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Date: Mon, 23 May 2016 18:43:17 -0000
Message-ID: <20160523184317.86188.32698@eos.apache.org>
Subject: =?utf-8?q?=5BIncubator_Wiki=5D_Trivial_Update_of_=22SensSoftProposal=22_b?=
 =?utf-8?q?y_LewisJohnMcgibbney?=
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archived-at: Mon, 23 May 2016 18:43:23 -0000

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The "SensSoftProposal" page has been changed by LewisJohnMcgibbney:
https://wiki.apache.org/incubator/SensSoftProposal?action=3Ddiff&rev1=3D4&r=
ev2=3D5

  =

  =3D=3D Software as a Sensor=E2=84=A2 Project Overview =3D=3D
  =

- {{attachment:userale_figure_1.png||align=3D"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 senso=
rs 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 th=
rough scalable Elastic infrastructure and easy-to-manage Restful APIs. =

  Distill is the analytic framework of the Software as a Sensor=E2=84=A2 Pr=
oject, providing (at release) segmentation and graph analysis metrics descr=
ibing users=E2=80=99 interactions with the application to adopters. The seg=
mentation 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=E2=80=99s usage and usability metrics are =
derived from a representation of users=E2=80=99 sequential interactions wit=
h the application as a directed graph. This provides an extensible framewor=
k for providing insight as to how users integrate the functional components=
 of the application to accomplish tasks.
  =

- {{attachment:userale_figure_2.png||align=3D"left"}}
+ {{attachment:userale_figure_2.png}}
  =

  Figure 2. Software as a Sensor=E2=84=A2 System Architecture with all comp=
onents.
  =

  The Test Application Portal (TAP) provides a single point of interface fo=
r adopters of the Software as a Sensor=E2=84=A2 project. Through the Portal=
, adopters can register their applications, providing version data and perm=
issions to others for accessing data. The Portal ensures that all component=
s of the Software as a Sensor=E2=84=A2 Project have the same information. T=
he Portal also hosts a number of python D3 visualization libraries, providi=
ng adopters with a customizable =E2=80=9Cdashboard=E2=80=9D with which to a=
nalyze 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 us=
ers in systematic ways or within experimental designs. STOUT supports user =
registration, anonymization, user tracking, tasking (see Figure 3), and dat=
a integration from a variety of services. STOUT allows adopters to perform =
human subject review board compliant research studies, and both between- an=
d within-subjects designs. Adopters can add tasks, surveys and questionnair=
es through 3rd party services (e.g., SurveyMonkey). STOUT tracks users=E2=
=80=99 progress by passing a unique user IDs to other services, allowing re=
searchers to trace progress by passing a unique user IDs to other services,=
 allowing researchers to trace form data and User ALE logs to specific user=
s and task sets (see Figure 4).
  =

- {{attachment:userale_figure_3.png||align=3D"left"}}
+ {{attachment:userale_figure_3.png}}
  =

  Figure 3. STOUT assigns participants subjects to experimental conditions =
and ensures the correct task sequence. STOUT=E2=80=99s Django back end prov=
ides data on task completion, this can be used to drive other automation, i=
ncluding unlocking different task sequences and/or achievements.
  =

- {{attachment:userale_figure_4.png||align=3D"left"}}
+ {{attachment:userale_figure_4.png}}
  =

  Figure 4. STOUT User Tracking. Anonymized User IDs (hashes) are concatena=
ted with unique Task IDs. This =E2=80=9CSession ID=E2=80=9D is appended to =
URLs (see Highlighted region), custom variable fields, and User ALE, to pro=
vide and integrated user testing data collection service.
  =

  STOUT also provides for data polling from third party services (e.g., Sur=
veyMonkey) and integration with python or R scripts for statistical process=
ing 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=3D"left"}}
+ {{attachment:userale_figure_5.png}}
  =

  Figure 5. STOUT Visualization. STOUT gives experimenters direct and conti=
nuous access to automatically processed research data.
  =

@@ -92, +92 @@

  The Software as a Sensor=E2=84=A2 Project is ultimately designed to addre=
ss the wide gaps between current best practices in software user testing an=
d trends toward agile software development practices. Like much of the appl=
ied psychological sciences, user testing methods generally borrow heavily f=
rom basic research methods. These methods are designed to make data collect=
ion systematic and remove extraneous influences on test conditions. However=
, this usually means removing what we test from dynamic, noisy=E2=80=94real=
-life=E2=80=94environments. The Software as a Sensor=E2=84=A2 Project is de=
signed to allow for the same kind of systematic data collection that we exp=
ect in the laboratory, but in real-life software environments, by making so=
ftware environments data collection platforms. In doing so, we aim to not o=
nly 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=E2=84=A2 Project is that it provi=
des software developers, HCI/UX researchers, and project managers a mechani=
sm for continuous, iterative usability testing for software tools in a way =
that supports the flow (and schedule) of modern software development practi=
ces=E2=80=94Iterative, Waterfall, Spiral, and Agile. This is enabled by a f=
ew discriminating facets:
  =

- {{attachment:userale_figure_6.png||align=3D"left"}}
+ {{attachment:userale_figure_6.png}}
  =

  Figure 6. Version to Version Testing for Agile, Iterative Software Develo=
pment Methods. The Software as a Sensor=E2=84=A2 Project enables new method=
s 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 Interfac=
es 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 Co=
herence with Context. IEEE Transactions on Affective Computing, PP(99), 1-1=
. doi: 10.1109/taffc.2015.2512867
  =

-=20

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