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Wingkvist, Anna, PhDORCID iD iconorcid.org/0000-0002-0835-823X
Publikasjoner (10 av 62) Visa alla publikasjoner
Olsson, T., Ericsson, M. & Wingkvist, A. (2019). An exploration and experiment tool suite for code to architecture mapping techniques. In: Laurence Duchien (Ed.), ECSA '19 Proceedings of the 13th European Conference on Software Architecture: . Paper presented at 13th European Conference on Software Architecture, september 9-13, 2019, Paris, France (pp. 26-29). New York, NY, USA: ACM Publications, 2
Åpne denne publikasjonen i ny fane eller vindu >>An exploration and experiment tool suite for code to architecture mapping techniques
2019 (engelsk)Inngår i: ECSA '19 Proceedings of the 13th European Conference on Software Architecture / [ed] Laurence Duchien, New York, NY, USA: ACM Publications, 2019, Vol. 2, s. 26-29Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Reflexion modeling can be used to validate that source code conforms to an intended architecture. However, it requires a mapping of source code modules (e.g., classes) to (software) architecture elements. We have developed a tool suite that allows for evaluation and exploration of automatic techniques to map source code modules to architecture elements. The suite includes a reusable core component and tools to define the architecture, define and run experiments with mapping strategies, and explore the results of these experiments. The experiments can be executed locally or in a remote high-performance computing environment.

sted, utgiver, år, opplag, sider
New York, NY, USA: ACM Publications, 2019
Emneord
Software Architecture, Module View, Architecture Compliance, Source to Architecture Mapping, Experiment
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-89210 (URN)10.1145/3344948.3344997 (DOI)978-1-4503-7142-1 (ISBN)
Konferanse
13th European Conference on Software Architecture, september 9-13, 2019, Paris, France
Tilgjengelig fra: 2019-09-20 Laget: 2019-09-20 Sist oppdatert: 2019-09-26bibliografisk kontrollert
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.
Åpne denne publikasjonen i ny fane eller vindu >>Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities
2019 (engelsk)Inngår i: QRS 2019 Proceedings / [ed] Dr. David Shepherd, 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

Emneord
Software Quality, Commit Classification, Source Code Density, Maintenance Activities
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-85473 (URN)
Konferanse
The 19th IEEE International Conference on Software Quality, Reliability, and Security, July 22-26, 2019, Sofia, Bulgaria
Tilgjengelig fra: 2019-06-17 Laget: 2019-06-17 Sist oppdatert: 2019-09-26
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
Åpne denne publikasjonen i ny fane eller vindu >>Multi-criteria Ranking Based on Joint Distributions: A Tool to Support Decision Making
2019 (engelsk)Inngår i: Perspectives in Business Informatics Research.BIR 2019: 18th International Conference on Business Informatics Research / [ed] Pańkowska M., Sandkuhl K, Springer, 2019, s. 74-88Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2019
Serie
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 365
Emneord
Aggregation, Management by objectives, Ranking
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap; Datavetenskap, Programvaruteknik
Identifikatorer
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)
Konferanse
18th International Conference, BIR 2019, Katowice, Poland, September 23-25, 2019
Forskningsfinansiär
Knowledge Foundation, 20150088
Tilgjengelig fra: 2019-09-17 Laget: 2019-09-17 Sist oppdatert: 2019-09-18bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Optimization of Software Estimation Models
2019 (engelsk)Inngår i: Proceedings of the 14th International Conference on Software Technologies - Volume 1, SciTePress, 2019, s. 141-150Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
SciTePress, 2019
Emneord
Estimation Models, Optimization, Software Engineering
HSV kategori
Forskningsprogram
Datavetenskap, Programvaruteknik
Identifikatorer
urn:nbn:se:lnu:diva-88137 (URN)10.5220/0008117701410150 (DOI)978-989-758-379-7 (ISBN)
Konferanse
14th International Conference on Software Technologies, ICSOFT, July 26-28, 2019, Prague, Czech Republic
Forskningsfinansiär
Knowledge Foundation, 20150088
Tilgjengelig fra: 2019-08-20 Laget: 2019-08-20 Sist oppdatert: 2019-08-28bibliografisk kontrollert
Olsson, T., Ericsson, M. & Wingkvist, A. (2019). Semi-Automatic Mapping of Source Code Using Naive Bayes. In: Laurence Duchien (Ed.), ECSA '19 Proceedings of the 13th European Conference on Software Architecture -: . Paper presented at 13th European Conference on Software Architecture, september 9-13, 2019, Paris, France (pp. 209-216). New York, NY, USA: ACM Publications, 2
Åpne denne publikasjonen i ny fane eller vindu >>Semi-Automatic Mapping of Source Code Using Naive Bayes
2019 (engelsk)Inngår i: ECSA '19 Proceedings of the 13th European Conference on Software Architecture - / [ed] Laurence Duchien, New York, NY, USA: ACM Publications, 2019, Vol. 2, s. 209-216Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The software industry has not adopted continuous use of static architecture conformance checking. One hindrance is the needed mapping from source code elements to elements of the architecture. We present a novel approach of generating and combining dependency and semantic information extracted from an initial set of mapped source code files. We use this to train a Naive Bayes classifier that is then used to map the remainder of the source code files. We compare this approach with the HuGMe technique on six open source projects with known mappings. We find that our approach provides an average performance improvement of 0.22 and an average precision and recall F1-score improvement of 0.26 in comparison to HuGMe.

sted, utgiver, år, opplag, sider
New York, NY, USA: ACM Publications, 2019
Emneord
software architecture, software architecture conformance, reflexion modeling, naive bayes, source code
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-89209 (URN)10.1145/3344948.3344984 (DOI)978-1-4503-7142-1 (ISBN)
Konferanse
13th European Conference on Software Architecture, september 9-13, 2019, Paris, France
Tilgjengelig fra: 2019-09-20 Laget: 2019-09-20 Sist oppdatert: 2019-09-26bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>A changeset-based approach to assess source code density and developer efficacy
2018 (engelsk)Inngår i: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE , 2018, s. 220-221Konferansepaper, Poster (with or without abstract) (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2018
Serie
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Emneord
Software Repositories, Clone Detection, Source code density, Effort estimation
HSV kategori
Forskningsprogram
Datavetenskap, Programvaruteknik
Identifikatorer
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)
Konferanse
40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN
Tilgjengelig fra: 2018-12-06 Laget: 2018-12-06 Sist oppdatert: 2019-08-29bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Interviews Aided with Machine Learning
2018 (engelsk)Inngår i: 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, s. 202-216Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2018
Serie
Lecture Notes in Business Information Processing ; 330
HSV kategori
Forskningsprogram
Datavetenskap, Programvaruteknik
Identifikatorer
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)
Konferanse
17th International Conference, BIR 2018, Stockholm, Sweden, September 24-26, 2018
Forskningsfinansiär
Knowledge Foundation, 20150088
Tilgjengelig fra: 2019-03-05 Laget: 2019-03-05 Sist oppdatert: 2019-08-29bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Introducing Quality Models Based On Joint Probabilities: Introducing Quality Models Based On Joint Probabilities
2018 (engelsk)Inngår i: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE, 2018, s. 216-217Konferansepaper, Poster (with or without abstract) (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2018
Serie
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Emneord
Quality assessment, Software metrics, Bayesian networks
HSV kategori
Forskningsprogram
Datavetenskap, Programvaruteknik
Identifikatorer
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)
Konferanse
40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN
Tilgjengelig fra: 2018-12-06 Laget: 2018-12-06 Sist oppdatert: 2019-08-29bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities
Vise andre…
2018 (engelsk)Inngår 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. 65-75Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2018
Emneord
hierarchical data exploration, multivariate data visualization, joint probabilities, t-SNE, data abstraction
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering; Datavetenskap, Programvaruteknik
Identifikatorer
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)
Konferanse
IEEE Working Conference on Software Visualization (VISSOFT), Madrid, Spain, 24-25 September, 2018
Prosjekter
Software technology for self-adaptive systems
Forskningsfinansiär
Knowledge Foundation, 20150088
Tilgjengelig fra: 2018-10-01 Laget: 2018-10-01 Sist oppdatert: 2019-08-29bibliografisk kontrollert
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)
Åpne denne publikasjonen i ny fane eller vindu >>Towards Improved Initial Mapping in Semi Automatic Clustering
2018 (engelsk)Inngår i: ECSA 2018: PROCEEDINGS OF THE 12TH EUROPEAN CONFERENCE ON SOFTWARE ARCHITECTURE: COMPANION PROCEEDINGS, Association for Computing Machinery (ACM), 2018Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2018
Emneord
Clustering, Software Architecture Conformance, HuGMe
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
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)
Konferanse
12th European Conference on Software Architecture (ECSA), Madrid, Spain, Sep 24-28, 2018
Tilgjengelig fra: 2019-02-01 Laget: 2019-02-01 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-0835-823X