How Has the Health of Software Ecosystems Been Evaluated?

Amorim, Simone da Silva; Neto, Félix Simas S.; McGregor, John D.; Almeida, Eduardo Santana de; Chávez, Christina

doi:10.1145/3131151.3131174

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…CESs can form collaborative system groups that function in the context of an ecosystem and influence the health of the ecosystem and its participants. The health of an ecosystem is a measure that describes the business opportunities of the ecosystem and its participants [40].…”

Section: Automotive Smart Ecosystemsmentioning

confidence: 99%

Simulation methods and tools for collaborative embedded systems: with focus on the automotive smart ecosystems

Cioroaica¹,

Pudlitz²,

Gerostathopoulos³

et al. 2019

SICS Softw.-Inensiv. Cyber-Phys. Syst.

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Embedded Systems are increasingly equipped with open interfaces that enable communication and collaboration with other embedded systems. Collaborative Embedded Systems (CES) can be seen as an emerging new class of systems which, although individually designed and developed, can form collaborations at runtime. When embedded systems collaborate with each other, functions developed independently need to be integrated for performing evaluation of the resulting system in order to discover unwanted side-effects. Traditionally, early-stage validation and verification (V&V) of systems composed of collaborative subsystems is performed by function integration at design time. Simulation is used at this stage to verify system's behaviour in a predefined set of test scenarios. In this paper we provide a survey of simulation methods and tools for the V&V of CES. In the context of one use case from the automotive domain (vehicle platooning) we present solutions (methods and tools) and challenges brought by evaluating vehicle collaboration using simulation.

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Section: Automotive Smart Ecosystemsmentioning

confidence: 99%

Simulation methods and tools for collaborative embedded systems: with focus on the automotive smart ecosystems

Cioroaica¹,

Pudlitz²,

Gerostathopoulos³

et al. 2019

SICS Softw.-Inensiv. Cyber-Phys. Syst.

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“…Only through a self-regulating mechanism that enables continuous scrutiny of its health, an ecosystem can grow well. The health of an ecosystem is an indicator of how well the business performs [5].…”

Section: Introductionmentioning

confidence: 99%

Goals within Trust-based Digital Ecosystems

Cioroaica

Purohit

Bühnová

et al. 2021

2021 IEEE/ACM Joint 9th International Workshop on Software Engineering for Systems-of-Systems and 15th Workshop on Distributed

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Within a digital ecosystem, systems and actors form coalitions for achieving common and individual goals. In a constant motion of collaborative and competitive forces and faced with the risk of malicious attacks, ecosystem participants require strong guarantees of their collaborators' trustworthiness. Evidence of trustworthy behavior derived from runtime executions can provide these trust guarantees, given that clear definition and delimitation of trust concerns exist. Without them, a base for negotiating expectations, quantifying achievements and identifying strategical attacks cannot be established and attainment of strategic benefits relies solely on vulnerable collaborations. In this paper we examine the relationship between goals and trust and we introduce a formalism for goal representation. We delimit the trust concerns with anti-goals. The anti-goals set the boundaries within which we structure the trust analysis and build up evidence for motivated attacks.

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“…To guarantee the health of OSSEs, health measure is the most basic step [8]. Some researchers have attempted to create their own operationalization, but these typically get stuck in the concept phase [10], [11]. Therefore, it is still difficult to really measure the health status of an OSSE from its conceptual level to quantitative study.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is still difficult to really measure the health status of an OSSE from its conceptual level to quantitative study. However, there are many other factors that affect the health of open-source software, which makes it a challenging work to measure the health of OSSEs [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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Toward the Health Measure for Open Source Software Ecosystem Via Projection Pursuit and Real-Coded Accelerated Genetic

2019

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The benign development of Open-source Software Ecosystem (or OSSE) helps to fuse the wisdom of the community. It can facilitate the development and solve the urgent application needs of large-scale complex software systems. To guarantee that an OSSE is stable and effective for supporting the application development, health assessment for an OSSE has become a research hotspot. In this paper, starting from a new perspective, the OSSE is compared with the ecosystem in the natural world. An OSSE health measure method is proposed by integrating projection pursuit and real-coded accelerated genetic algorithm. First, according to the snowball sampling data collection method and the grounded theory, the data is collected and processed. Second, by designing evaluation indicators and utility functions, the projection pursuit classification model of the natural ecosystem is evaluated and combined with a real-coded accelerated genetic algorithm, thereby designing the health measure model. The experimental results suggest the effectiveness of the proposed approach.INDEX TERMS Open-source software ecosystem, health measure, projection pursuit model, real-coded accelerated genetic algorithm.

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How Has the Health of Software Ecosystems Been Evaluated?

Cited by 17 publications

References 13 publications

Simulation methods and tools for collaborative embedded systems: with focus on the automotive smart ecosystems

Simulation methods and tools for collaborative embedded systems: with focus on the automotive smart ecosystems

Goals within Trust-based Digital Ecosystems

Toward the Health Measure for Open Source Software Ecosystem Via Projection Pursuit and Real-Coded Accelerated Genetic

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