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Dressler, D., Liapota, P. & Löwe, W. (2019). Data Driven Human Movement Assessment. In: : . Paper presented at 11th International KES Conference on Intelligent Decision Technologies; Invited session on Digital Health, Distance Learning and decision support for eHealth. Springer
Open this publication in new window or tab >>Data Driven Human Movement Assessment
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Quality assessment of human movements has many of applications in diagnosis and therapy of musculoskeletal insufficiencies and high performance sport. We suggest five purely data driven assessment methods for arbitrary human movements using inexpensive 3D sensor technology. We evaluate their accuracy by comparing them against a validated digitalization of a standardized human-expert-based assessment method for deep squats. We suggest the data driven method that shows high agreement with this baseline method, requires little expertise in the human movement and no expertise in the assessment method itself. It allows for an effective and efficient, automatic and quantitative assessment of  arbitrary human movements.

Place, publisher, year, edition, pages
Springer, 2019
Series
KES Smart Innovation Systems and Technologies series
National Category
Computer Sciences
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80984 (URN)
Conference
11th International KES Conference on Intelligent Decision Technologies; Invited session on Digital Health, Distance Learning and decision support for eHealth
Available from: 2019-03-06 Created: 2019-03-06 Last updated: 2019-04-17
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.
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
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-06-17
Dressler, D., Liapota, P. & Löwe, W. (2019). Towards an automated assessment of musculoskeletal insufficiencies. In: : . Paper presented at 11th International KES Conference on Intelligent Decision Technologies; Invited session on Data Selection in Machine Learning. Springer
Open this publication in new window or tab >>Towards an automated assessment of musculoskeletal insufficiencies
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The paper suggests a quantitative assessment of human movements using inexpensive 3D sensor technology and evaluates its accuracy by comparing it with human expert assessments. The two assessment methods show a high agreement. To achieve this, a novel sequence alignment algorithm was developed that works for arbitrary time series.

Place, publisher, year, edition, pages
Springer, 2019
Series
Smart Innovation Systems and Technologies, ISSN 2190-3018
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science; Computer and Information Sciences Computer Science, Computer Science; Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80946 (URN)
Conference
11th International KES Conference on Intelligent Decision Technologies; Invited session on Data Selection in Machine Learning
Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-04-17
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 ()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: 2019-03-06Bibliographically approved
Weyns, D., Ericsson, M., Löwe, W., Frejdestedt, F., Thornadtsson, J. & Hulth, A.-K. (2018). Applying Self-Adaptation to Automate the Management of Online Documentation of Telecom Systems. In: 14th International Conference on Automation Science and Engineering (CASE): Munich, Germany, August 20-24, 2018. Paper presented at 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, August 20-24, 2018 (pp. 1375-1380). IEEE
Open this publication in new window or tab >>Applying Self-Adaptation to Automate the Management of Online Documentation of Telecom Systems
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2018 (English)In: 14th International Conference on Automation Science and Engineering (CASE): Munich, Germany, August 20-24, 2018, IEEE, 2018, p. 1375-1380Conference paper, Published paper (Refereed)
Abstract [en]

Engineering software-intensive systems, such as production systems, is complex as these systems are subject to various types of changes that are often difficult to anticipate before deployment. Tackling this complexity requires joint expertise from different backgrounds. In this paper we focus on the problem of maintaining online technical documentation of telecom systems. In the context of continuous deployment and ever-changing user needs, high quality of the documentation of such products is in a key concern of users. To tackle this problem, different experts worked together equipping the online documentation system with a feedback loop. This feedback loop tracks changes in the system and its context and automatically adapts the documentation accordingly. The results demonstrate that this self-adaptation approach offers a viable solution to tackle the maintainability problem of online documentation of telecom systems.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-80973 (URN)10.1109/COASE.2018.8560502 (DOI)978-1-5386-3593-3 (ISBN)
Conference
14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, August 20-24, 2018
Funder
Knowledge Foundation, 20150088
Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-14Bibliographically 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)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: 2019-03-14Bibliographically 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 ()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: 2019-03-06Bibliographically 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)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: 2019-04-08Bibliographically approved
Österlund, E. & Löwe, W. (2018). Self-adaptive concurrent components. Automated Software Engineering: An International Journal, 25(1), 47-99
Open this publication in new window or tab >>Self-adaptive concurrent components
2018 (English)In: Automated Software Engineering: An International Journal, ISSN 0928-8910, E-ISSN 1573-7535, Vol. 25, no 1, p. 47-99Article in journal (Refereed) Published
Abstract [en]

Selecting the optimum component implementation variant is sometimes difficult since it depends on the component's usage context at runtime, e.g., on the concurrency level of the application using the component, call sequences to the component, actual parameters, the hardware available etc. A conservative selection of implementation variants leads to suboptimal performance, e.g., if a component is conservatively implemented as thread-safe while during the actual execution it is only accessed from a single thread. In general, an optimal component implementation variant cannot be determined before runtime and a single optimal variant might not even exist since the usage contexts can change significantly over the runtime. We introduce self-adaptive concurrent components that automatically and dynamically change not only their internal representation and operation implementation variants but also their synchronization mechanism based on a possibly changing usage context. The most suitable variant is selected at runtime rather than at compile time. The decision is revised if the usage context changes, e.g., if a single-threaded context changes to a highly contended concurrent context. As a consequence, programmers can focus on the semantics of their systems and, e.g., conservatively use thread-safe components to ensure consistency of their data, while deferring implementation and optimization decisions to context-aware runtime optimizations. We demonstrate the effect on performance with self-adaptive concurrent queues, sets, and ordered sets. In all three cases, experimental evaluation shows close to optimal performance regardless of actual contention.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Context-aware composition, Self-adaptive components, Concurrent context
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
urn:nbn:se:lnu:diva-69936 (URN)10.1007/s10515-017-0219-0 (DOI)000419589600003 ()
Available from: 2018-01-18 Created: 2018-01-18 Last updated: 2018-08-29Bibliographically approved
Bravo, G., Laitinen, M., Levin, M., Löwe, W. & Petersson, G. (2017). Big Data in Cross-Disciplinary Research: J.UCS Focused Topic. Journal of universal computer science (Online), 23(11), 1035-1037
Open this publication in new window or tab >>Big Data in Cross-Disciplinary Research: J.UCS Focused Topic
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2017 (English)In: Journal of universal computer science (Online), ISSN 0948-695X, E-ISSN 0948-6968, Vol. 23, no 11, p. 1035-1037Article in journal, Editorial material (Other academic) Published
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-72044 (URN)000429070900003 ()2-s2.0-85045057959 (Scopus ID)
Available from: 2018-03-30 Created: 2018-03-30 Last updated: 2019-05-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7565-3714

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