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Publikasjoner (10 av 48) Visa alla publikasjoner
Garousi, V., Borg, M. & Oivo, M. (2020). Practical relevance of software engineering research: synthesizing the community’s voice. Journal of Empirical Software Engineering
Åpne denne publikasjonen i ny fane eller vindu >>Practical relevance of software engineering research: synthesizing the community’s voice
2020 (engelsk)Inngår i: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]

Software engineering (SE) research should be relevant to industrial practice. There have been regular discussions in the SE community on this issue since the 1980’s, led by pioneers such as Robert Glass. As we recently passed the milestone of “50 years of software engineering”, some recent positive efforts have been made in this direction, e.g., establishing “industrial” tracks in several SE conferences. However, many researchers and practitioners believe that we, as a community, are still struggling with research relevance and utility. The goal of this paper is to synthesize the evidence and experience-based opinions shared on this topic so far in the SE community, and to encourage the community to further reflect and act on the research relevance. For this purpose, we have conducted a Multi-vocal Literature Review (MLR) of 54 systematically-selected sources (papers and non peer-reviewed articles). Instead of relying on and considering the individual opinions on research relevance, mentioned in each of the sources, the MLR aims to synthesize and provide the “holistic” view on the topic. The highlights of our MLR findings are as follows. The top three root causes of low relevance, discussed in the community, are: (1) Researchers having simplistic views (or wrong assumptions) about SE in practice; (2) Lack of connection with industry; and (3) Wrong identification of research problems. The top three suggestions for improving research relevance are: (1) Using appropriate research approaches such as action-research; (2) Choosing relevant (practical) research problems; and (3) Collaborating with industry. By synthesizing all the discussions on this important topic so far, this paper aims to encourage further discussions and actions in the community to increase our collective efforts to improve the research relevance. Furthermore, we raise the need for empirically-grounded and rigorous studies on the relevance problem in SE research, as carried out in other fields such as management science. © 2020, The Author(s).

sted, utgiver, år, opplag, sider
Springer, 2020
Emneord
Evidence, Multi-vocal literature review (MLR), Research relevance, Research utility, Software engineering, Utility programs, Action research, Industrial practices, Literature reviews, Research approach, Research problems, Root cause, Industrial research
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-44927 (URN)10.1007/s10664-020-09803-0 (DOI)2-s2.0-85081619988 (Scopus ID)
Merknad

Funding details: Natural Sciences and Engineering Research Council of Canada, NSERC; Funding details: Horizon 2020; Funding details: European Commission, EC; Funding text 1: Synthesis: Due to the above fundamental short-comings in many national / international funding systems, it is challenging to conduct truly relevant research. Although there are positive policies in the context of certain funding agencies which encourage, or even require,98 IAC for submitting research grants, e.g., the Horizon 2020 funding system of the European Union, the “Engage” and Collaborative Research and Development (CRD) grants in Canada by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Tilgjengelig fra: 2020-05-12 Laget: 2020-05-12 Sist oppdatert: 2020-05-12bibliografisk kontrollert
Borg, M. & Groen, E. C. (2020). Preface: REFSQ 2020 posters and tools track. Paper presented at Joint 26th International Conference on Requirements Engineering: Foundation for Software Quality Workshops, Doctoral Symposium, Live Studies Track, and Poster Track, REFSQ-JP 2020; Pisa; Italy; 24 March 2020 through 27 March 2020. CEUR Workshop Proceedings, 2584
Åpne denne publikasjonen i ny fane eller vindu >>Preface: REFSQ 2020 posters and tools track
2020 (engelsk)Inngår i: CEUR Workshop Proceedings, ISSN 1613-0073, E-ISSN 1613-0073, Vol. 2584Artikkel i tidsskrift, Editorial material (Fagfellevurdert) Published
sted, utgiver, år, opplag, sider
CEUR-WS, 2020
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-44725 (URN)2-s2.0-85082726744 (Scopus ID)
Konferanse
Joint 26th International Conference on Requirements Engineering: Foundation for Software Quality Workshops, Doctoral Symposium, Live Studies Track, and Poster Track, REFSQ-JP 2020; Pisa; Italy; 24 March 2020 through 27 March 2020
Tilgjengelig fra: 2020-04-14 Laget: 2020-04-14 Sist oppdatert: 2020-04-14bibliografisk kontrollert
Borg, M. & Groen, E. (2020). Preface: REFSQ 2020 posters and tools track. Paper presented at 24 March 2020 through 27 March 2020. CEUR Workshop Proceedings, 2584
Åpne denne publikasjonen i ny fane eller vindu >>Preface: REFSQ 2020 posters and tools track
2020 (engelsk)Inngår i: CEUR Workshop Proceedings, ISSN 1613-0073, E-ISSN 1613-0073, Vol. 2584Artikkel i tidsskrift, Editorial material (Annet vitenskapelig) Published
sted, utgiver, år, opplag, sider
CEUR-WS, 2020
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-44912 (URN)2-s2.0-85082726744 (Scopus ID)
Konferanse
24 March 2020 through 27 March 2020
Tilgjengelig fra: 2020-05-12 Laget: 2020-05-12 Sist oppdatert: 2020-05-12bibliografisk kontrollert
Chatzipetrou, P., Papatheocharous, E., Wnuk, K., Borg, M., Alegroth, E. & Gorschek, T. (2019). Component attributes and their importance in decisions and component selection. Software quality journal, 1-27
Åpne denne publikasjonen i ny fane eller vindu >>Component attributes and their importance in decisions and component selection
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2019 (engelsk)Inngår i: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, s. 1-27Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Component-based software engineering is a common approach in the development and evolution of contemporary software systems. Different component sourcing options are available, such as: (1) Software developed internally (in-house), (2) Software developed outsourced, (3) Commercial off-the-shelf software, and (4) Open-Source Software. However, there is little available research on what attributes of a component are the most important ones when selecting new components. The objective of this study is to investigate what matters the most to industry practitioners when they decide to select a component. We conducted a cross-domain anonymous survey with industry practitioners involved in component selection. First, the practitioners selected the most important attributes from a list. Next, they prioritized their selection using the Hundred-Dollar ($100) test. We analyzed the results using compositional data analysis. The results of this exploratory analysis showed that cost was clearly considered to be the most important attribute for component selection. Other important attributes for the practitioners were: support of the componentlongevity prediction, and level of off-the-shelf fit to product. Moreover, several practitioners still consider in-house software development to be the sole option when adding or replacing a component. On the other hand, there is a trend to complement it with other component sourcing options and, apart from cost, different attributes factor into their decision. Furthermore, in our analysis, nonparametric tests and biplots were used to further investigate the practitioners’ inherent characteristics. It seems that smaller and larger organizations have different views on what attributes are the most important, and the most surprising finding is their contrasting views on the cost attribute: larger organizations with mature products are considerably more cost aware.

Emneord
Component-based software engineering, Component sourcing options, Decision making, Compositional data analysis, Cumulative voting
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-39897 (URN)10.1007/s11219-019-09465-2 (DOI)2-s2.0-85073954446 (Scopus ID)
Tilgjengelig fra: 2019-09-11 Laget: 2019-09-11 Sist oppdatert: 2020-01-31
Cito, J., Wettinger, J., Lwakatare, L., Borg, M. & Li, F. (2019). Feedback from operations to software development—a devops perspective on runtime metrics and logs. In: Lect. Notes Comput. Sci.: . Paper presented at DEVOPS 2018: Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment (pp. 184-195).
Åpne denne publikasjonen i ny fane eller vindu >>Feedback from operations to software development—a devops perspective on runtime metrics and logs
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2019 (engelsk)Inngår i: Lect. Notes Comput. Sci., 2019, s. 184-195Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

DevOps achieve synergy between software development and operations engineers. This synergy can only happen if the right culture is in place to foster communication between these roles. We investigate the relationship between runtime data generated during production and how this data can be used as feedback in the software development process. For that, we want to discuss case study organizations that have different needs on their operations-to-development feedback pipeline, from which we abstract and propose a more general, higher-level feedback process. Given such a process, we discuss a technical environment required to support this process. We sketch out different scenarios in which feedback is useful in different phases of the software development life-cycle.

Emneord
DevOps, Feedback, Software engineering, Computer software, Life cycle, Development and operations, Feedback process, Run-time data, Runtimes, Software development life cycle, Software development process, Technical environments, Software design
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-37755 (URN)10.1007/978-3-030-06019-0_14 (DOI)2-s2.0-85061095685 (Scopus ID)9783030060183 (ISBN)
Konferanse
DEVOPS 2018: Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment
Tilgjengelig fra: 2019-02-13 Laget: 2019-02-13 Sist oppdatert: 2019-02-13bibliografisk kontrollert
Helali Moghadam, M., Saadatmand, M., Borg, M., Bohlin, M. & Lisper, B. (2019). Machine Learning to Guide Performance Testing: An Autonomous Test Framework. In: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 2019: . Paper presented at ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 22 Apr 2019, Xi’an, China.
Åpne denne publikasjonen i ny fane eller vindu >>Machine Learning to Guide Performance Testing: An Autonomous Test Framework
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2019 (engelsk)Inngår i: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 2019, 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Satisfying performance requirements is of great importance for performance-critical software systems. Performance analysis to provide an estimation of performance indices and ascertain whether the requirements are met is essential for achieving this target. Model-based analysis as a common approach might provide useful information but inferring a precise performance model is challenging, especially for complex systems. Performance testing is considered as a dynamic approach for doing performance analysis. In this work-in-progress paper, we propose a self-adaptive learning-based test framework which learns how to apply stress testing as one aspect of performance testing on various software systems to find the performance breaking point. It learns the optimal policy of generating stress test cases for different types of software systems, then replays the learned policy to generate the test cases with less required effort. Our study indicates that the proposed learning-based framework could be applied to different types of software systems and guides towards autonomous performance testing.

Emneord
performance requirements, performance testing, test case generation, reinforcement learning, autonomous testing, Engineering and Technology, Teknik och teknologier, Computer Systems, Datorsystem
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-39327 (URN)10.1109/ICSTW.2019.00046 (DOI)2-s2.0-85068406208 (Scopus ID)
Konferanse
ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 22 Apr 2019, Xi’an, China
Tilgjengelig fra: 2019-07-04 Laget: 2019-07-04 Sist oppdatert: 2020-01-29bibliografisk kontrollert
Modeus, G., Sandgren, P., Borg, M., Andersson, F., Wiel-Berggren, G. & Rosendahl, M. (2019). Mjukvara är Sveriges nya infrastruktur: här är nästa steg.
Åpne denne publikasjonen i ny fane eller vindu >>Mjukvara är Sveriges nya infrastruktur: här är nästa steg
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2019 (svensk)Rapport (Annet vitenskapelig)
Abstract [en]

I Sverige bedriver hela 35 procent av företagen egen mjukvaruutveckling.1 Data och mjukvarahar kommit att genomsyra i stort sett hela näringslivet och den offentliga sektorn – både iden operativa driften, men även i utvecklings- och innovationsarbetet.Det här är en utveckling som har gått mycket snabbt, vilket illustreras väl av att utgifter förmjukvara hos företag i Sverige har fördubblats från 25 miljarder kr år 2014 till 50 miljarder kr år2019.2 Men samtidigt som digitaliseringen accelererar och efterfrågan på mjukvaruutvecklingoch nya datatjänster blir allt större så kräver en fortsatt hög innovationstakt att lagstiftningenoch utbildningsväsendet anpassar sig efter det fält där en allt större del av svensk tillväxt skapas

Publisher
s. 20
Serie
Teknikföretagen Swedsoft
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-39333 (URN)
Tilgjengelig fra: 2019-07-05 Laget: 2019-07-05 Sist oppdatert: 2019-07-05bibliografisk kontrollert
Henriksson, J., Berger, C., Borg, M., Tornberg, L., Sathyamoorthy, S. R. & Englund, C. (2019). Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks. In: 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): . Paper presented at 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 113-120).
Åpne denne publikasjonen i ny fane eller vindu >>Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
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2019 (engelsk)Inngår i: 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2019, s. 113-120Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Several areas have been improved with Deep Learning during the past years. For non-safety related products adoption of AI and ML is not an issue, whereas in safety critical applications, robustness of such approaches is still an issue. A common challenge for Deep Neural Networks (DNN) occur when exposed to out-of-distribution samples that are previously unseen, where DNNs can yield high confidence predictions despite no prior knowledge of the input. In this paper we analyse two supervisors on two well-known DNNs with varied setups of training and find that the outlier detection performance improves with the quality of the training procedure. We analyse the performance of the supervisor after each epoch during the training cycle, to investigate supervisor performance as the accuracy converges. Understanding the relationship between training results and supervisor performance is valuable to improve robustness of the model and indicates where more work has to be done to create generalized models for safety critical applications.

Emneord
deep-neural-networks, -robustness, -out-of-distribution, -automotive-perception
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-42595 (URN)10.1109/SEAA.2019.00026 (DOI)2-s2.0-85076012153 (Scopus ID)
Konferanse
2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
Tilgjengelig fra: 2020-01-10 Laget: 2020-01-10 Sist oppdatert: 2020-02-04bibliografisk kontrollert
Trubiani, C., Jamshidi, P., Cito, J., Shang, W., Jiang, Z. M. & Borg, M. (2019). Performance Issues?: Hey DevOps, Mind the Uncertainty!. IEEE Software, 36(2), 110-117, Article ID 8501933.
Åpne denne publikasjonen i ny fane eller vindu >>Performance Issues?: Hey DevOps, Mind the Uncertainty!
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2019 (engelsk)Inngår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 36, nr 2, s. 110-117, artikkel-id 8501933Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

DevOps is a novel trend that aims to bridge the gap between software development and operation teams. When applied to the performance evaluation process, it brings new challenges since developers need to be aware of the deployment settings and application runtime characteristics. At the operational stage, several uncertainties, e.g., workload fluctuations and resource availability, may affect the performance analysis. The goal of this paper is to identify the uncertain parameters and quantify their propagation to performance analysis results, in order to bring upfront the main system criticisms. To this end, we make use of a popular big data system showing that the sources of uncertainty may span on different characteristics and the performance analysis results can be heavily affected by these uncertainties. The paper contributes as an experience report aiming to better identify performance uncertainties through a case study. It provides a step-by-step guide to practitioners for controlling system uncertainties.

Emneord
DevOps, Performance Analysis, Software Development, Uncertainty, Big data, Software design, Software engineering, Development and operations, Performance evaluations, Resource availability, Sources of uncertainty, Uncertain parameters, Uncertainty analysis
HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-36541 (URN)10.1109/MS.2018.2875989 (DOI)2-s2.0-85055138945 (Scopus ID)
Tilgjengelig fra: 2018-11-27 Laget: 2018-11-27 Sist oppdatert: 2019-07-01bibliografisk kontrollert
Vogelsang, A. & Borg, M. (2019). Requirements Engineering for Machine Learning: Perspectives from Data Scientists.
Åpne denne publikasjonen i ny fane eller vindu >>Requirements Engineering for Machine Learning: Perspectives from Data Scientists
2019 (engelsk)Inngår i: Artikkel i tidsskrift (Fagfellevurdert) In press
Abstract [en]

Machine learning (ML) is used increasingly in real-world applications. In this paper, we describe our ongoing endeavor to define characteristics and challenges unique to Requirements Engineering (RE) for ML-based systems. As a first step, we interviewed four data scientists to understand how ML experts approach elicitation, specification, and assurance of requirements and expectations. The results show that changes in the development paradigm, i.e., from coding to training, also demands changes in RE. We conclude that development of ML systems demands requirements engineers to: (1) understand ML performance measures to state good functional requirements, (2) be aware of new quality requirements such as explainability, freedom from discrimination, or specific legal requirements, and (3) integrate ML specifics in the RE process. Our study provides a first contribution towards an RE methodology for ML systems.

HSV kategori
Identifikatorer
urn:nbn:se:ri:diva-40582 (URN)
Tilgjengelig fra: 2019-10-22 Laget: 2019-10-22 Sist oppdatert: 2019-12-04bibliografisk kontrollert
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-7879-4371
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