Relevant bibliographies by topics / Automatic flaky

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Academic literature on the topic 'Automatic flaky'

Author: Grafiati
Published: 25 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Automatic flaky"

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Garnica B., Sergio A., Marius Knaust, and Sergej Fatikow. "Automatic Micro-Robotic Identification and Electrical Characterization of Graphene." Micromachines 10, no. 12 (December 11, 2019): 870. http://dx.doi.org/10.3390/mi10120870.

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Micromechanically exfoliating graphene on S i / S i O 2 substrates is commonplace for graphene researchers, but locating actual graphene flakes on these substrates is a high-effort and tiresome task. The main purpose of this work was to establish a completely automated procedure to identify those graphene flakes with as little human interaction as possible, improving on the limitations of current methods. Furthermore, automatic electrical characterization of the identified flakes was performed. The proposed micro-robotic automation sequence consists of three main steps. To start, a sample surface plane is calculated, based on multiple foci points across the substrate. Secondly, flakes on the substrate are identified in the hue, saturation, and value (HSV) color space, with an implementation to fit the measurement probe, used to avoid undersized samples and adjust the flake orientation. Finally, electrical characterization is performed based on four point probe measurements with the Van der Pauw method. Results of the successfully implemented automation sequence are presented together with flake electrical properties and validation.
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Mascheroni, Maximiliano A., and Emanuel Irrazábal. "Identifying Key Success Factors in Stopping Flaky Tests in Automated REST Service Testing." Journal of Computer Science and Technology 18, no. 02 (October 9, 2018): e16. http://dx.doi.org/10.24215/16666038.18.e16.

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A flaky test is a test which could fail or pass for the same version of a certain software code. In continuous software development environments, flaky tests represent a problem. It is difficult to get an effective and reliable testing pipeline with a set of flaky tests. Also, according to many practitioners, despite the persistence of flaky tests in software development, they have not drawn much attention from the research community. In this paper, we describe how a company faced this issue, and implemented solutions to solve flaky tests for REST web services. The paper concludes proposing a set of key success factors for stopping flaky tests in this type of testing.
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Reed, David E., Ankur R. Desai, Emily C. Whitaker, and Henry Nuckles. "Evaluation of Low-Cost, Automated Lake Ice Thickness Measurements." Journal of Atmospheric and Oceanic Technology 36, no. 4 (April 2019): 527–34. http://dx.doi.org/10.1175/jtech-d-18-0214.1.

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AbstractClimate change is expected to decrease ice coverage and thickness globally while increasing the variability of ice coverage and thickness on midlatitude lakes. Ice thickness affects physical, biological, and chemical processes as well as safety conditions for scientists and the general public. Measurements of ice thickness that are both temporally frequent and spatially extensive remain a technical challenge. Here new observational methods using repurposed soil moisture sensors that facilitate high spatial–temporal sampling of ice thickness are field tested on Lake Mendota in Wisconsin during the winter 2015/16 season. Spatial variability in ice thickness was high, with differences of 10 cm of ice column thickness over 1.05 km of horizontal distance. When observational data were compared with manual measurements and model output from both the Freshwater Lake (FLake) model and General Lake Model (GLM), ice thickness from sensors matches manual measurements, whereas GLM and FLake results showed a thinner and thicker ice layer, respectively. The FLake-modeled ice column temperature effectively remained at 0°C, not matching observations. We also show that daily ice dynamics follows the expected linear function of ice thickness growth/melt, improving confidence in sensor accuracy under field conditions. We have demonstrated a new method that allows low-cost and high-frequency measurements of ice thickness, which will be needed both to advance winter limnology and to improve on-ice safety.
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Thiery, W., A. Martynov, F. Darchambeau, J. P. Descy, P. D. Plisnier, L. Sushama, and N. P. M. van Lipzig. "Understanding the performance of the FLake model over two African Great Lakes." Geoscientific Model Development 7, no. 1 (February 18, 2014): 317–37. http://dx.doi.org/10.5194/gmd-7-317-2014.

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Abstract. The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from ~ 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60 m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.
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Thiery, W., A. Martynov, F. Darchambeau, J. P. Descy, P. D. Plisnier, L. Sushama, and N. P. M. van Lipzig. "Understanding the performance of the FLake model over the African Great Lakes." Geoscientific Model Development Discussions 6, no. 4 (October 2, 2013): 5141–87. http://dx.doi.org/10.5194/gmdd-6-5141-2013.

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Abstract. The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu, Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding Automatic Weather Stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration allowed to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and temperature structure. At Lake Kivu, mixolimnion temperatures predicted by FLake were found sensitive both to minimal variations in the external parameters (lake depth and water transparency) as to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may already lead to a regime switch from the correctly represented seasonal mixed layer deepening to either completely mixed (down to the model lake bottom) or permanently stratified (from ~10 m downwards) conditions. In contrast, model temperatures are found robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterize tropical lake water surface temperatures within atmospheric prediction models, but may be less appropriate, in its current form, to study complex limnological processes within tropical lakes. Furthermore, a study of different initial conditions showed that for tropical lakes lacking reliable initial data, a fully mixed, artificially warm initialisation is to be preferred, but only if the model is allowed to spin up until convergence is reached. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60 m during the main dry season is primarily due to enhanced lake evaporation and secondarily due to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.
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Shi, August. "SIGSOFT Outstanding Doctoral Dissertation Award." ACM SIGSOFT Software Engineering Notes 46, no. 3 (July 14, 2021): 17–18. http://dx.doi.org/10.1145/3468744.3468749.

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As software becomes more important and ubiquitous, high quality software also becomes crucial. We depend on software developers who write the software to also maintain and improve its quality. When developers make changes to software, they rely on continuous integration [6] and regression testing [15] to check that changes do not break existing functionality. Continuous integration (CI) automates the process of building and testing software after every change. The process of running tests on the code after every change is known as regression testing. The goal of regression testing is to allow developers to detect and fix faults early on, ideally the moment the faults are introduced. Regression testing is widely used in both industry and open source, but regression testing suffers from two main challenges: (1) regression testing is costly, and (2) regression test suites often contain flaky tests.
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Prakash, Pattan, V. D. Mytri, and P. S. Hiremath. "Fuzzy Rule Based Classification and Quantification of Graphite Inclusions from Microstructure Images of Cast Iron." Microscopy and Microanalysis 17, no. 6 (November 7, 2011): 896–902. http://dx.doi.org/10.1017/s1431927611011986.

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AbstractThe quantification of three classes of graphite inclusions in cast iron, namely, nodular, flake, and irregular, is the most important process in the foundry industry. This classification is based on the ISO 945 proposed morphology of graphite inclusions. This work presents a novel solution for automatic quantitative analysis of graphite inclusions into the three mentioned classes. The proposed work comprises three stages, namely, preprocessing of micrographs, classification of graphite inclusions, and then quantification of inclusions in each class. An effort has been made in this work to propose a minimum set of features to represent graphite inclusion morphology. The method employs just two geometric shape descriptors: the diameter ratio and the area ratio. A fuzzy rule based classifier is built using known feature values that are efficient in the classification of the three classes of graphite inclusions. The proposed method is automatic, fast, and provides the basis for determining many more morphological parameters that can be determined with the least effort. The results obtained by the proposed method are compared with the manual method. It is observed that the results obtained from the proposed method are useful in the optimization of cast iron manufacturing in the foundry industry.
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Xia, Z. Z., R. Z. Wang, Z. S. Lu, and L. W. Wang. "Two Heat Pipe Type High Efficient Adsorption Icemakers for Fishing Boats." Open Chemical Engineering Journal 1, no. 1 (July 20, 2007): 17–22. http://dx.doi.org/10.2174/1874123100701010017.

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The adsorption performances of compound adsorbent (the mixing of activated carbon and CaCl2 by proper technology)-ammonia are studied, which shows the obviously improvement for long term stable operation of adsorption/ desorption, and also the large adsorption cooling density. A multifunction heat pipe for heat transfer design in adsorber is invented to use waste heat for heating and sea water for cooling effectively and reliably. An adsorption icemaker experimental system driven by the exhausted heat from the diesel engine of fishing boats are studied, which shows the optimum average SCP (specific cooling power) and COP (coefficient of performance) for the refrigerator have reached to 770.4W/kg and 0.39 respectively at about -20°C evaporating temperature. Based upon the studies above, a real multifunction heat pipe adsorption icemaker is then designed and built, the system is fully automatic controlled by Programmable Logic Controller (PLC). The system operation shows the capability to make flake ice for more than 20 kg/hr.
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Nofal, Adel, Mohamed Waly, Ahmed Ahmed, and Mohamed Agour. "Selection of Cast Iron Grade for Stub-Anode Fixation in Aluminum Electrolytic Cells." Key Engineering Materials 457 (December 2010): 435–40. http://dx.doi.org/10.4028/www.scientific.net/kem.457.435.

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This work is a trial to reach the optimum composition and structure of cast iron used for anode fixation in the aluminum electrolytic cells. Grey iron with low- and high-phosphorus contents, spheroidal graphite irons with compositions typical of ferritic and pearlitic grades as well as grey iron with different carbon equivalent values were compared. A bench-scale experimental set up was used to simulate the operating conditions at the steel stub/cast iron collar/carbon anode connection. The change in microstructure and electrical resistance was measured at temperatures up to 850°C for 30 days, which correspond to the electrolytic cell operating conditions. The thermal expansion properties of irons were measured using a high precision automatic dilatometer. The electrical resistance at the connection was found to decrease with spheroidal graphite irons. In flake graphite irons the resistance decreases with lower phosphorus content as well as higher CE values due to the enhanced graphitization potential during solidification, which increases the contact pressure at the connection. Decomposition of ledeburitic carbides in the structure plays – to lower extent – some role in increasing the contact pressure. This contact pressure rather than the resistivity of the cast iron seems to play the dominant role in determining the electrical resistance of the connection.
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Feldens, Peter, Alexander Darr, Agata Feldens, and Franz Tauber. "Detection of Boulders in Side Scan Sonar Mosaics by a Neural Network." Geosciences 9, no. 4 (April 3, 2019): 159. http://dx.doi.org/10.3390/geosciences9040159.

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Boulders provide ecologically important hard grounds in shelf seas, and form protected habitats under the European Habitats Directive. Boulders on the seafloor can usually be recognized in backscatter mosaics due to a characteristic pattern of high backscatter intensity followed by an acoustic shadow. The manual identification of boulders on mosaics is tedious and subjective, and thus could benefit from automation. In this study, we train an object detection framework, RetinaNet, based on a neural network backbone, ResNet, to detect boulders in backscatter mosaics derived from a sidescan-sonar operating at 384 kHz. A training dataset comprising 4617 boulders and 2005 negative examples similar to boulders was used to train RetinaNet. The trained model was applied to a test area located in the Kriegers Flak area (Baltic Sea), and the results compared to mosaic interpretation by expert analysis. Some misclassification of water column noise and boundaries of artificial plough marks occurs, but the results of the trained model are comparable to the human interpretation. While the trained model correctly identified a higher number of boulders, the human interpreter had an advantage at recognizing smaller objects comprising a bounding box of less than 7 × 7 pixels. Almost identical performance between the best model and expert analysis was found when classifying boulder density into three classes (0, 1–5, more than 5) over 10,000 m² areas, with the best performing model reaching an agreement with the human interpretation of 90%.
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Dissertations / Theses on the topic "Automatic flaky"

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Mjörnman, Jesper, and Daniel Mastell. "Randomness as a Cause of Test Flakiness." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177303.

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With today’s focus on Continuous Integration, test cases are used to ensure the software’s reliability when integrating and developing code. Test cases that behave in an undeterministic manner are known as flaky tests, which threatens the software’s reliability. Because of flaky test’s undeterministic nature, they can be troublesome to detect and correct. This is causing companies to spend great amount of resources on flaky tests since they can reduce the quality of their products and services. The aim of this thesis was to develop a usable tool that can automatically detect flakiness in the Randomness category. This was done by initially locating and rerunning flaky tests found in public Git repositories. By scanning the resulting pytest logs from the tests that manifested flaky behaviour, noting indicators of how flakiness manifests in the Randomness category. From these findings we determined tracing to be a viable option of detecting Randomness as a cause of flakiness. The findings were implemented into our proposed tool FlakyReporter, which reruns flaky tests to determine if they pertain to the Randomness category. Our FlakyReporter tool was found to accurately categorise flaky tests into the Randomness category when tested against 25 different flaky tests. This indicates the viability of utilizing tracing as a method of categorizing flakiness.
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Nilsson, Joel. "Possibilities of automatic detection of "Async Wait" Flaky tests in Python applications." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177329.

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Flaky tests are defined as tests that show non-deterministic outcomes, meaning they can show both passing and failing results without changes to the code. These tests cause a major problem in the software development process since it can be difficult to know if the cause of a failure originates from the production- or test code. Developers may choose to ignore failing tests known to be flaky when they might actually hide real bugs in the production code. This thesis investigates a specific category of flaky tests known as "Async Wait", which are tests that makes asynchronous calls to servers and other remote resources and fails to properly wait for the results to be returned. There are tools available for detecting flaky tests, but most of these need the test to be executed and operate on run time information. In order to detect potential flakiness in an even earlier state, this thesis looks in to if it is possible to predict flaky outcomes by analyzing only at the test code itself without running it. The scope is limited to the Async Wait only to determine in which cases and under what circumstances developing an algorithm to automatically detect these flaky tests would be possible in this category. Commits from open source projects on GitHub were scanned for Async Wait flaky tests with the intention of finding the characteristics of the asynchronous calls and how the waiting for them is handled as well as how the flakiness is resolved by developers in practice in order to see if the information in only the test code is enough to predict flaky behavior.
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Conference papers on the topic "Automatic flaky"

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Groce, Alex, and Josie Holmes. "Practical Automatic Lightweight Nondeterminism and Flaky Test Detection and Debugging for Python." In 2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2020. http://dx.doi.org/10.1109/qrs51102.2020.00035.

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King, Tariq M., Dionny Santiago, Justin Phillips, and Peter J. Clarke. "Towards a Bayesian Network Model for Predicting Flaky Automated Tests." In 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2018. http://dx.doi.org/10.1109/qrs-c.2018.00031.

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Qin, Yihao, Shangwen Wang, Kui Liu, Xiaoguang Mao, and Tegawende F. Bissyande. "On the Impact of Flaky Tests in Automated Program Repair." In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2021. http://dx.doi.org/10.1109/saner50967.2021.00035.

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O'Kane, Jason M., and Steven M. LaValle. "Sloppy motors, flaky sensors, and virtual dirt: Comparing imperfect ill-informed robots." In 2007 IEEE International Conference on Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/robot.2007.364106.

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