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Ericsson, Morgan, DocentORCID iD iconorcid.org/0000-0003-1173-5187
Publications (10 of 62) Show all publications
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
Open this publication in new window or tab >>An exploration and experiment tool suite for code to architecture mapping techniques
2019 (English)In: ECSA '19 Proceedings of the 13th European Conference on Software Architecture / [ed] Laurence Duchien, New York, NY, USA: ACM Publications, 2019, Vol. 2, p. 26-29Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Publications, 2019
Keywords
Software Architecture, Module View, Architecture Compliance, Source to Architecture Mapping, Experiment
National Category
Software Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-89210 (URN)10.1145/3344948.3344997 (DOI)978-1-4503-7142-1 (ISBN)
Conference
13th European Conference on Software Architecture, september 9-13, 2019, Paris, France
Available from: 2019-09-20 Created: 2019-09-20 Last updated: 2019-09-26Bibliographically approved
Hönel, S., Ericsson, M., Löwe, W. & Wingkvist, A. (2019). Bayesian Regression on segmented data using Kernel Density Estimation. In: 5th annual Big Data Conference: Linnaeus University, Växjö, Sweden, 5-6 December 2019. Paper presented at 5th annual Big Data Conference, Linnaeus University, Växjö, Sweden, 5-6 December 2019. Zenodo
Open this publication in new window or tab >>Bayesian Regression on segmented data using Kernel Density Estimation
2019 (English)In: 5th annual Big Data Conference: Linnaeus University, Växjö, Sweden, 5-6 December 2019, Zenodo , 2019Conference paper, Poster (with or without abstract) (Other academic)
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.

Place, publisher, year, edition, pages
Zenodo, 2019
Keywords
Bayes Theorem, Classification, Regression
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-90518 (URN)10.5281/zenodo.3571980 (DOI)
Conference
5th annual Big Data Conference, Linnaeus University, Växjö, Sweden, 5-6 December 2019
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2019-12-19Bibliographically approved
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: 2019-12-19
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
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
Open this publication in new window or tab >>Semi-Automatic Mapping of Source Code Using Naive Bayes
2019 (English)In: ECSA '19 Proceedings of the 13th European Conference on Software Architecture - / [ed] Laurence Duchien, New York, NY, USA: ACM Publications, 2019, Vol. 2, p. 209-216Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Publications, 2019
Keywords
software architecture, software architecture conformance, reflexion modeling, naive bayes, source code
National Category
Software Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-89209 (URN)10.1145/3344948.3344984 (DOI)978-1-4503-7142-1 (ISBN)
Conference
13th European Conference on Software Architecture, september 9-13, 2019, Paris, France
Available from: 2019-09-20 Created: 2019-09-20 Last updated: 2019-09-26Bibliographically approved
Ericsson, M. & Wingkvist, A. (2019). TDMentions: A Dataset of Technical Debt Mentions in Online Posts. In: 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON TECHNICAL DEBT (TECHDEBT 2019): . Paper presented at 2nd IEEE/ACM International Conference on Technical Debt (TechDebt), Montreal, CANADA, MAY 26-27, 2019 (pp. 123-124). IEEE
Open this publication in new window or tab >>TDMentions: A Dataset of Technical Debt Mentions in Online Posts
2019 (English)In: 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON TECHNICAL DEBT (TECHDEBT 2019), IEEE, 2019, p. 123-124Conference paper, Published paper (Refereed)
Abstract [en]

The term technical debt is easy to understand as a metaphor, but can quickly grow complex in practice. We contribute with a dataset, TDMentions, that enables researchers to study how developers and end users use the term technical debt in online posts and discussions. The dataset consists of posts from news aggregators and Q&A-sites, blog posts, and issues and commits on GitHub.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Technical debt, Data mining, Social networks
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-90909 (URN)10.1109/TechDebt.2019.00031 (DOI)000502789500023 ()978-1-7281-3371-3 (ISBN)
Conference
2nd IEEE/ACM International Conference on Technical Debt (TechDebt), Montreal, CANADA, MAY 26-27, 2019
Available from: 2020-01-15 Created: 2020-01-15 Last updated: 2020-01-15Bibliographically 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
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)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
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1173-5187

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