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    Wyświetlanie 1-3 z 3
    Tytuł:
    Empirical Study of the Evolution of Python Questions on Stack Overflow
    Autorzy:
    Syam, Gopika
    Lal, Sangeeta
    Chen, Tao
    Powiązania:
    https://bibliotekanauki.pl/articles/9783959.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    Python programming
    Software Development
    Stack Overflow
    Topic Modelling
    Opis:
    Background: Python is a popular and easy-to-use programming language. It is constantly expanding, with new features and libraries being introduced daily for a broad range of applications. This dynamic expansion needs a robust support structure for developers to effectively utilise the language. Aim: In this study we conduct an in-depth analysis focusing on several research topics to understand the theme of Python questions and identify the challenges that developers encounter, using the questions posted on Stack Overflow. Method:We perform a quantitative and qualitative analysis of Python questions in Stack Overflow. Topic Modelling is also used to determine the most popular and difficult topics among developers. Results: The findings of this study revealed a recent surge in questions about scientific computing libraries pandas and TensorFlow. Also, we observed that the discussion of Data Structures and Formats is more popular in the Python community, whereas areas such as Installation, Deployment, and IDE are still challenging. Conclusion: This study can direct the research and development community to put more emphasis on tackling the actual issues that Python programmers are facing.
    Źródło:
    e-Informatica Software Engineering Journal; 2023, 17, 1; 230107
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applying Machine Learning to Software Fault Prediction
    Autorzy:
    Wójcicki, B.
    Dabrowski, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/384105.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    classifier
    fault prediction
    machine learning
    metric
    Naïve Bayes
    Python
    quality
    software intelligence
    Opis:
    Introduction: Software engineering continuously suffers from inadequate software testing. The automated prediction of possibly faulty fragments of source code allows developers to focus development efforts on fault-prone fragments first. Fault prediction has been a topic of many studies concentrating on C/C++ and Java programs, with little focus on such programming languages as Python. Objectives: In this study the authors want to verify whether the type of approach used in former fault prediction studies can be applied to Python. More precisely, the primary objective is conducting preliminary research using simple methods that would support (or contradict) the expectation that predicting faults in Python programs is also feasible. The secondary objective is establishing grounds for more thorough future research and publications, provided promising results are obtained during the preliminary research. Methods: It has been demonstrated that using machine learning techniques, it is possible to predict faults for C/C++ and Java projects with recall 0.71 and false positive rate 0.25. A similar approach was applied in order to find out if promising results can be obtained for Python projects. The working hypothesis is that choosing Python as a programming language does not significantly alter those results. A preliminary study is conducted and a basic machine learning technique is applied to a few sample Python projects. If these efforts succeed, it will indicate that the selected approach is worth pursuing as it is possible to obtain for Python results similar to the ones obtained for C/C++ and Java. However, if these efforts fail, it will indicate that the selected approach was not appropriate for the selected group of Python projects. Results: The research demonstrates experimental evidence that fault-prediction methods similar to those developed for C/C++ and Java programs can be successfully applied to Python programs, achieving recall up to 0.64 with false positive rate 0.23 (mean recall 0.53 with false positive rate 0.24). This indicates that more thorough research in this area is worth conducting. Conclusion: Having obtained promising results using this simple approach, the authors conclude that the research on predicting faults in Python programs using machine learning techniques is worth conducting, natural ways to enhance the future research being: using more sophisticated machine learning techniques, using additional Python-specific features and extended data sets.
    Źródło:
    e-Informatica Software Engineering Journal; 2018, 12, 1; 199-216
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Metody realizacji wybranych algorytmów numerycznych do modelowania układów elektromechanicznych za pomocą aplikacji internetowej
    Methods of implementation of selected numerical algorithms for modeling of electromechanical systems using web applications
    Autorzy:
    Macek-Kamińska, K.
    Kamiński, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1812160.pdf
    Data publikacji:
    2012
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    modelowanie układów elektromechanicznych
    algorytmy numeryczne
    aplikacja internetowa
    system bazy danych
    język programowania Python
    rama programowa Django
    Opis:
    Article presents the authors expierences in creating a web-based application which is able to perform selected numerical calculations useful in electromechanical systems modeling. This application will enable to carry out the simulation calculations in real time and present their results on dinamic created web pages. All data required for calculations will be derived from the corresponding database tables. The proposed solution will give two new possibilities not available in traditional methods of calculation: it will allow to carry out calculations using any computer with Internet access and tools for easy data storage in a properly designed database.
    Źródło:
    Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej. Studia i Materiały; 2012, 66, 32; 354-359
    1733-0718
    Pojawia się w:
    Prace Naukowe Instytutu Maszyn, Napędów i Pomiarów Elektrycznych Politechniki Wrocławskiej. Studia i Materiały
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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