Error propagation in software architectures

Abdelmoez, Walid; Nassar, D.M.; Shereshevsky, M.; Gradetsky, N.; Gunnalan, R.; Ammar, H.H.; Mili, Ali

doi:10.1109/metric.2004.1357923

Cited by 62 publications

(56 citation statements)

References 14 publications

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“…Only 1% of the measures provided probabilistic results. For example, Shaik [52] and Abdelmoez et al [1] calculate the Change propagation probability and the Error Propagation Probability of product line architectures, respectively. While having many measures with exact results is beneficial, we think that it would be advantageous to have probabilistic measures which had different results as a consequence of several parameters.…”

Section: Criterion 4 Results Of the Measurementioning

confidence: 99%

“…Abdelmoez et al [1] measure the probability of an error that arises in one component being propagated to other components, using the Error Propagation measures.…”

Section: Criterion 1 Quality Characteristic Evaluated By the Measurementioning

confidence: 99%

“…Section 2 presents the background, including a discussion about the specific characteristics of SPLs and existing systematic literature 1 In this study, we use the terms measure and metric as synonyms since the ISO/IEC 9126 standard [27] uses the term metric to refer to the defined measurement method and the measurement scale whereas the new standard for Software Product Quality Requirements and Evaluation (SQuaRE) [26] uses the term measure to refer to a variable to which a value is assigned as the result of measurement. reviews in the field.…”

Section: Introductionmentioning

confidence: 99%

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A systematic review of quality attributes and measures for software product lines

2011

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It is widely accepted that software measures provide an appropriate mechanism for understanding, monitoring, controlling and predicting the quality of software development projects. In Software Product Lines (SPL), quality is even more important than in a single software product since, owing to systematic reuse, a fault or an inadequate design decision could be propagated to several products in the family. Over the last few years, a great number of quality attributes and measures for assessing the quality of SPL have been reported in literature. However, no studies summarizing the current knowledge about them exist. This paper presents a systematic literature review with the objective of identifying and interpreting all the available studies from 1996 to date that present quality attributes and/or measures for SPL. These attributes and measures have been classified using a set of criteria that includes the life cycle phase in which the measures are applied; the corresponding quality characteristics; their support for specific SPL characteristics (e.g., variability, compositionality); the procedure used to validate the measures, etc. We found 165 measures related to 98 different quality attributes. The results of the review indicated that 92% of the measures evaluate attributes that are related to maintainability. In addition, 67% of the measures are used during the design phase of Domain Engineering, and 56% are applied to evaluate the product line architecture. However, only 25% of them have been empirically validated.In conclusion, the results provide a global vision of the state of the research within this area in order to help researchers in detecting weaknesses, directing research efforts, and identifying new research lines. In particular, there is a need for new measures with which to evaluate both the quality of the artifacts produced during the entire SPL life cycle and other quality characteristics. There is also a need for more validation (both theoretical and empirical) of existing measures. In addition, our results may be useful as a reference guide for practitioners to assist them in the selection or the adaptation of existing measures for evaluating their software product lines.

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Section: Criterion 4 Results Of the Measurementioning

confidence: 99%

“…Abdelmoez et al [1] measure the probability of an error that arises in one component being propagated to other components, using the Error Propagation measures.…”

Section: Criterion 1 Quality Characteristic Evaluated By the Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A systematic review of quality attributes and measures for software product lines

2011

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“…Moreover, it is proposed a method for the identification of which modules are more likely exposed to propagated errors and which modules more likely produce severe consequences on the global system, considering the propagation path of their own failure. In [35,34,33] approaches based on fault injection to estimate the error propagation characteristics of a software system during testing are presented. In the context of safety some works exist dealing with multiple failure modes, see for example [20].…”

Section: Related Workmentioning

confidence: 99%

A compositional method for reliability analysis of workflows affected by multiple failure modes

Distefano

Filieri

Ghezzi

et al. 2011

Proceedings of the 14th International ACM Sigsoft Symposium on Component Based Software Engineering

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We focus on reliability analysis for systems designed as workflowbased compositions of components. Components are characterized by their failure profiles, which take into account possible multiple failure modes. A compositional calculus is provided to evaluate the failure profile of a composite system, given failure profiles of the components. The calculus is described as a syntax-driven procedure that synthesizes a workflow's failure profile. The method is viewed as a design-time aid that can help software engineers reason about system's reliability in the early stage of development. A simple case study is presented to illustrate the proposed approach.

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“…Moreover, it is proposed a method for the identification of which modules are more likely exposed to propagated errors and which modules more likely produce severe consequences on the global system, considering the propagation path of their own failure. In [22][23][24] approaches based on fault injection to estimate the error propagation characteristics of a software system during testing are presented. In the context of safety some works exist dealing with multiple failure modes, see for example [25].…”

Section: Related Workmentioning

confidence: 99%

Reliability Analysis of Component-Based Systems with Multiple Failure Modes

Filieri

Ghezzi

Grassi

et al. 2010

Component-Based Software Engineering

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Abstract. This paper presents a novel approach to the reliability modeling and analysis of a component-based system that allows dealing with multiple failure modes and studying the error propagation among components. The proposed model permits to specify the components attitude to produce, propagate, transform or mask different failure modes. These component-level reliability specifications together with information about systems global structure allow precise estimation of reliability properties by means of analytical closed formulas, probabilistic modelchecking or simulation methods. To support the rapid identification of components that could heavily affect systems reliability, we also show how our modeling approach easily support the automated estimation of the system sensitivity to variations in the reliability properties of its components. The results of this analysis allow system designers and developers to identify critical components where it is worth spending additional improvement efforts.

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Error propagation in software architectures

Cited by 62 publications

References 14 publications

A systematic review of quality attributes and measures for software product lines

A systematic review of quality attributes and measures for software product lines

A compositional method for reliability analysis of workflows affected by multiple failure modes

Reliability Analysis of Component-Based Systems with Multiple Failure Modes

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