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From Apache Wiki <>
Subject [Incubator Wiki] Trivial Update of "SensSoftProposal" by LewisJohnMcgibbney
Date Mon, 23 May 2016 18:43:17 GMT
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The "SensSoftProposal" page has been changed by LewisJohnMcgibbney:

  == Software as a Sensor™ Project Overview ==
- {{attachment:userale_figure_1.png||align="left"}}
+ {{attachment:userale_figure_1.png}}
  Figure 1. User ALE Elastic Back End Schema, with Transfer Protocols.
@@ -48, +48 @@

  Once instrumented with User ALE, software tools become human signal sensors in their own
right. Most importantly, the data that User ALE collects is owned outright by adopters and
can be made available to other processes through scalable Elastic infrastructure and easy-to-manage
Restful APIs. 
  Distill is the analytic framework of the Software as a Sensor™ Project, providing (at
release) segmentation and graph analysis metrics describing users’ interactions with the
application to adopters. The segmentation features allow adopters to focus their analyses
of user activity data based on desired data attributes (e.g., certain interactions, elements,
etc.), as well as attributes describing the software tool users, if that data was also collected.
Distill’s usage and usability metrics are derived from a representation of users’ sequential
interactions with the application as a directed graph. This provides an extensible framework
for providing insight as to how users integrate the functional components of the application
to accomplish tasks.
- {{attachment:userale_figure_2.png||align="left"}}
+ {{attachment:userale_figure_2.png}}
  Figure 2. Software as a Sensor™ System Architecture with all components.
  The Test Application Portal (TAP) provides a single point of interface for adopters of the
Software as a Sensor™ project. Through the Portal, adopters can register their applications,
providing version data and permissions to others for accessing data. The Portal ensures that
all components of the Software as a Sensor™ Project have the same information. The Portal
also hosts a number of python D3 visualization libraries, providing adopters with a customizable
“dashboard” with which to analyze and view user activity data, calling analytic processes
from Distill.
  Finally, the Subject Tracking and Online User Testing (STOUT) application, provides support
for HCI/UX researchers that want to collect data from users in systematic ways or within experimental
designs. STOUT supports user registration, anonymization, user tracking, tasking (see Figure
3), and data integration from a variety of services. STOUT allows adopters to perform human
subject review board compliant research studies, and both between- and within-subjects designs.
Adopters can add tasks, surveys and questionnaires through 3rd party services (e.g., SurveyMonkey).
STOUT tracks users’ progress by passing a unique user IDs to other services, allowing researchers
to trace progress by passing a unique user IDs to other services, allowing researchers to
trace form data and User ALE logs to specific users and task sets (see Figure 4).
- {{attachment:userale_figure_3.png||align="left"}}
+ {{attachment:userale_figure_3.png}}
  Figure 3. STOUT assigns participants subjects to experimental conditions and ensures the
correct task sequence. STOUT’s Django back end provides data on task completion, this can
be used to drive other automation, including unlocking different task sequences and/or achievements.
- {{attachment:userale_figure_4.png||align="left"}}
+ {{attachment:userale_figure_4.png}}
  Figure 4. STOUT User Tracking. Anonymized User IDs (hashes) are concatenated with unique
Task IDs. This “Session ID” is appended to URLs (see Highlighted region), custom variable
fields, and User ALE, to provide and integrated user testing data collection service.
  STOUT also provides for data polling from third party services (e.g., SurveyMonkey) and
integration with python or R scripts for statistical processing of data collected through
STOUT. D3 visualization libraries embedded in STOUT allow adopters to view distributions of
quantitative data collected from form data (see Figure 5).
- {{attachment:userale_figure_5.png||align="left"}}
+ {{attachment:userale_figure_5.png}}
  Figure 5. STOUT Visualization. STOUT gives experimenters direct and continuous access to
automatically processed research data.
@@ -92, +92 @@

  The Software as a Sensor™ Project is ultimately designed to address the wide gaps between
current best practices in software user testing and trends toward agile software development
practices. Like much of the applied psychological sciences, user testing methods generally
borrow heavily from basic research methods. These methods are designed to make data collection
systematic and remove extraneous influences on test conditions. However, this usually means
removing what we test from dynamic, noisy—real-life—environments. The Software as a Sensor™
Project is designed to allow for the same kind of systematic data collection that we expect
in the laboratory, but in real-life software environments, by making software environments
data collection platforms. In doing so, we aim to not only collect data from more realistic
environments, and use-cases, but also to integrate the test enterprise into agile software
development process. 
  Our vision for The Software as a Sensor™ Project is that it provides software developers,
HCI/UX researchers, and project managers a mechanism for continuous, iterative usability testing
for software tools in a way that supports the flow (and schedule) of modern software development
practices—Iterative, Waterfall, Spiral, and Agile. This is enabled by a few discriminating
- {{attachment:userale_figure_6.png||align="left"}}
+ {{attachment:userale_figure_6.png}}
  Figure 6. Version to Version Testing for Agile, Iterative Software Development Methods.
The Software as a Sensor™ Project enables new methods for collecting large amounts of data
on software tools, deriving insights rapidly to inject into subsequent iterations
@@ -269, +269 @@

  Mariano, L. J., Poore, J. C., Krum, D. M., Schwartz, J. L., Coskren, W. D., & Jones,
E. M. (2015). Modeling Strategic Use of Human Computer Interfaces with Novel Hidden Markov
Models. [Methods]. Frontiers in Psychology, 6. doi: 10.3389/fpsyg.2015.00919
  Poore, J., Webb, A., Cunha, M., Mariano, L., Chapell, D., Coskren, M., & Schwartz, J.
(2016). Operationalizing Engagement with Multimedia as User Coherence with Context. IEEE Transactions
on Affective Computing, PP(99), 1-1. doi: 10.1109/taffc.2015.2512867

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