The Supportive Effect of Traceability Links in Architecture-Level Software Understanding: Two Controlled Experiments

2014 ◽  
Author(s):  
Muhammad Atif Javed ◽  
Uwe Zdun
Keyword(s):  
Controlled Experiments ◽  
Software Understanding

scholarly journals Self-healing trans-cloud applications

Computing ◽  
2021 ◽  
Author(s):  
Antonio Brogi ◽  
Jose Carrasco ◽  
Francisco Durán ◽  
Ernesto Pimentel ◽  
Jacopo Soldani
Keyword(s):  
Open Source ◽  
Cloud Services ◽  
Self Healing ◽  
Controlled Experiments ◽  
Fault Handling ◽  
Extensive Evaluation ◽  
Cloud Providers ◽  
Heterogeneous Cloud ◽  
Cloud Applications ◽  
Unstable States

AbstractTrans-cloud applications consist of multiple interacting components deployed across different cloud providers and at different service layers (IaaS and PaaS). In such complex deployment scenarios, fault handling and recovery need to deal with heterogeneous cloud offerings and to take into account inter-component dependencies. We propose a methodology for self-healing trans-cloud applications from failures occurring in application components or in the cloud services hosting them, both during deployment and while they are being operated. The proposed methodology enables reducing the time application components rely on faulted services, hence residing in “unstable” states where they can suddenly fail in cascade or exhibit erroneous behaviour. We also present an open-source prototype illustrating the feasibility of our proposal, which we have exploited to carry out an extensive evaluation based on controlled experiments and monkey testing.

Nitrogen use efficiency: does a trade-off exist between the N productivity and the mean residence time within species?

2008 ◽  
Vol 56 (3) ◽  
pp. 272 ◽  
Author(s):  
Zhi Y. Yuan ◽  
Han Y. H. Chen ◽  
Ling H. Li
Keyword(s):  
Residence Time ◽  
Nitrogen Use Efficiency ◽  
Mean Residence Time ◽  
Negative Relationship ◽  
Controlled Experiments ◽  
Nitrogen Use ◽  
Trade Off ◽  
N Supply ◽  
Use Efficiency ◽  
The Mean

Nitrogen use efficiency (NUE) can be divided into two components, i.e. N productivity (A) and the mean residence time (MRT). Controlled experiments indicate that there is not a trade-off between A and MRT within species, but this theory has not been well tested in field conditions. Here, we studied the A, MRT and NUE of Stipa krylovii Roshev. in a grassland over 4 years of N fertilisation experimentation. The three parameters (A, MRT and NUE) were significantly related to soil N supply and there was a negative relationship between A and MRT within this species (r = –0.775, P < 0.05), i.e. plants with higher A had lower MRT. Our results showed a trade-off between A and MRT within this Stipa species and this observed trade-off was attributed to different responses of A and MRT to soil fertility.

Using controlled experiments in layout

1991 ◽  
Vol 21 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Steve Meyer
Keyword(s):  
Controlled Experiments

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

BlueFlood

2021 ◽  
Vol 2 (4) ◽  
pp. 1-30
Author(s):  
Beshr Al Nahas ◽  
Antonio Escobar-Molero ◽  
Jirka Klaue ◽  
Simon Duquennoy ◽  
Olaf Landsiedel
Keyword(s):  
Experimental Study ◽  
Bit Error Rate ◽  
Error Rate ◽  
Data Dissemination ◽  
Delivery Ratio ◽  
Controlled Experiments ◽  
Seamless Integration ◽  
Peer Communication ◽  
Shift Keying ◽  
University Buildings

Bluetooth is an omnipresent technology, available on billions of devices today. While it has been traditionally limited to peer-to-peer communication and star networks, the recent Bluetooth Mesh standard extends it to multi-hop networking. In addition, the Bluetooth 5 standard introduces new modes to allow for increased reliability. In this article, we evaluate the feasibility of concurrent transmissions (CT) in Bluetooth via modeling and controlled experiments and then devise an efficient network-wide data dissemination protocol, BlueFlood, based on CT for multi-hop Bluetooth networks. First, we model and analyze how CT distorts the received waveform and characterize the Bit Error Rate of a Frequency-Shift Keying receiver to show that CT is feasible over Bluetooth. Second, we verify our analytic results with a controlled experimental study of CT over Bluetooth PHY. Third, we present BlueFlood, a fast and efficient network-wide data dissemination in multi-hop Bluetooth networks. In our experimental evaluation, in two testbeds deployed in university buildings, we show that BlueFlood achieves 99.9% end-to-end delivery ratio with a duty-cycle of 0.4% for periodic dissemination of advertising packets of 38 bytes with 200 milliseconds intervals at 2 Mbps. Moreover, we show that BlueFlood can be received by off-the-shelf devices such as smartphones, paving a seamless integration with existing technologies.

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