Using architecture models for runtime adaptability

Floch, Jacqueline; Hallsteinsen, Svein; Stav, Erlend; Eliassen, Frank; Lund, Ketil; Gjørven, Eli

doi:10.1109/ms.2006.61

Cited by 250 publications

(167 citation statements)

References 8 publications

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“…The second loop of Algorithm 1 (lines [13][14][15][16][17][18][19][20][21][22][23][24][25] iterates through the class and activity diagram differences. In the worst case, (1) every statement is represented as a separate action node, in the activity diagram, (2) all original nodes are modified/deleted, (3) all transition flows between all nodes are modified/deleted, (4) all constructors are modified/deleted, and (5) all fields in the class diagram are modified/deleted.…”

Section: Time Complexity For the Extended Martsmentioning

confidence: 99%

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Supporting inheritance hierarchy changes in model-based regression test selection

2017

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Models can be used to ease and manage the development, evolution, and runtime adaptation of a software system. When models are adapted, the resulting models must be rigorously tested. Apart from adding new test cases, it is also important to perform regression testing to ensure that the evolution or adaptation did not break existing functionality. Since regression testing is performed with limited resources and under time constraints, regression test selection (RTS) techniques are needed to reduce the cost of regression testing. Applying modellevel RTS for model-based evolution and adaptation is more convenient than using code-level RTS because the test selection process happens at the same level of abstraction as that of evolution and adaptation.In earlier work, we proposed a model-based RTS approach called MaRTS to be used with a fine-grained model-based adaptation framework that targets applications implemented in Java. MaRTS uses UML models consisting of class and activity diagrams. It classifies test cases as obsolete, reusable, or retestable based on changes made to UML class and activity diagrams of the system being adapted. However, MaRTS did not take into account the changes made to the inheritance hierarchy in the class diagram and the impact of these changes on the selection of test cases. This paper extends MaRTS to support such changes, and demonstrates that the extended approach performs as well as or better than code-based RTS approaches in safely selecting regression test cases. While MaRTS can generally be used during any model-driven development or model-based evolution activity, we have developed it in the context of runtime adaptation. We evaluated the extended MaRTS on a set of applications, and compared the results with code-based RTS approaches that also support changes to the inheritance hierarchy. The results showed that the extended MaRTS selected all the test cases relevant to the inheritance hierarchy changes, and that the fault detection ability of the selected test cases was never lower than that of the baseline test cases. The extended MaRTS achieved comparable results to a graph-walk code-based RTS approach (DejaVu), and showed a higher reduction in the number of selected test cases when compared with a static analysis code-based RTS approach (ChEOPSJ).

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Section: Time Complexity For the Extended Martsmentioning

confidence: 99%

“…Most existing model-based adaptation approaches are coarse-grained and focus on using models at runtime to support self-adaptation in autonomous systems [10,15,18,19,26,34]. Adaptations in these approaches are performed at the component level, and are limited to adding/removing/reconnecting components of the software system.…”

Section: Introductionmentioning

confidence: 99%

Supporting inheritance hierarchy changes in model-based regression test selection

2017

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“…Consequently, our approach enabled the robot system to perform its services more reliably. Floch et al (Floch et al, 2006) proposed a utility-based adaptation scheme. This approach assumed that an adaptable application operates on the adaptation middleware that was previously proposed (Hallsteinsen et al, 2004), and the middleware monitors the current user and system context.…”

Section: Case Studymentioning

confidence: 99%

A Genetic Algorithm-based Approach to Dynamic Architectural Deployment

Kim¹

2010

Human-Robot Interaction

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“…MUSIC is based on an externalized approach to self-adaptation where the adaptation logic is delegated to generic middleware. The middleware exploits knowledge about the composition and quality of service (QoS) characteristics of its constituting components and services used [11], [12], [13]. The middleware monitors relevant context parameters, including 3 rd party services available in the environment, and adapts the active applications by reconfiguring internal components and binding to 3 rd party services when changes occur.…”

Section: Self-adaptation Middlewarementioning

confidence: 99%

“…This process is triggered at start-up of the application and at run-time when the middleware discovers relevant context changes. The planning middleware iterates over all possible bindings of the roles, computes the Predicted Properties and the utility [13]. The utility function of an application is provided by the developer and is typically expressed as a weighted sum of dimensional utilities where the weights express user preferences (i.e., relative importance of a dimension to the user).…”

Section: Self-adaptation Middlewarementioning

confidence: 99%

Software Architecture for Self-Adapting Sub-sea Sensor Networks: Work in Progress

Hallsteinsen

Sanders

2009

2009 Third International Conference on Sensor Technologies and Applications

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Abstract-Monitoring of the sub-sea environment requires advanced sensor networks with both stationary and mobile nodes on the surface and underwater, each node playing a different role. The combination of mobile and stationary nodes, the loss of nodes due to harsh conditions, the difficult conditions for wireless sub-sea communication and the need for nodes to collaborate are all factors that require new approaches to software architectures for such nodes and systems. This articles presents work in progress that sets out to address these issues by adapting approaches from software engineering solutions to ubiquitous computing.

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Using architecture models for runtime adaptability

Cited by 250 publications

References 8 publications

Supporting inheritance hierarchy changes in model-based regression test selection

Supporting inheritance hierarchy changes in model-based regression test selection

A Genetic Algorithm-based Approach to Dynamic Architectural Deployment

Software Architecture for Self-Adapting Sub-sea Sensor Networks: Work in Progress

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