From f672f896d6a3bf6dc4e153e1355b57fcbc15a10e Mon Sep 17 00:00:00 2001 From: Geoffrey Challen Date: Sat, 27 Dec 2014 02:37:53 -0600 Subject: [PATCH] Trimming. --- conclusion.tex | 2 +- metric.tex | 28 ++++++++++++++-------------- paper.tex | 1 - results.tex | 4 +++- usage.tex | 22 +++++++--------------- 5 files changed, 25 insertions(+), 32 deletions(-) diff --git a/conclusion.tex b/conclusion.tex index 7a99f74..9437d8a 100644 --- a/conclusion.tex +++ b/conclusion.tex @@ -21,4 +21,4 @@ awards \href{http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1205656}{1205656} and \href{http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1423215}{1423215}. - +The authors thank the anonymous reviewers for their feedback. diff --git a/metric.tex b/metric.tex index 2135804..dd62bef 100644 --- a/metric.tex +++ b/metric.tex @@ -17,10 +17,10 @@ user is paying attention to. Number of starts is also a potentially-useful input, as may be the distribution of interaction times across all times that the app was brought to the foreground. -While these measures of contact time are intuitive, there are obvious cases +\sloppypar{While these measures of contact time are intuitive, there are obvious cases in which they fail, particularly for apps that spend a great deal of time running in the background in order to deliver a small amount of useful -foreground information---such as a pedometer app. +foreground information---such as a pedometer app.} \subsection{User Interface Statistics} @@ -28,23 +28,23 @@ Patterns of interaction may also be useful to observe, and inputs such as keystrokes and touchscreen events are simple to track. However, there is more obvious differentiation between app interaction patterns between categories---users deliver far more keystrokes to a chat client than to a -video player---so it is clear that interaction statistics will have to be -used in conjunction with complementary value measure components that offset -the differences between high-interaction and low-interaction apps. This -approach also fails in the case where apps deploy confusing or unnecessary -interfaces that require a great deal of unnecessary interaction to accomplish -simple tasks. Clearly such apps should not be rewarded. +video player---so interaction statistics will have to be used in conjunction +with complementary value measure components that offset the differences +between high-interaction and low-interaction apps. This approach also fails +in the case where apps deploy confusing or unnecessary interfaces that +require a great deal of unnecessary interaction to accomplish simple tasks. +Clearly such apps should not be rewarded. \subsection{Notification Click-Through Rates} Another interesting statistic that could provide insight on app value is how often users view or click through app notifications. When notifications are delivered but not viewed, then it is unclear whether the app needed to -deliver them at all. When clickable notifications---such as those for new +deliver them. When clickable notifications---such as those for new email---provide a way for users to immediately launch the app, the percentage -of notifications that are actually clicked as opposed to ignored could be -used to at least evaluate how effective the notifications are, and may also -reflect on overall app value. +of notifications that are clicked versus ignored could be used to at least +evaluate how effective the notifications are, and may also reflect on overall +app value. Notification view and click-through rates also help put into context the energy used by apps when they are running in the background. Legitimate @@ -75,8 +75,8 @@ previously considered can fit into this framework: \item \textbf{Chat client:} the content is the messages exchanged by users, and efficiency is determined by the amount of screen time and interaction required to retrieve and render incoming messages and generate outgoing -messages as replies. Value is measured by the content of the messages and -efficient chat clients send and receive a large number of messages per joule. +messages as replies. Value is measured by the content of the messages. +Efficient chat clients exchange many messages per joule. \item \textbf{Video player:} the content is the video delivered to the user and efficiency is determined by the amount of network bandwidth and processing diff --git a/paper.tex b/paper.tex index 247de38..3a8a81e 100644 --- a/paper.tex +++ b/paper.tex @@ -77,7 +77,6 @@ Apps} \input{usage.tex} \input{metric.tex} \input{results.tex} -%\input{related.tex} \input{conclusion.tex} diff --git a/results.tex b/results.tex index 9a0a686..cbccf8c 100644 --- a/results.tex +++ b/results.tex @@ -39,6 +39,8 @@ it through a survey completed by 47~experiment participants. Unfortunately, our results are inconclusive and open to several possible interpretations which we discuss. +\newpage + \subsection{Total Energy} \input{./figures/tables/tableALL.tex} @@ -115,7 +117,7 @@ redraws. \centering \includegraphics[width=\textwidth]{./figures/survey.pdf} -\caption{\textbf{Survey Results.} The height of each bar demonstrates how +\caption{\small \textbf{Survey Results.} The height of each bar demonstrates how many of the suggested apps the user is willing to remove for better battery life, with suggestions based on overall usage or our new content-delivery efficiency measure. Our new measure does not convincingly out-perform the diff --git a/usage.tex b/usage.tex index 89ac9b2..84d061d 100644 --- a/usage.tex +++ b/usage.tex @@ -82,17 +82,13 @@ different app features or app configurations used by Alice and Bob. By computing value and, thus, energy efficiency, we can overcome these weaknesses. A value measure should allow us to compare the efficiency of two apps in different categories based on how efficiently they use energy to -deliver user value. -%, making it possible to compare games to email clients to video players. -Comparisons within the same app category should allow users to -select the most efficient email client or web browser. Aggregating results -over all users, differences in app energy efficiency should reflect how well -the app is written and how well it predicts and adapts to users, not just -differences in the core features it provides. When comparing two users using -the same app, differences in efficiency should reflect differences in +deliver user value. Comparisons within the same app category should allow +users to select the most efficient email client or web browser. Aggregating +results over all users, differences in app energy efficiency should reflect +how well the app is written and how well it predicts and adapts to users, not +just differences in the core features it provides. When comparing two users +using the same app, differences in efficiency should reflect differences in app configurations or app features. -%different app configurations or differences in how efficiently the app provides certain -%features. \subsection{Evaluating App Changes} @@ -101,11 +97,7 @@ and deliver more value per joule. Today's energy profiling tools may be able to show the energy impact of adding a new feature or changing the way that a particular feature is implemented, but energy consumption alone is not sufficient to apply Amdahl's Law properly to the problem of improving app -energy efficiency. -%For example, if a particular feature consumes a great deal -%of energy but adds little value, it is possible that it should be eliminated, -%not improved. Overall -Developers should strive to make the parts of their app +energy efficiency. Developers should strive to make the parts of their app that generate a large amount of value as energy-efficient as possible, remove parts that generate little value while consuming a great deal of energy, and defer work on everything else. -- libgit2 0.22.2