lnu.sePublikationer
Ändra sökning
Avgränsa sökresultatet
1 - 4 av 4
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Hönel, Sebastian
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Ericsson, Morgan
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Löwe, Welf
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Wingkvist, Anna
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    A changeset-based approach to assess source code density and developer efficacy2018Ingår i: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE , 2018, s. 220-221Konferensbidrag (Refereegranskat)
    Abstract [en]

    The productivity of a (team of) developer(s) can be expressed as a ratio between effort and delivered functionality. Several different estimation models have been proposed. These are based on statistical analysis of real development projects; their accuracy depends on the number and the precision of data points. We propose a data-driven method to automate the generation of precise data points. Functionality is proportional to the code size and Lines of Code (LoC) is a fundamental metric of code size. However, code size and LoC are not well defined as they could include or exclude lines that do not affect the delivered functionality. We present a new approach to measure the density of code in software repositories. We demonstrate how the accuracy of development time spent in relation to delivered code can be improved when basing it on net-instead of the gross-size measurements. We validated our tool by studying ca. 1,650 open-source software projects.

  • 2.
    Hönel, Sebastian
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Ericsson, Morgan
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Löwe, Welf
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Wingkvist, Anna
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Bayesian Regression on segmented data using Kernel Density Estimation2019Ingår i: 5th annual Big Data Conference: Linnaeus University, Växjö, Sweden, 5-6 December 2019, Zenodo , 2019Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    The challenge of having to deal with dependent variables in classification and regression using techniques based on Bayes' theorem is often avoided by assuming a strong independence between them, hence such techniques are said to be naive. While analytical solutions supporting classification on arbitrary amounts of discrete and continuous random variables exist, practical solutions are scarce. We are evaluating a few Bayesian models empirically and consider their computational complexity. To overcome the often assumed independence, those models attempt to resolve the dependencies using empirical joint conditional probabilities and joint conditional probability densities. These are obtained by posterior probabilities of the dependent variable after segmenting the dataset for each random variable's value. We demonstrate the advantages of these models, such as their nature being deterministic (no randomization or weights required), that no training is required, that each random variable may have any kind of probability distribution, how robustness is upheld without having to impute missing data, and that online learning is effortlessly possible. We compare such Bayesian models against well-established classifiers and regression models, using some well-known datasets. We conclude that our evaluated models can outperform other models in certain settings, using classification. The regression models deliver respectable performance, without leading the field.

  • 3.
    Hönel, Sebastian
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Ericsson, Morgan
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Löwe, Welf
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Wingkvist, Anna
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities2019Ingår i: 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS) / [ed] Dr. David Shepherd, IEEE, 2019, s. 109-120Konferensbidrag (Refereegranskat)
    Abstract [en]

    Commit classification, the automatic classification of the purpose of changes to software, can support the understanding and quality improvement of software and its development process. We introduce code density of a commit, a measure of the net size of a commit, as a novel feature and study how well it is suited to determine the purpose of a change. We also compare the accuracy of code-density-based classifications with existing size-based classifications. By applying standard classification models, we demonstrate the significance of code density for the accuracy of commit classification. We achieve up to 89% accuracy and a Kappa of 0.82 for the cross-project commit classification where the model is trained on one project and applied to other projects. Such highly accurate classification of the purpose of software changes helps to improve the confidence in software (process) quality analyses exploiting this classification information.

  • 4.
    Ulan, Maria
    et al.
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Hönel, Sebastian
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Martins, Rafael Messias
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Ericsson, Morgan
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Löwe, Welf
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Wingkvist, Anna
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Kerren, Andreas
    Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
    Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities2018Ingår i: Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018 / [ed] J. Ángel Velázquez Iturbide, Jaime Urquiza Fuentes, Andreas Kerren, and Mircea F. Lungu, IEEE, 2018, s. 65-75Konferensbidrag (Refereegranskat)
    Abstract [en]

    Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.

1 - 4 av 4
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf