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Wingkvist, Anna, PhDORCID iD iconorcid.org/0000-0002-0835-823X
Publications (10 of 60) Show all publications
Hönel, S., Ericsson, M., Löwe, W. & Wingkvist, A. (2019). Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities. In: Dr. David Shepherd (Ed.), QRS 2019 Proceedings: . Paper presented at The 19th IEEE International Conference on Software Quality, Reliability, and Security, July 22-26, 2019, Sofia, Bulgaria.
Open this publication in new window or tab >>Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities
2019 (English)In: QRS 2019 Proceedings / [ed] Dr. David Shepherd, 2019Conference paper, Published paper (Refereed)
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.

Keywords
Software Quality, Commit Classification, Source Code Density, Maintenance Activities
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-85473 (URN)
Conference
The 19th IEEE International Conference on Software Quality, Reliability, and Security, July 22-26, 2019, Sofia, Bulgaria
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-08-27
Ulan, M., Ericsson, M., Löwe, W. & Wingkvist, A. (2019). Multi-criteria Ranking Based on Joint Distributions: A Tool to Support Decision Making. In: Pańkowska M., Sandkuhl K (Ed.), Perspectives in Business Informatics Research.BIR 2019: 18th International Conference on Business Informatics Research. Paper presented at 18th International Conference, BIR 2019, Katowice, Poland, September 23-25, 2019 (pp. 74-88). Springer
Open this publication in new window or tab >>Multi-criteria Ranking Based on Joint Distributions: A Tool to Support Decision Making
2019 (English)In: Perspectives in Business Informatics Research.BIR 2019: 18th International Conference on Business Informatics Research / [ed] Pańkowska M., Sandkuhl K, Springer, 2019, p. 74-88Conference paper, Published paper (Refereed)
Abstract [en]

Sound assessment and ranking of alternatives are fundamental to effective decision making. Creating an overall ranking is not trivial if there are multiple criteria, and none of the alternatives is the best according to all criteria. To address this challenge, we propose an approach that aggregates criteria scores based on their joint (probability) distribution and obtains the ranking as a weighted product of these scores. We evaluate our approach in a real-world use case based on a funding allocation problem and compare it with the traditional weighted sum aggregation model. The results show that the approaches assign similar ranks, while our approach is more interpretable and sensitive.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 365
Keywords
Aggregation, Management by objectives, Ranking
National Category
Software Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-89171 (URN)10.1007/978-3-030-31143-8_6 (DOI)978-3-030-31142-1 (ISBN)978-3-030-31143-8 (ISBN)
Conference
18th International Conference, BIR 2019, Katowice, Poland, September 23-25, 2019
Funder
Knowledge Foundation, 20150088
Available from: 2019-09-17 Created: 2019-09-17 Last updated: 2019-09-18Bibliographically approved
Kopetschny, C., Ericsson, M., Löwe, W. & Wingkvist, A. (2019). Optimization of Software Estimation Models. In: Proceedings of the 14th International Conference on Software Technologies - Volume 1: . Paper presented at 14th International Conference on Software Technologies, ICSOFT, July 26-28, 2019, Prague, Czech Republic (pp. 141-150). SciTePress
Open this publication in new window or tab >>Optimization of Software Estimation Models
2019 (English)In: Proceedings of the 14th International Conference on Software Technologies - Volume 1, SciTePress, 2019, p. 141-150Conference paper, Published paper (Refereed)
Abstract [en]

In software engineering, estimations are frequently used to determine expected but yet unknown properties of software development processes or the developed systems, such as costs, time, number of developers, efforts, sizes, and complexities. Plenty of estimation models exist, but it is hard to compare and improve them as software technologies evolve quickly. We suggest an approach to estimation model design and automated optimization allowing for model comparison and improvement based on commonly collected data points. This way, the approach simplifies model optimization and selection. It contributes to a convergence of existing estimation models to meet contemporary software technology practices and provide a possibility for selecting the most appropriate ones.

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Estimation Models, Optimization, Software Engineering
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-88137 (URN)10.5220/0008117701410150 (DOI)978-989-758-379-7 (ISBN)
Conference
14th International Conference on Software Technologies, ICSOFT, July 26-28, 2019, Prague, Czech Republic
Funder
Knowledge Foundation, 20150088
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-08-28Bibliographically approved
Hönel, S., Ericsson, M., Löwe, W. & Wingkvist, A. (2018). A changeset-based approach to assess source code density and developer efficacy. In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings: . Paper presented at 40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN (pp. 220-221). IEEE
Open this publication in new window or tab >>A changeset-based approach to assess source code density and developer efficacy
2018 (English)In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE , 2018, p. 220-221Conference paper, Poster (with or without abstract) (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Keywords
Software Repositories, Clone Detection, Source code density, Effort estimation
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-79016 (URN)10.1145/3183440.3195105 (DOI)000450109000080 ()2-s2.0-85049691648 (Scopus ID)978-1-4503-5663-3 (ISBN)
Conference
40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-08-29Bibliographically approved
Ambrosius, R., Ericsson, M., Löwe, W. & Wingkvist, A. (2018). Interviews Aided with Machine Learning. In: Zdravkovic J., Grabis J., Nurcan S., Stirna J. (Ed.), Perspectives in Business Informatics Research. BIR 2018: 17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018, Proceedings. Paper presented at 17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018 (pp. 202-216). Springer, 330
Open this publication in new window or tab >>Interviews Aided with Machine Learning
2018 (English)In: Perspectives in Business Informatics Research. BIR 2018: 17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018, Proceedings / [ed] Zdravkovic J., Grabis J., Nurcan S., Stirna J., Springer, 2018, Vol. 330, p. 202-216Conference paper, Published paper (Refereed)
Abstract [en]

We have designed and implemented a Computer Aided Personal Interview (CAPI) system that learns from expert interviews and can support less experienced interviewers by for example suggesting questions to ask or skip. We were particularly interested to streamline the due diligence process when estimating the value for software startups. For our design we evaluated some machine learning algorithms and their trade-offs, and in a small case study we evaluates their implementation and performance. We find that while there is room for improvement, the system can learn and recommend questions. The CAPI system can in principle be applied to any domain in which long interview sessions should be shortened without sacrificing the quality of the assessment.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Business Information Processing ; 330
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80974 (URN)10.1007/978-3-319-99951-7_14 (DOI)2-s2.0-85054349611 (Scopus ID)978-3-319-99950-0 (ISBN)978-3-319-99951-7 (ISBN)
Conference
17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018
Funder
Knowledge Foundation, 20150088
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-29Bibliographically approved
Ulan, M., Löwe, W., Ericsson, M. & Wingkvist, A. (2018). Introducing Quality Models Based On Joint Probabilities: Introducing Quality Models Based On Joint Probabilities. In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings: . Paper presented at 40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN (pp. 216-217). IEEE
Open this publication in new window or tab >>Introducing Quality Models Based On Joint Probabilities: Introducing Quality Models Based On Joint Probabilities
2018 (English)In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE, 2018, p. 216-217Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Multi-dimensional goals can be formalized in so-called quality models. Often, each dimension is assessed with a set of metrics that are not comparable; they come with different units, scale types, and distributions of values. Aggregating the metrics to a single quality score in an ad-hoc manner cannot be expected to provide a reliable basis for decision making. Therefore, aggregation needs to be mathematically well-defined and interpretable. We present such a way of defining quality models based on joint probabilities. We exemplify our approach using a quality model with 30 standard metrics assessing technical documentation quality and study ca. 20,000 real-world files. We study the effect of several tests on the independence and results show that metrics are, in general, not independent. Finally, we exemplify our suggested definition of quality models in this domain.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Keywords
Quality assessment, Software metrics, Bayesian networks
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-79015 (URN)10.1145/3183440.3195103 (DOI)000450109000078 ()2-s2.0-85049686901 (Scopus ID)978-1-4503-5663-3 (ISBN)
Conference
40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-08-29Bibliographically approved
Ulan, M., Hönel, S., Martins, R. M., Ericsson, M., Löwe, W., Wingkvist, A. & Kerren, A. (2018). Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities. In: J. Ángel Velázquez Iturbide, Jaime Urquiza Fuentes, Andreas Kerren, and Mircea F. Lungu (Ed.), Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018: . Paper presented at IEEE Working Conference on Software Visualization (VISSOFT), Madrid, Spain, 24-25 September, 2018 (pp. 65-75). IEEE
Open this publication in new window or tab >>Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities
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2018 (English)In: 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, p. 65-75Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
hierarchical data exploration, multivariate data visualization, joint probabilities, t-SNE, data abstraction
National Category
Human Computer Interaction Software Engineering
Research subject
Computer Science, Information and software visualization; Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-78093 (URN)10.1109/VISSOFT.2018.00015 (DOI)2-s2.0-85058463111 (Scopus ID)978-1-5386-8292-0 (ISBN)978-1-5386-8293-7 (ISBN)
Conference
IEEE Working Conference on Software Visualization (VISSOFT), Madrid, Spain, 24-25 September, 2018
Projects
Software technology for self-adaptive systems
Funder
Knowledge Foundation, 20150088
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2019-08-29Bibliographically approved
Olsson, T., Ericsson, M. & Wingkvist, A. (2018). Towards Improved Initial Mapping in Semi Automatic Clustering. In: ECSA 2018: PROCEEDINGS OF THE 12TH EUROPEAN CONFERENCE ON SOFTWARE ARCHITECTURE: COMPANION PROCEEDINGS. Paper presented at 12th European Conference on Software Architecture (ECSA), Madrid, Spain, Sep 24-28, 2018. Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Towards Improved Initial Mapping in Semi Automatic Clustering
2018 (English)In: ECSA 2018: PROCEEDINGS OF THE 12TH EUROPEAN CONFERENCE ON SOFTWARE ARCHITECTURE: COMPANION PROCEEDINGS, Association for Computing Machinery (ACM), 2018Conference paper, Published paper (Refereed)
Abstract [en]

An important step in Static Architecture Conformance Checking (SACC) is the mapping of source code entities to entities in the intended architecture. This step is currently relying on manual work, which is one hindrance for more widespread adoption of SACC in industry. Semi-automatic clustering is a promising approach to improve this, and the HuGMe clustering algorithm is an example of such a technique for use in SACC. But HuGMe relies on an initial set of clustered source code elements and algorithm parameters. We investigate the automatic mapping performance of HuGMe in two experiments to gain insight into what influence the starting set has in a medium-sized open source system, JabRef, which contain a relatively large number of architectural violations. Our results show that the highest automatic mapping performance can be achieved with a low number of elements within the initial set. However, the variability of the performance is high. We find a benefit in favoring source code elements with a high fan-out in the initial set.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018
Keywords
Clustering, Software Architecture Conformance, HuGMe
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-80159 (URN)10.1145/3241403.3241456 (DOI)000455670400051 ()2-s2.0-85055708745 (Scopus ID)978-1-4503-6483-6 (ISBN)
Conference
12th European Conference on Software Architecture (ECSA), Madrid, Spain, Sep 24-28, 2018
Available from: 2019-02-01 Created: 2019-02-01 Last updated: 2019-08-29Bibliographically approved
Olsson, T., Ericsson, M. & Wingkvist, A. (2018). Using Repository Data for Driving Software Architecture. In: Proceeding of the 40th International Conference on Software Engineering: Companion Proceeedings (ICSE), 2018. Paper presented at 40th International Conference on Software Engineering, Gothenburg, May 27-June 3, 2018 (pp. 197-198). ACM Publications
Open this publication in new window or tab >>Using Repository Data for Driving Software Architecture
2018 (English)In: Proceeding of the 40th International Conference on Software Engineering: Companion Proceeedings (ICSE), 2018, ACM Publications, 2018, p. 197-198Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

We are in the pursuit of establishing a method for continuous data driven software architecture. We describe the problem with current methods for measuring the impact of refactoring long lived systems at the architectural level and architecture compliance checking. We summarize our studies of code churn, productivity and an automatic tool for compliance checking. We conclude that architecture violations seem to play an important role, but current methods are infeasible for industry practice. Finally we propose to use repository data mining to improve current methods for architecture compliance checking.

Place, publisher, year, edition, pages
ACM Publications, 2018
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80971 (URN)10.1145/3183440.3195094 (DOI)978-1-4503-5663-3 (ISBN)
Conference
40th International Conference on Software Engineering, Gothenburg, May 27-June 3, 2018
Funder
Knowledge Foundation, 20150088
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-14Bibliographically approved
Toll, D. & Wingkvist, A. (2018). Visualizing programming session timelines. In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction: . Paper presented at 11th International Symposium on Visual Information Communication and Interaction, VINCI 2018, 13-15 August 2018 (pp. 106-107). ACM Publications
Open this publication in new window or tab >>Visualizing programming session timelines
2018 (English)In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction, ACM Publications, 2018, p. 106-107Conference paper, Published paper (Refereed)
Abstract [en]

Learning programming with tutor tools has grown in popularity. These tools present programming assignments and provide feedback in the form of test-cases and compilation errors. Our timeline visualization of data from one such tool allows us to tell a story about what files were accessed and for how long, in what order files were edited, grown or shrunk, what errors the student ran into, and how those errors were addressed. This can be done without a need to read and replay the entire programming session. In sum, the tool has been used to visualize logs from students that tried to solve programming assignments and we find interesting stories that can help us improve how we address new assignments.

Place, publisher, year, edition, pages
ACM Publications, 2018
Keywords
Software visualization, Time series data, Errors, Visual communication, Visualization, Learning programming, Programming assignments, Test case, Time-series data, Timeline visualizations, Data visualization
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-83308 (URN)10.1145/3231622.3232506 (DOI)2-s2.0-85055502530 (Scopus ID)9781450365017 (ISBN)
Conference
11th International Symposium on Visual Information Communication and Interaction, VINCI 2018, 13-15 August 2018
Available from: 2019-05-24 Created: 2019-05-24 Last updated: 2019-05-24Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0835-823X

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