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Wyświetlanie 1-3 z 3
Tytuł:
The Spanish Party System and the Issue of Assigning Responsibility
Autorzy:
Sroka, Anna
Powiązania:
https://bibliotekanauki.pl/articles/514751.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Nauk Społecznych
Tematy:
party system
Spanish party system
assigning responsibility
electoral accountability
Opis:
The article addresses the dependency between the level of institutionalization present in the Spanish party system, electoral accountability and assigning responsibility. The primary research objective of this article is to determine the extent to which electoral volatility is present in Spain, both at the aggregate and individual level, which is a measure of the degree of institutionalization reached by a party system. Next, the dependency between electoral volatility and fluidity of elites at the electoral and parliamentary level is analysed. This allows for an answer to the question of whether there is a problem in Spain with assigning responsibility, having regard to the fact that the presence of extensive electoral volatility among both voters and political elites makes it difficult to speak of effective accountability.
Źródło:
Political Preferences; 2015, 11; 162-173
2449-9064
Pojawia się w:
Political Preferences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The method of using a GPS device for distance assigning
Sposób użycia urządzenia typu GPS do wyznaczenia odległości
Autorzy:
Szpytko, J.
Hyla, P.
Powiązania:
https://bibliotekanauki.pl/articles/375176.pdf
Data publikacji:
2007
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
GPS
NMEA
wyznaczenie odległości
pozycjonowanie
nawigacja satelitarna
distance assigning
positioning
satelitte navigation
Opis:
The paper is focusing on a method of using a GPS device for assigning distance with the NMEA standard. Experiment carried out showed the usefulness of the GPS/GSM module to use in mobile applications. Further works are required to increase the accuracy of positioning.
Referat przedstawia sposób użycia urządzenia typu GPS do wyznaczania odległości z użyciem standerdu NMEA. Przeprowadzony eksperyment wykazał użyteczność modułu GPS/GSM do zastosowań w mobilnych aplikacjach. Otrzymana dokładność pozycjonowania ogranicza obszar zastosowania wykonanego rozwiązania. Wymagane są dalsze prace, aby zwiększyć dokładność pozycjonowania urządzenia.
Źródło:
Transport Problems; 2007, 2, 2; 17-23
1896-0596
2300-861X
Pojawia się w:
Transport Problems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Deep-Learning-Based Bug Priority Prediction Using RNN-LSTM Neural Networks
Autorzy:
Bani-Salameh, Hani
Sallam, Mohammed
Al shboul, Bashar
Powiązania:
https://bibliotekanauki.pl/articles/1818480.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
assigning
priority
bug tracking systems
bug priority
bug severity
closed-source
data mining
machine learning
ML
deep learning
RNN-LSTM
SVM
KNN
Opis:
Context: Predicting the priority of bug reports is an important activity in software maintenance. Bug priority refers to the order in which a bug or defect should be resolved. A huge number of bug reports are submitted every day. Manual filtering of bug reports and assigning priority to each report is a heavy process, which requires time, resources, and expertise. In many cases mistakes happen when priority is assigned manually, which prevents the developers from finishing their tasks, fixing bugs, and improve the quality. Objective: Bugs are widespread and there is a noticeable increase in the number of bug reports that are submitted by the users and teams’ members with the presence of limited resources, which raises the fact that there is a need for a model that focuses on detecting the priority of bug reports, and allows developers to find the highest priority bug reports. This paper presents a model that focuses on predicting and assigning a priority level (high or low) for each bug report. Method: This model considers a set of factors (indicators) such as component name, summary, assignee, and reporter that possibly affect the priority level of a bug report. The factors are extracted as features from a dataset built using bug reports that are taken from closed-source projects stored in the JIRA bug tracking system, which are used then to train and test the framework. Also, this work presents a tool that helps developers to assign a priority level for the bug report automatically and based on the LSTM’s model prediction. Results: Our experiments consisted of applying a 5-layer deep learning RNN-LSTM neural network and comparing the results with Support Vector Machine (SVM) and K-nearest neighbors (KNN) to predict the priority of bug reports. The performance of the proposed RNN-LSTM model has been analyzed over the JIRA dataset with more than 2000 bug reports. The proposed model has been found 90% accurate in comparison with KNN (74%) and SVM (87%). On average, RNN-LSTM improves the F-measure by 3% compared to SVM and 15.2% compared to KNN. Conclusion: It concluded that LSTM predicts and assigns the priority of the bug more accurately and effectively than the other ML algorithms (KNN and SVM). LSTM significantly improves the average F-measure in comparison to the other classifiers. The study showed that LSTM reported the best performance results based on all performance measures (Accuracy = 0.908, AUC = 0.95, F-measure = 0.892).
Źródło:
e-Informatica Software Engineering Journal; 2021, 15, 1; 29--45
1897-7979
Pojawia się w:
e-Informatica Software Engineering Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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