cycle time ( ) = 100 msec Visual Image Perceptual Subsystem Parameters Processor – cycle time ( ) = 100 msec Visual Image](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-12.jpg)
Perceptual Subsystem Parameters Processor – cycle time ( ) = 100 msec Visual Image Store – storage capacity ( ) = 17 letters – decay time of an item ( ) = 200 msec – info code type ( ) = physical • physical properties of visual stimulus – e. g. , intensity, color, curvature, length Auditory Image Store – similar parameters = 17 [7 -17] letters VIS = 200 [70 -1000] msec VIS = Physical VIS p = 100 [50 -200] msec
![One Principle of Operation Power Law of Practice – task time on the nth One Principle of Operation Power Law of Practice – task time on the nth](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-13.jpg)
One Principle of Operation Power Law of Practice – task time on the nth trial follows a power law • • Tn = T 1 n-a, where a =. 4 i. e. , you get faster the more times you do it! applies to skilled behavior (perceptual & motor) does not apply to knowledge acquisition or quality
![Original GOMS (CMN-GOMS) Card, Moran & Newell (1983) Engineering model of user interaction – Original GOMS (CMN-GOMS) Card, Moran & Newell (1983) Engineering model of user interaction –](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-14.jpg)
Original GOMS (CMN-GOMS) Card, Moran & Newell (1983) Engineering model of user interaction – task analysis (“how to” knowledge) • Goals - user’s intentions (tasks) – e. g. , delete a file, edit text, assist a customer • Operators - actions to complete task – cognitive, perceptual & motor (MHP) – low-level (e. g. , move the mouse to menu)
![CMN-GOMS Engineering model of user interaction (continued) – task analysis (“how to” knowledge) • CMN-GOMS Engineering model of user interaction (continued) – task analysis (“how to” knowledge) •](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-15.jpg)
CMN-GOMS Engineering model of user interaction (continued) – task analysis (“how to” knowledge) • Methods - sequences of actions (operators) – based on error-free expert – may be multiple methods for accomplishing same goal » e. g. , shortcut key or menu selection • Selections - rules for choosing appropriate method – method predicted based on context – explicit task structure • hierarchy of goals & sub-goals
![Text-Editing Example (CMNGOMS) Text-Editing Example (CMNGOMS)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-16.jpg)
Text-Editing Example (CMNGOMS)
![CMN-GOMS Analysis of explicit task structure – add parameters for operators • approximations (MHP) CMN-GOMS Analysis of explicit task structure – add parameters for operators • approximations (MHP)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-17.jpg)
CMN-GOMS Analysis of explicit task structure – add parameters for operators • approximations (MHP) or empirical data • single value or parameterized estimate – predict user performance • execution time (count statements in task structure) • short-term memory requirements (stacking depth of task structure) – benefits • apply before implementation (comparing alternative designs) • apply before usability testing (reduce costs)
![Limitations of CMN-GOMS No directions for task analysis – granularity (start & stop) Serial Limitations of CMN-GOMS No directions for task analysis – granularity (start & stop) Serial](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-18.jpg)
Limitations of CMN-GOMS No directions for task analysis – granularity (start & stop) Serial instead of parallel perceptual processing – contrary to MHP Only one active goal Error-free expert performance – no problem solving or evaluation • Norman’s Human Action Cycle
![Norman’s Human Action Cycle (1988) Intention to act Evaluation of interpretations Sequence of actions Norman’s Human Action Cycle (1988) Intention to act Evaluation of interpretations Sequence of actions](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-19.jpg)
Norman’s Human Action Cycle (1988) Intention to act Evaluation of interpretations Sequence of actions Interpreting the perception Execution of sequence of actions Perceiving the state of the world GOMS The World
![Variants of GOMS Keystroke-Level Model (KLM) – simpler than CMN-GOMS • six keystroke-level primitive Variants of GOMS Keystroke-Level Model (KLM) – simpler than CMN-GOMS • six keystroke-level primitive](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-20.jpg)
Variants of GOMS Keystroke-Level Model (KLM) – simpler than CMN-GOMS • six keystroke-level primitive operators – – – K - press a key or button P - point with a mouse H - home hands D - draw a line segment M - mentally prepare to do an action R - system response time • no selections • five heuristic rules (mental operators) – still one goal activation
![Text-Editing Example (KLM) Text-Editing Example (KLM)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-21.jpg)
Text-Editing Example (KLM)
![Variants of GOMS Natural GOMS Language (NGOMSL) – more rigorous than CMN-GOMS • uses Variants of GOMS Natural GOMS Language (NGOMSL) – more rigorous than CMN-GOMS • uses](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-22.jpg)
Variants of GOMS Natural GOMS Language (NGOMSL) – more rigorous than CMN-GOMS • uses cognitive complexity theory (CCT) – user and system models » mapping between user’s goals & system model – user style rules (novice support) • task-analysis methodology • learning time predictions • flatten CMN-GOMS goal hierarchy – high-level notation (proceduralized actions) v. s. low-level operators – still one goal activation
![Text-Editing Example (NGOMSL) Text-Editing Example (NGOMSL)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-23.jpg)
Text-Editing Example (NGOMSL)
![Variants of GOMS Cognitive-Perceptual-Motor GOMS (CPM-GOMS) – activation of several goals • uses schedule Variants of GOMS Cognitive-Perceptual-Motor GOMS (CPM-GOMS) – activation of several goals • uses schedule](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-24.jpg)
Variants of GOMS Cognitive-Perceptual-Motor GOMS (CPM-GOMS) – activation of several goals • uses schedule chart (PERT chart) to represent operators & dependencies • critical path method for predictions – no selections
![Text-Editing Example (CPMGOMS) Text-Editing Example (CPMGOMS)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-25.jpg)
Text-Editing Example (CPMGOMS)
![GOMS in Practice Mouse-driven text editor (KLM) CAD system (KLM) Television control system (NGOMSL) GOMS in Practice Mouse-driven text editor (KLM) CAD system (KLM) Television control system (NGOMSL)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-26.jpg)
GOMS in Practice Mouse-driven text editor (KLM) CAD system (KLM) Television control system (NGOMSL) Minimalist documentation (NGOMSL) Telephone assistance operator workstation (CMP-GOMS) – saved about $2 million a year
![Activity GOMS analysis of using a search engine – Search for “free food”, explore Activity GOMS analysis of using a search engine – Search for “free food”, explore](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-27.jpg)
Activity GOMS analysis of using a search engine – Search for “free food”, explore 2 retrieved pages and find what you are looking for
![Summary GOMS in general – “The analysis of knowledge of how to do a Summary GOMS in general – “The analysis of knowledge of how to do a](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-28.jpg)
Summary GOMS in general – “The analysis of knowledge of how to do a task in terms of the components of goals, operators, methods & selection rules. ” (John & Kieras 94) • CMN-GOMS, KLM, NGOMSL, CPM-GOMS Analysis entails • task-analysis • parameterization of operators • predictions – execution time, learning time (NGOMSL), short-term memory requirements Application to other types of interfaces (e. g. , Web or information retrieval) – Limitations?
![Automating Usability Assessment Outline Automated Usability Assessment? Characterizing Automated Methods Automated Assessment Methods Summary Automating Usability Assessment Outline Automated Usability Assessment? Characterizing Automated Methods Automated Assessment Methods Summary](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-29.jpg)
Automating Usability Assessment Outline Automated Usability Assessment? Characterizing Automated Methods Automated Assessment Methods Summary
![Automated Usability Assessment? What does it mean to automate assessment? How could this be Automated Usability Assessment? What does it mean to automate assessment? How could this be](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-30.jpg)
Automated Usability Assessment? What does it mean to automate assessment? How could this be done? What does it require?
![Characterizing Automated Methods: Method Classes Testing – an evaluator observes users interacting with an Characterizing Automated Methods: Method Classes Testing – an evaluator observes users interacting with an](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-31.jpg)
Characterizing Automated Methods: Method Classes Testing – an evaluator observes users interacting with an interface (i. e. , completing tasks) to determine usability problems Inspection – an evaluator uses a set of criteria or heuristics to identify potential usability problems in an interface Inquiry – users provide feedback on an interface via interviews, surveys, etc.
![Characterizing Automated Methods: Method Classes Analytical Modeling – an evaluator employs user and interface Characterizing Automated Methods: Method Classes Analytical Modeling – an evaluator employs user and interface](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-32.jpg)
Characterizing Automated Methods: Method Classes Analytical Modeling – an evaluator employs user and interface models to generate usability predictions – GOMS is one example Simulation – an evaluator employs user and interface models to mimic a user interacting with an interface and report the results of this interaction (e. g. , simulated activities, errors and other quantitative measures)
![Characterizing Automated Methods: Automation Types None – no level of automation supported (i. e. Characterizing Automated Methods: Automation Types None – no level of automation supported (i. e.](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-33.jpg)
Characterizing Automated Methods: Automation Types None – no level of automation supported (i. e. , evaluator performs all aspects of the evaluation method) Capture – software automatically records usability data (e. g. , logging interface usage) Analysis – software automatically identifies potential usability problems Critique – software automates analysis and suggests improvements
![Characterizing Automated Methods: Effort Levels Minimal Effort – does not require interface usage or Characterizing Automated Methods: Effort Levels Minimal Effort – does not require interface usage or](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-34.jpg)
Characterizing Automated Methods: Effort Levels Minimal Effort – does not require interface usage or modeling Model Development (M) – requires the evaluator to develop a UI model and/or a user model in order to employ the method Informal Use (I) – requires completion of freely chosen tasks (i. e. , unconstrained use by a user or evaluator) Formal Use (F) – requires completion of specially selected tasks (i. e. , constrained use by a user or evaluator)
![Automated Assessment Methods Automated Assessment Methods](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-35.jpg)
Automated Assessment Methods
![Automated Assessment Methods: Generating Usage Data Simulation – Automated Capture – Mimic user and Automated Assessment Methods: Generating Usage Data Simulation – Automated Capture – Mimic user and](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-36.jpg)
Automated Assessment Methods: Generating Usage Data Simulation – Automated Capture – Mimic user and record activities for subsequent analysis Genetic Algorithm Modeling – Script interacts with running interface (Motif-based UI) – Deviation points in script behavior determined by genetic algorithm • Mimic novice user learning by exploration – Inexpensively generate a large number of usage traces • Find weak spots, failures, usability problems, etc. – Requires manual evaluation of trace execution
![Automated Assessment Methods: Generating Usage Data Information Scent Modeling (Bloodhound, Co. Li. De. S) Automated Assessment Methods: Generating Usage Data Information Scent Modeling (Bloodhound, Co. Li. De. S)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-37.jpg)
Automated Assessment Methods: Generating Usage Data Information Scent Modeling (Bloodhound, Co. Li. De. S) – Mimic users navigating a Web site and record paths • Web site model – linking structure, usage data, and content similarity • Considers information scent (common keywords between user goals and link text) in choosing links – Percentage of agents follow higher- and lowerscent links • Does not consider impact of page elements, such as images, reading complexity, etc. • Stopping criteria – Reach target pages or some threshold (e. g. , Co. Li. De. S
![Automated Assessment Methods: Detecting Guideline Conformance Inspection – Automated Analysis – Cannot automatically detect Automated Assessment Methods: Detecting Guideline Conformance Inspection – Automated Analysis – Cannot automatically detect](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-38.jpg)
Automated Assessment Methods: Detecting Guideline Conformance Inspection – Automated Analysis – Cannot automatically detect conformance for all guidelines – One study [Farenc et al. 99]: 78% best case, 44% worst case Quantitative Screen Measures – Size of screen elements, alignment, balance, etc. – Possibly generate initial layouts (AIDE) Interface Consistency (Sherlock) – Same widget placement and terminology (Visual Basic UIs) – Studies showed 10 -25% speedup for consistent
![Automated Assessment Methods: Detecting Guideline Conformance Quantitative Web Measures – Words, links, graphics, page Automated Assessment Methods: Detecting Guideline Conformance Quantitative Web Measures – Words, links, graphics, page](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-39.jpg)
Automated Assessment Methods: Detecting Guideline Conformance Quantitative Web Measures – Words, links, graphics, page breadth & depth, etc. (Rating Game, Hyper. AT, Web. Tango) – Most techniques not empiricallyvalidated • Web. TANGO uses empirical data & expert ratings to develop prediction models HTML Analysis (Web. SAT) – All images have alt tags, one outgoing link/page, download speed, etc.
![Automated Assessment Methods: Detecting Guideline Conformance Web Scanning Path (Design Advisor) – Determine how Automated Assessment Methods: Detecting Guideline Conformance Web Scanning Path (Design Advisor) – Determine how](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-40.jpg)
Automated Assessment Methods: Detecting Guideline Conformance Web Scanning Path (Design Advisor) – Determine how users will scan a page based on attentional effects of elements • motion, size, images, color, text style, and position – Derived from studies of multimedia presentations vs. Web designs
![Automated Assessment Methods: Suggesting Improvements Inspection – Automated Critique Rule-based critique systems – Typically Automated Assessment Methods: Suggesting Improvements Inspection – Automated Critique Rule-based critique systems – Typically](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-41.jpg)
Automated Assessment Methods: Suggesting Improvements Inspection – Automated Critique Rule-based critique systems – Typically done within a user interface management system • Very limited application – X Window UIs (KRI/AG), control systems (SYNOP), space systems (CHIMES) Object-based critique systems (Ergoval & Web. Eval) – Apply guidelines relevant to each graphical object – Widely applicable to Windows UIs HTML Critique (Bobby, Lift) – Syntax, validation, accessibility (Bobby), and others – Embed into popular authoring tool (Lift & Macromedia) – Although useful, not empirically validated
![Automated Assessment Methods: Modeling User Performance Analytical Modeling – Automated Analysis – Predict user Automated Assessment Methods: Modeling User Performance Analytical Modeling – Automated Analysis – Predict user](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-42.jpg)
Automated Assessment Methods: Modeling User Performance Analytical Modeling – Automated Analysis – Predict user behavior, mainly execution time – No methods for Web interfaces GOMS Analysis (previously discussed) – Generate predictions for GOMS models (CATHCI, QGOMS) – Generate model and predictions (USAGE, CRITIQUE) • UIs developed within user interface development environment
![Automated Assessment Methods: Modeling User Performance Cognitive Task Analysis – Input interface parameters to Automated Assessment Methods: Modeling User Performance Cognitive Task Analysis – Input interface parameters to](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-43.jpg)
Automated Assessment Methods: Modeling User Performance Cognitive Task Analysis – Input interface parameters to an underlying theoretical model (expert system) • Do not construct new model for each task – Generate predictions based on parameters as well as theoretical basis for predictions – Similar to cognitive walkthrough (supportive evaluation) Programmable User Models – Cross between GOMS and CTA analyses – Program UI on a psychologically-constrained architecture • Constraint violations suggest usability problems • Generate quantitative predictions
![Automated Assessment Methods: Simulating User Behavior Simulation – Automated Analysis Petri Net Modeling (AMME) Automated Assessment Methods: Simulating User Behavior Simulation – Automated Analysis Petri Net Modeling (AMME)](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-44.jpg)
Automated Assessment Methods: Simulating User Behavior Simulation – Automated Analysis Petri Net Modeling (AMME) – Construct petri net from logged interface usage – Simulates problem solving process (learning, decisions, and task completion) – Outputs measure of behavior complexity Information Processor Modeling (ACT-R, SOAR, CCT, …) – Methods employ sophisticated cognitive architecture with varying features • Modeled tasks and components, predictions, etc.
![Automated Assessment Methods: Simulating User Behavior Web Site Navigation (Web. Criteria) – Claimed to Automated Assessment Methods: Simulating User Behavior Web Site Navigation (Web. Criteria) – Claimed to](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-45.jpg)
Automated Assessment Methods: Simulating User Behavior Web Site Navigation (Web. Criteria) – Claimed to be similar to GOMS Analysis • Constructs model of site and predicts navigation time for a specified path – Based on idealized Web user (Max) – Navigation time only for shortest path between endpoints » Does not consider impact of page elements (e. g. , colors, reading complexity, etc. ) – Reports on page freshness and composition of pages (text, image, applets, etc. ) – Supports only a small fraction of analysis possible with guideline review approaches • Pirolli Critique, March 2000 issue of Internetworking – Used to compare sites (Industry Benchmarks)
![Activity Brainstorm about other ways to automate usability assessment – What about new technology? Activity Brainstorm about other ways to automate usability assessment – What about new technology?](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-46.jpg)
Activity Brainstorm about other ways to automate usability assessment – What about new technology?
![Summary Characterizing Automated Methods – Method Classes, Automation Types, Effort Levels Automated Methods – Summary Characterizing Automated Methods – Method Classes, Automation Types, Effort Levels Automated Methods –](http://slidetodoc.com/presentation_image_h/14b0c52274113a60e398127b189e6fd0/image-47.jpg)
Summary Characterizing Automated Methods – Method Classes, Automation Types, Effort Levels Automated Methods – Mainly automated capture and analysis – Guideline review enables automated critique – Represented only 33% of 132 surveyed approaches – Most require formal or informal interface usage More Information – webtango. berkeley. edu – Survey paper on automated methods – Papers on quantitative Web page analysis
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