Use of ontology in identifying missing artefact links

The techniques of requirement traceability have evolved over recent years. However, as much as they have contributed to the software engineering field, significant ambiguity remains in many software engineering processes. This paper reports on an investigation of requirement traceability artefacts, stakeholders, and SDLC development models. Data were collected to gather evidence of artefacts and their properties from previous studies. The aim was to find the missing link between artefacts and their relationship to one another, the stakeholders, and SDLC models. This paper undertakes the first phase of the main research project, which aims to develop a framework for guiding software developers to actively manage traceability. After inquiring into and examining previous research on this topic, the links between artefacts and their functions were identified. The analysis resulted in the development of a new model for requirement traceability, defined in the form of an ontology portraying the contributively relations between software artefacts using common properties with the aid of Protégé Software. This study thus provides an important insight into the future of the requirement artefacts relation, and thereby lays an important foundation towards increasing our understanding of their potential and limitations

Designing for Collaboration Using Social Network Analysis: Towards a Conceptual Method to Understand Organisational Interaction

The spreading of innovation within organisations is an area of interest for both academics and practitioners. Within information systems research collaboration issues are often addressed and solved through implementation of technology artefacts to meditate communication. With more and more resources being spent on collaborative technologies we argue that there can be cost advantages in looking at the socio-technical aspects of the information system when trying improve organisational communication. As an initial step of information system interventions we argue that an overview of the information exchange network within organisations can lead to valuable insights into where to start and we argue that social network analysis can provide such an bird’s-eye view over organisational interaction. This leads us to our research question: How can social network analysis be used to describe, understand and explain organisational interaction in designing information systems for collaboration? Taking a design science approach to the research question we aim to construct a meta-artefact, i.e. in our case knowledge about how to design for collaboration with the help of social network analysis. To test the applicability of social network analysis we collect sociometric interaction data from a knowledge intensive organisation using a name generating survey. The usability of the visualisations that are the output of the social network analysis are evaluated by decision makers within the organisation through interviews. We conclude that social network analysis is a time-efficient method of collecting empirical data that can lead to deep insights into the structure of the organisational communication network. The visualisation can be seen as a map used to pinpoint the emergence of social networks within organisations and thereby acting as a tool to drive continuous change and innovation

Knowledge Graphs in Support of Credit Risk Assessment

An ontology is a formal and reusable knowledge structure that pertains to a specific domain of expertise. Building an ontology can be difficult. Consistency and completeness within the boundaries of the domain of expertise is required. Knowledge graphs are less complex to build. They remove the burden of specifying boundaries for the domain and reduce completeness and consistency requirements. They have been successful in facilitating knowledge reuse and maintenance. Adding knowledge continuously, in small localised chunks, is easier than the holistic engineering required for ontologies. In this paper, we exploit this to use knowledge graphs in combination with ontologies for transfer learning in machine learning. Through the use of knowledge graphs, data is extracted and transformed from one domain to another where data is lacking. This synthesized data is then used to support machine learning overcoming the lack of data. This approach is illustrated to support transfer learning in lending risk assessment. The approach provides a template for supporting data driven innovation as a finance company explores new markets and designs new products

A situational approach for the definition and tailoring of a data-driven software evolution method

Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance. To overcome this challenge, there is an increasing number of approaches that propose intensive use of data to drive evolution. This trend has motivated the SUPERSEDE method, which proposes the collection and analysis of user feedback and monitoring data as the baseline to elicit and prioritize requirements, which are then used to plan the next release. However, every company may be interested in tailoring this method depending on factors like project size, scope, etc. In order to provide a systematic approach, we propose the use of Situational Method Engineering to describe SUPERSEDE and guide its tailoring to a particular context.Peer ReviewedPostprint (author's final draft