2,989 research outputs found
Towards Automated Performance Bug Identification in Python
Context: Software performance is a critical non-functional requirement,
appearing in many fields such as mission critical applications, financial, and
real time systems. In this work we focused on early detection of performance
bugs; our software under study was a real time system used in the
advertisement/marketing domain.
Goal: Find a simple and easy to implement solution, predicting performance
bugs.
Method: We built several models using four machine learning methods, commonly
used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian
Networks, and Logistic Regression.
Results: Our empirical results show that a C4.5 model, using lines of code
changed, file's age and size as explanatory variables, can be used to predict
performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that
reducing the number of changes delivered on a commit, can decrease the chance
of performance bug injection.
Conclusions: We believe that our approach can help practitioners to eliminate
performance bugs early in the development cycle. Our results are also of
interest to theoreticians, establishing a link between functional bugs and
(non-functional) performance bugs, and explicitly showing that attributes used
for prediction of functional bugs can be used for prediction of performance
bugs
An Evolutionary Algorithm to Optimize Log/Restore Operations within Optimistic Simulation Platforms
In this work we address state recoverability in advanced optimistic simulation systems by proposing an evolutionary algorithm to optimize at run-time the parameters associated with state log/restore activities. Optimization takes place by adaptively selecting for each simulation object both (i) the best suited log mode (incremental vs non-incremental) and (ii) the corresponding optimal value of the log interval. Our performance optimization approach allows to indirectly cope with hidden effects (e.g., locality) as well as cross-object effects due to the variation of log/restore parameters for different simulation objects (e.g., rollback thrashing). Both of them are not captured by literature solutions based on analytical models of the overhead associated with log/restore tasks. More in detail, our evolutionary algorithm dynamically adjusts the log/restore parameters of distinct simulation objects as a whole, towards a well suited configuration. In such a way, we prevent negative effects on performance due to the biasing of the optimization towards individual simulation objects, which may cause reduced gains (or even decrease) in performance just due to the aforementioned hidden and/or cross-object phenomena. We also present an application-transparent implementation of the evolutionary algorithm within the ROme OpTimistic Simulator (ROOT-Sim), namely an open source, general purpose simulation environment designed according to the optimistic synchronization paradigm
Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions
With the advent of Digital Therapeutics (DTx), the development of software as
a medical device (SaMD) for mobile and wearable devices has gained significant
attention in recent years. Existing DTx evaluations, such as randomized
clinical trials, mostly focus on verifying the effectiveness of DTx products.
To acquire a deeper understanding of DTx engagement and behavioral adherence,
beyond efficacy, a large amount of contextual and interaction data from mobile
and wearable devices during field deployment would be required for analysis. In
this work, the overall flow of the data-driven DTx analytics is reviewed to
help researchers and practitioners to explore DTx datasets, to investigate
contextual patterns associated with DTx usage, and to establish the (causal)
relationship of DTx engagement and behavioral adherence. This review of the key
components of data-driven analytics provides novel research directions in the
analysis of mobile sensor and interaction datasets, which helps to iteratively
improve the receptivity of existing DTx.Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica
Sinic
Digital fitness: Self-monitored fitness and the commodification of movement
This work is licensed under a Creative Commons Attribution-NoDerivs (CC BY_ND) Licence. For information on use, visit www.creativecommons.org/licenses.This article moves beyond a history of domestic home video fitness programs to explore digital fitness with specific attention to the self-monitored fitness 'movement' and the hardware and software that facilitate its proliferation. The research in this area is currently conducted through preliminary small scale studies, alongside some flawed but still (inadvertently) useful undergraduate and graduate projects. Popular cultural interest is burgeoning, with the popularity of the Fitbit suite and the iWatch surging through an array of commentaries on blogs, YouTube videos, tweets and Facebook posts. This theoretical paper links digitisation with fitness to explore the balance between self-monitoring and surveillance, motivation and shaming. The Fitbit is an example of this self-monitored fitness 'movement' that reveals the ambivalence of quantifying steps and stairs while managing a volatile neoliberal working environment
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Reinforcement learning serves as a potent tool for modeling dynamic user
interests within recommender systems, garnering increasing research attention
of late. However, a significant drawback persists: its poor data efficiency,
stemming from its interactive nature. The training of reinforcement
learning-based recommender systems demands expensive online interactions to
amass adequate trajectories, essential for agents to learn user preferences.
This inefficiency renders reinforcement learning-based recommender systems a
formidable undertaking, necessitating the exploration of potential solutions.
Recent strides in offline reinforcement learning present a new perspective.
Offline reinforcement learning empowers agents to glean insights from offline
datasets and deploy learned policies in online settings. Given that recommender
systems possess extensive offline datasets, the framework of offline
reinforcement learning aligns seamlessly. Despite being a burgeoning field,
works centered on recommender systems utilizing offline reinforcement learning
remain limited. This survey aims to introduce and delve into offline
reinforcement learning within recommender systems, offering an inclusive review
of existing literature in this domain. Furthermore, we strive to underscore
prevalent challenges, opportunities, and future pathways, poised to propel
research in this evolving field.Comment: under revie
Scaling Causality Analysis for Production Systems.
Causality analysis reveals how program values influence each other.
It is important for debugging, optimizing, and understanding the execution of
programs. This thesis scales causality analysis to production systems
consisting of desktop and server applications as well as large-scale Internet
services. This enables developers to employ causality analysis to debug and
optimize complex, modern software systems. This thesis shows that it is
possible to scale causality analysis to both fine-grained instruction level
analysis and analysis of Internet scale distributed systems with thousands of
discrete software components by developing and employing automated methods to
observe and reason about causality.
First, we observe causality at a fine-grained instruction level by developing
the first taint tracking framework to support tracking millions of input
sources. We also introduce flexible taint tracking to allow
for scoping different queries and dynamic filtering of inputs, outputs, and
relationships.
Next, we introduce the Mystery Machine, which uses a ``big data'' approach to
discover causal relationships between software components in a large-scale
Internet service. We leverage the fact that large-scale Internet services
receive a large number of requests in order to observe counterexamples to
hypothesized causal relationships. Using discovered casual relationships, we
identify the critical path for request execution and use the critical path
analysis to explore potential scheduling optimizations.
Finally, we explore using causality to make data-quality tradeoffs in
Internet services. A data-quality tradeoff is an explicit decision by a software
component to return lower-fidelity data in order to improve response time or
minimize resource usage. We perform a study of data-quality tradeoffs in a
large-scale Internet service to show the pervasiveness of these
tradeoffs. We develop DQBarge, a system that enables better data-quality
tradeoffs by propagating critical information along the causal path of request
processing. Our evaluation shows that DQBarge helps Internet services mitigate
load spikes, improve utilization of spare resources, and implement dynamic
capacity planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135888/1/mcchow_1.pd
Mobile text entry behaviour in lab and in-the-wild studies : is it different?
Text entry in smartphones remains a critical element of mobile HCI. It has been widely studied in lab settings, using primarily transcription tasks, and to a far lesser extent through in-the-wild (field) experiments. So far it remains unknown how well user behaviour during lab transcription tasks approximates real use. In this paper, we present a study that provides evidence that lab text entry behaviour is clearly distinguishable from real world use. Using machine learning techniques, we show that it is possible to accurately identify the type of study in which text entry sessions took place. The implications of our findings relate to the design of future studies in text entry, aiming to support input with virtual smartphone keyboards
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