Providing Foundation for User Feedback Concepts by Extending a Communication Ontology

Morales-Ramirez, Itzel; Perini, Anna; Guizzardi, Renata S. S.

doi:10.1007/978-3-319-12206-9_25

Cited by 5 publications

(3 citation statements)

References 15 publications

(13 reference statements)

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“…Several tables have been developed to populate controlled vocabularies, e.g., target types, feedback status and feedback types. The feedback types indicates the possible intentions of a data consumer to provide feedback, and these are compiled from existing literature (Schneider, 2011;Morales-Ramirez et al, 2014;Pagano and Maalej, 2013) on feedback from software users. Examples are comment (recommendations and references to other related sources), requirement (new feature and content request, shortcomings and discovered issues), clarification request, rating and user experience.…”

Section: Preliminary Results Obtainedmentioning

confidence: 99%

Capturing data provenance with a user-driven feedback approach

2015

Weber, T., McPhee, M.J. And Anderssen, R.S. (Eds) MODSIM2015, 21st International Congress on Modelling and Simulation

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Various portals have been developed to provide an easy way to discover and access public research data sets from various organizations. Data sets are made available with descriptive metadata based on common (e.g., OGC, CUAHSI, FGDC, INSPIRE, ISO, Dublin Core) or proprietary standards to facilitate better understanding and use of the data sets. Provenance descriptions may be included as part of the metadata and are specified from a data provider's perspective. These can include, for example, different entities and activities involved in a data creation flow, such as sensing platforms, personnel, and data calculation and transformation processes. Moving beyond the provider-centric descriptions, data provenance may be complemented with forward provenance records supplied by data consumers. The records may be gathered via a user-driven feedback approach. The feedback information from data consumers gives valuable insights into application and assessment of published data sets. This might include descriptions about a scientific analysis in which the data sets were used, the corrected version of an actual data set or any discovered issues and suggestions concerning the quality of the published data sets. Data providers might then use this information to handle erroneous data and improve existing metadata, their data collection and processing methods. Contributors can use the feedback channel to share their scientific analyses. Data consumers can learn more about data sets based on other people's experiences, and potentially save time by avoiding the need for interpreting or cleaning data sets. The goals of the study are to capture feedback from data users on published research data sets, link this to actual data sets, and finally support search and discovery of research data using feedback information. This paper reports preliminary results addressing the goals. We provide a summary of current practices on gathering feedback from end-users on research data portals, and discuss their relevance and limitations. Examples from the Earth Science domain on how commentaries from data users might be useful in practice are also included. Then, we present a data model representing key aspects of user feedback. We propose a system architecture to gather and manage feedback from end-users. We describe how the core PROV model may be used to represent the provenance of user feedback information. Technical solutions for linking feedback to existing data portals are also specified.

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Section: Preliminary Results Obtainedmentioning

confidence: 99%

Capturing data provenance with a user-driven feedback approach

2015

Weber, T., McPhee, M.J. And Anderssen, R.S. (Eds) MODSIM2015, 21st International Congress on Modelling and Simulation

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“…In [4], four clusters of user types are presented by their attitude towards providing feedback (see Figure 1), differing in such factors as openness towards being asked or reminded to provide feedback; the extent to which privacy outweighs allowing (anonymized) data mining, and whether feedback is provided out of an intrinsic motivation or because of social factors. Finally, user feedback was characterized in [8], while [9] provides a user feedback ontology to clarify the concepts of this domain.…”

Section: Related Work: Conceptualizationmentioning

confidence: 99%

Towards Crowd-Based Requirements Engineering A Research Preview

Groen

Doerr

Adam

2015

Requirements Engineering: Foundation for Software Quality

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Abstract. [Context and motivation]Stakeholders who are highly distributed form a large, heterogeneous online group, the so-called "crowd". The rise of mobile, social and cloud apps has led to a stark increase in crowd-based settings. [Question/problem] Traditional requirements engineering (RE) techniques face scalability issues and require the co-presence of stakeholders and engineers, which cannot be realized in a crowd setting. While different approaches have recently been introduced to partially automate RE in this context, a multi-method approach to (semi-)automate all RE activities is still needed. [Principal ideas/results]We propose "Crowd-based Requirements Engineering" as an approach that integrates existing elicitation and analysis techniques and fills existing gaps by introducing new concepts. It collects feedback through direct interactions and social collaboration, and by deploying mining techniques.[Contribution] This paper describes the initial state of the art of our approach, and previews our plans for further research. IntroductionOffering services and applications online opens the way to a potentially large market, but competition is high in this field. This pressures developers and service providers into continuously exciting their customers with positive interactions and innovations in order to prevent them from switching to competitive solutions. Requirements engineering (RE) plays a pivotal role in mapping and anticipating the stakeholders' needs. However, traditional RE techniques depend on co-presence (i.e., on the analyst(s) and stakeholder(s) gathered at the same time and place) and therefore do not scale well to settings with many distributed stakeholders [1]. As online users are physically distributed, remote RE techniques are required that allow the analyst(s) and the many stakeholders to be active in different places and at different times [2]. Existing techniques in the area of remote RE techniques rely on (semi-)automating aspects of RE (e.g., [2,3]), but the greatest challenge is to bridge the gaps between them in an integrated approach. We argue in this paper that research has so far focused on particular sub-domains and has not approached this field holistically

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“…In a previous work, we described the methodology we followed for the ontology development (Guizzardi et al, 2014), which takes advantage of a goal oriented approach described by Fernandes et al (2011) for defining the competency questions to be answered by the ontology and the concepts to be considered for its scope. Moreover, in another work we introduced the ontology at an initial stage and how we built it (Morales-Ramirez et al, 2014b), by exploiting the Unified Foundational Ontology (UFO) by Guizzardi (2005).…”

Section: Introductionmentioning

confidence: 99%

An ontology of online user feedback in software engineering

Morales-Ramirez

Perini

Guizzardi³

2015

Self Cite

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Online user feedback is principally used as an information source for evaluating customers' satisfaction for a given goods, service or software application. The increasing attitude of people towards sharing comments through the social media is making online user feedback a resource containing different types of valuable information. The huge amount of available user feedback has drawn the attention of researchers from different fields. For instance, data mining techniques have been developed to enable information extraction for different purposes, or the use of social techniques for involving users in the innovation of services and processes. Specifically, current research and technological efforts are put into the definition of platforms to gather and/or analyze multi-modal feedback. But we believe that the understanding of the type of concepts instantiated as information contained in user feedback would be beneficial to define new methods for its better exploitation. In our research, we focus on online explicit user feedback that can be considered as a powerful means for user-driven evolution of software services and applications. Up to our knowledge, a conceptualization of user feedback is still missing. With the purpose of contributing to fill up this gap we propose an ontology, for explicit online user feedback that is founded on a foundational ontology and has been proposed to describe artifacts and processes in software engineering. Our contribution in this paper concerns a novel user feedback ontology founded on a Unified Foundational Ontology (UFO) that supports the description of analysis processes of user feedback in software engineering. We describe the ontology together with an evaluation of its quality, and discuss some application scenarios.

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Providing Foundation for User Feedback Concepts by Extending a Communication Ontology

Cited by 5 publications

References 15 publications

Capturing data provenance with a user-driven feedback approach

Capturing data provenance with a user-driven feedback approach

Towards Crowd-Based Requirements Engineering A Research Preview

An ontology of online user feedback in software engineering

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