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    Wyświetlanie 1-4 z 4
    Tytuł:
    Searching Design Patterns Fast by Using Tree Traversals
    Autorzy:
    Cicciarella, S.
    Napoli, C.
    Tramontana, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/226611.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    design patterns
    source code analysis
    software architecture
    tree traversals
    Opis:
    Large software systems need to be modified to remain useful. Changes can be more easily performed when their design has been carefully documented. This paper presents an approach to quickly find design patterns that have been implemented into a software system. The devised solution greatly reduces the performed checks by organising the search for a design pattern as tree traversals, where candidate classes are carefully positioned into trees. By automatically tagging classes with design pattern roles we make it easier for developers to reason with large software systems. Our approach can provide documentation that lets developers understand the role each class is playing, assess the quality of the code, have assistance for refactoring and enhancing the functionalities of the software system.
    Źródło:
    International Journal of Electronics and Telecommunications; 2015, 61, 4; 321-326
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Three Dimensional Empirical Study of Logging Questions From Six Popular Q & A Websites
    Autorzy:
    Gujral, Harshit
    Sharma, Abhinav
    Lal, Sangeeta
    Kumar, Lov
    Powiązania:
    https://bibliotekanauki.pl/articles/384178.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    classification
    debugging
    ensemble
    logging
    machine learning
    source code analysis
    tracing
    Opis:
    Background: Q&A websites such as StackOverflow or Serverfault provide an open platform for users to ask questions and to get help from experts present worldwide. These websites not only help users by answering their questions but also act as a knowledge base. These data present on these websites can be mined to extract valuable information that can benefit the software practitioners. Software engineering research community has already understood the potential benefits of mining data from Q&A websites and several research studies have already been conducted in this area. Aim: The aim of the study presented in this paper is to perform an empirical analysis of logging questions from six popular Q&A websites. Method: We perform statistical, programming language and content analysis of logging questions. Our analysis helped us to gain insight about the logging discussion happening in six different domains of the StackExchange websites. Results: Our analysis provides insight about the logging issues of software practitioners: logging questions are pervasive in all the Q&A websites, the mean time to get accepted answer for logging questions on SU and SF websites are much higher as compared to other websites, a large number of logging question invite a great amount of discussion in the SoftwareEngineering Q&A website, most of the logging issues occur in C++ and Java, the trend for number of logging questions is increasing for Java, Python, and Javascript, whereas, it is decreasing or constant for C, C++, C#, for the ServerFault and Superuser website 'C' is the dominant programming language.
    Źródło:
    e-Informatica Software Engineering Journal; 2019, 13, 1; 105-139
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    ECLogger: Cross-Project Catch-Block Logging Prediction Using Ensemble of Classifiers
    Autorzy:
    Lal, S.
    Sardana, N.
    Sureka, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/953061.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    classification
    debugging
    ensemble logging
    machine learning
    source code
    analysis
    tracing
    Opis:
    Background: Software developers insert log statements in the source code to record program execution information. However, optimizing the number of log statements in the source code is challenging. Machine learning based within-project logging prediction tools, proposed in previous studies, may not be suitable for new or small software projects. For such software projects, we can use cross-project logging prediction. Aim: The aim of the study presented here is to investigate cross-project logging prediction methods and techniques. Method: The proposed method is ECLogger, which is a novel, ensemble-based, cross-project, catch-block logging prediction model. In the research We use 9 base classifiers were used and combined using ensemble techniques. The performance of ECLogger was evaluated on on three open-source Java projects: Tomcat, CloudStack and Hadoop. Results: ECLoggerBagging, ECLoggerAverageVote, and ECLoggerMajorityVote show a considerable improvement in the average Logged F-measure (LF) on 3, 5, and 4 source!target project pairs, respectively, compared to the baseline classifiers. ECLoggerAverageVote performs best and shows improvements of 3.12% (average LF) and 6.08% (average ACC – Accuracy). Conclusion: The classifier based on ensemble techniques, such as bagging, average vote, and majority vote outperforms the baseline classifier. Overall, the ECLoggerAverageVote model performs best. The results show that the CloudStack project is more generalizable than the other projects.
    Źródło:
    e-Informatica Software Engineering Journal; 2017, 11, 1; 7-38
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    CDMA wireless system with blind multiuser detector
    Autorzy:
    Leong, W. Y.
    Homer, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/309140.pdf
    Data publikacji:
    2006
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    code division multiple access
    independent component analysis
    blind source separation
    Opis:
    In this paper we present an approach capable of countering the presence of multiple access interference (MAI) in code division multiple access (CDMA) channels. We develop and implement a blind multiuser detector, based on an independent component analysis (ICA) to mitigate both MAI and noise. This algorithm has been utilized in blind source separation (BSS) of unknown sources from their linear mixtures. It can also be used for estimation of the basis vectors of BSS. The aim is to include an ICA algorithm within a wireless receiver in order to reduce the level of interference in CDMA systems. This blind multiuser detector requires less precise knowledge of the channel than does the conventional single-user receiver. The proposed blind multiuser detector is made robust with respect to imprecise knowledge of the received signature waveforms of the user of interest. Several experiments are performed in order to verify the validity of the proposed learning algorithm.
    Źródło:
    Journal of Telecommunications and Information Technology; 2006, 1; 69-75
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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