Automated System-Level Regression Test Prioritization in a Nutshell

Providing intelligent algorithms to manage the ever-increasing flow of air traffic is critical to the efficiency and economic viability of air transportation systems. Yet, current automated solutions leave existing human controllers "out of the loop" rendering the potential solutions both technically dangerous (e.g. inability to react to suddenly developing conditions) and politically charged (e.g. role of air traffic controllers in a fully automated system). Instead, this paper outlines a distributed agent-based solution where agents provide suggestions to human controllers. Though conceptually pleasing, this approach introduces two critical research issues. First, the agent actions are now filtered through interactions with other agents, human controllers and the environment before leading to a system state. This indirect action-to-effect process creates a complex learning problem. Second, even in the best case, not all air traffic controllers will be willing or able to follow the agents' suggestions. This partial participation effect will require the system to be robust to the number of controllers that follow the agent suggestions. In this paper, we present an agent reward structure that allows agents to learn good actions in this indirect environment, and explore the ability of those suggestion agents to achieve good system level performance. We present a series of experiments based on real historical air traffic data combined with simulation of air traffic flow around the New York city area. Results show that the agents can improve system-wide performance by up to 20% over that of human controllers alone, and that these results degrade gracefully when the number of human controllers that follow the agents' suggestions declines.

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QPSK demodulator using the methodology of automated system-level ASIC design

Proceedings Eleventh Annual IEEE International ASIC Conference (Cat. No.98TH8372) ◽

10.1109/asic.1998.723017 ◽

2002 ◽

Author(s):

Dae-Soon Kim ◽

Hwak-Dong Park ◽

Chan-Hyoung Kang

Keyword(s):

Automated System ◽

System Level ◽

Asic Design

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Multi-perspective Regression Test Prioritization for Time-Constrained Environments

2015 IEEE International Conference on Software Quality, Reliability and Security ◽

10.1109/qrs.2015.31 ◽

2015 ◽

Cited By ~ 10

Author(s):

Dusica Marijan

Keyword(s):

Regression Test ◽

Constrained Environments ◽

Test Prioritization

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Developing an Automated Monitoring System for Cable Yarding Systems

Croatian journal of forest engineering ◽

10.5552/crojfe.2021.768 ◽

2021 ◽

Vol 42 (2) ◽

Author(s):

Raimondo Gallo ◽

Rien Visser ◽

Fabrizio Mazzetto

Keyword(s):

Monitoring System ◽

Cycle Time ◽

Automated System ◽

System Level ◽

Tree Cover ◽

Analysis Program ◽

Automated Monitoring ◽

Time And Motion ◽

Cable Yarding ◽

Automated Monitoring System

Cable yarders are often the preferred harvesting system when extracting trees on steep terrain. While the practice of cable logging is well established, productivity is dependent on many stand and terrain variables. Being able to continuously monitor a cable yarder operation would provide the opportunity not only to manage and improve the system, but also to study the effect on operations in different conditions.This paper presents the results of an automated monitoring system that was developed and tested on a series of cable yarder operations. The system is based on the installation of a Geographical Navigation Satellite System (GNSS) onto the carriage, coupled with a data-logging unit and a data analysis program. The analysis program includes a set of algorithms able to transform the raw carriage movement data into detailed timing elements. Outputs include basic aspects such average extraction distance, average inhaul and outhaul carriage speed, but is also able to distinguish number of cycles, cycle time, as well as break the cycles into its distinct elements of outhaul, hook, inhaul and unhook.The system was tested in eight locations; four in thinning operations in Italy and four clear-cut operations in New Zealand, using three different rigging configuration of motorized slack-pulling, motorized grapple and North Bend. At all locations, a manual time and motion study was completed for comparison to the data produced by the newly developed automated system. Results showed that the system was able to identify 98% of the 369 cycles measured. The 8 cycles not detected were directly attributed to the loss of GNSS signal at two Italian sites with tree cover. For the remaining 361 cycles, the difference in gross cycle time was less than 1% and the overall accuracy for the separate elements of the cycle was less than 3% when considered at the rigging system level. The study showed that the data analyses system developed can readily convert GNSS data of the carriage movement into information useful for monitoring and studying cable yarding operations.

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