Adoption of Free Libre Open Source Software (FLOSS): A Risk Management Perspective

Kenett, Ron S.; Franch, Xavier; Susi, Angelo; Galanis, Nikolas

doi:10.1109/compsac.2014.25

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…In our proposal, we have requirements elicitation and risk identification performed in Early Requirements and the creation of the models for WIS that use Linked Data in Late Requirements. Kenett et al (2014) propose capturing, filtering, analyzing and reasoning about risks, based on RISCOSS, using a three layered approach to risk management in FLOSS (Free Libre Open Source Software) projects. In the first layer, raw data is collected from FLOSS communities and projects; in the second layer risk indicators are defined and models are produced, in which the risks can be linked to the objectives; finally, in the third layer the risks indicators are converted in Business Risks and, linked with iStar, model business goals to see how risks impact them.…”

Section: Related Workmentioning

confidence: 99%

GRALD: an Approach for Goal and Risk Analysis in the Development of Information Systems for the Web of Data

Freitas

Guizzardi

Souza

2019

JSERD

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The publication of Linked Data on the Web regarding several application domains leads to new problems related to Requirements Engineering, which needs to take into account aspects related to new ways of developing systems and delivering information integrated with the Web of Data. Tasks such as (functional and non-functional) requirements elicitation and ontology-based conceptual modeling can be applied to the development of systems that publish Linked Data, in order to obtain a better shared conceptualization (i.e., a domain ontology) of the published data. The use of vocabularies is an intrinsic activity when publishing or consuming Linked Data and their choice can be supported by the elicited requirements and domain ontology. However, it is important to assess the risk when choosing external vocabularies, as their use can lead to problems, such as misinterpretation of meanings due to poor documentation, connection timeouts due to infrastructure problems, etc. Thus, risk identification, modeling and analysis techniques can be employed, in order to identify risks and their impacts on stakeholder goals. In this work, we propose GRALD: Goals and Risks Analysis for Linked Data, an approach for modeling goals and risks for information systems for the Web of Data.

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Section: Related Workmentioning

confidence: 99%

GRALD: an Approach for Goal and Risk Analysis in the Development of Information Systems for the Web of Data

Freitas

Guizzardi

Souza

2019

JSERD

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“…The central concept in i* is that of intentional actor. Intentional properties of an agent such as goals, beliefs, abilities and commitments are used in modelling requirements [2], [14]. The actor or agent construct is used to identify the intentional characteristics represented as dependencies involving goals to be achieved, tasks to be performed, resources to be furnished or soft goals (optimization objectives or preferences) to be satisfied.…”

Section: The I* Modelling Frameworkmentioning

confidence: 99%

Integration of Organisational Models and UML Use Case

Bhuiyan¹

2018

JCP

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The fundamental step in systems implementation is requirements gathering. This step is categorized into early phase requirements elicitation and systems modelling. Requirement elicitation involves gathering stakeholders' intentions, goals, rationale and organizational context that can be modelled with agent-oriented conceptual modelling such as i*. On the other hand, systems' modelling is portraying abstract version of interaction among interrelated components within a whole system via UML languages such as Use Case diagrams. The co-evolution of Agent-Oriented conceptual model such as i* and Use Case Diagrams aims to close the gap of capturing organizational requirements and system requirements. However, changes made in i* must be reflected in the Use Case diagram to ensure consistency in requirements. Therefore, this paper proposes a methodology supporting the co-evolution of these two otherwise disparate approaches in a synergistic fashion.

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“…Other risk impact factors also include project size, organizational impact, and complexity of project dependencies within an organization and technological compatibilities and architectures [2], [14]. Underestimating technical risk on integration is a major challenge when adopting OSS [21]. Culture can also be a risk factor in OSS adoption because if internal personnel are not open to the implementation of OSS, it will increase the time spent training users to adopt the system and consequently affecting the cost of implementation [15].…”

Section: Risk In Adoption Of Ossmentioning

confidence: 99%

“…Adding to that, the level of interest in the project suggests better quality and compliance [22]. There are two types of ecosystems related to OSS [21]:…”

Section: Oss Evaluation Criteriamentioning

confidence: 99%

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Risk Framework for Open Source Applications Using Agent Oriented Modelling

Teh¹,

Bhuiyan²,

Prasad³

et al. 2016

JSW

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This paper aims to propose a novel risk framework utilizing agent oriented modelling in order to provide guidelines for the adoption of Open Source Software (OSS) solutions to an organization. Multiple studies have focused on user requirements and technical aspects when implementing OSS solutions. However, there is a lack of literature focusing on the social aspects of 'why' and 'how' risks of OSS adoption can impact the overall organizational goals and the OSS ecosystem. Therefore, another focus area of this work is to bridge the gap between actors (goals, softgoals, task, and resources) and risk factors when adopting OSS in the organization by proposing new risk measurement and risk prioritization methods. This study is based on literature survey and a case study on OSS adoption in an organization. The research results show that the proposed risk measurement validates the current methods while providing deeper insights into the criticality and strong relationship between actors which in turn assists in proposing appropriate risk response strategies such as delegation of risk among these actors. The result implies that taking ecosystems into consideration is important for risk mitigation.

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Adoption of Free Libre Open Source Software (FLOSS): A Risk Management Perspective

Cited by 12 publications

References 10 publications

GRALD: an Approach for Goal and Risk Analysis in the Development of Information Systems for the Web of Data

GRALD: an Approach for Goal and Risk Analysis in the Development of Information Systems for the Web of Data

Integration of Organisational Models and UML Use Case

Risk Framework for Open Source Applications Using Agent Oriented Modelling

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