Search for dissertations about: "dm"

Showing result 6 - 10 of 192 swedish dissertations containing the word dm.

  2. 6. Quantifying Process Quality : The Role of Effective Organizational Learning in Software Evolution

    Author : Sebastian Hönel; Morgan Ericsson; Welf Löwe; Anna Wingkvist; Miroslaw Staron; Linnéuniversitetet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Software Size; Software Metrics; Commit Classification; Maintenance Activities; Software Quality; Process Quality; Project Management; Organizational Learning; Machine Learning; Visualization; Optimization; Software Technology; Programvaruteknik; Informations- och programvisualisering; Information and software visualization; Computer Science; Datavetenskap; Statistics Econometrics; Statistik;

    Abstract : Real-world software applications must constantly evolve to remain relevant. This evolution occurs when developing new applications or adapting existing ones to meet new requirements, make corrections, or incorporate future functionality. READ MORE

  4. 7. Sentiment and Stance Visualization of Textual Data for Social Media

    Author : Kostiantyn Kucher; Andreas Kerren; Carita Paradis; Magnus Sahlgren; Ross Maciejewski; Linnéuniversitetet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; stance visualization; sentiment visualization; text visualization; stance analysis; sentiment analysis; opinion mining; visualization; interaction; visual analytics; NLP; text mining; text analytics; social media; Informations- och programvisualisering; Information and software visualization;

    Abstract : Rapid progress in digital technologies has transformed the world in many ways during the past few decades, in particular, with the new means of communication such as social media. Social media platforms typically rely on textual data produced or shared by the users in multiple timestamped posts. READ MORE

  5. 8. A Computational Approach for Modelling Context across Different Application Domains

    Author : Alisa Lincke; Marc Jansen; Marcelo Milrad; Barbara Wasson; Linnéuniversitetet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; context modelling; multidimensional vector space model; contextualisation; recommender systems; rich context model; Media Technology; Medieteknik; Computer Science; Datavetenskap;

    Abstract : Nowadays, people use a wide range of devices (e.g., mobile phones, smart watches, tablets, activity bands, laptops) to access different digital applications and services. The ubiquitous distribution of these devices allows them to be used across different settings, in different situations, and in a large number of different domains. READ MORE

  6. 9. A Multimodal Seamless Learning Approach Supported by Mobile Digital Storytelling (mDS)

    Author : Susanna Nordmark; Marcelo Milrad; Simon Winter; Jimmy Jaldemark; Linnéuniversitetet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; co-design; constructivism; design-based research; digital cultural heritage; mobile digital storytelling; mobile learning; mobile seamless learning; multimodality; new media literacies; technology enhanced learning; Data- och informationsvetenskap; Computer and Information Sciences Computer Science; Media Technology; Medieteknik;

    Abstract : The use of digital tools such as smartphones, tablets and laptops have shown potential to enhance teaching and learning in a wide variety of contexts. 21st century skills such as creativity, problem-solving and innovation as means for supporting learning and knowledge creation, are considered fundamental proficiencies in today's technology- driven society, and they are therefore considered essential to promote, already from the earliest of school years. READ MORE

  7. 10. Incremental Clustering of Source Code : a Machine Learning Approach

    Author : Tobias Olsson; Morgan Ericsson; Sebastian Herold; Linnéuniversitetet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Naive Bayes; Source Code Clustering; Incremental Clustering; Software Architecture; Technical Debt; Computer Science; Datavetenskap;

    Abstract : Technical debt at the architectural level is a severe threat to software development projects. Uncontrolled technical debt that is allowed to accumulate will undoubtedly hinder speedy development and maintenance, introduce bugs and problems in the software product, and may ultimately result in the abandonment of the source code. READ MORE