Empirical studies to assess the understandability of data warehouse schemas using structural metrics

Serrano, Manuel A.; Calero, Coral; Sahraoui, Houari; Piattini, Mario

doi:10.1007/s11219-007-9030-7

Cited by 43 publications

(33 citation statements)

References 17 publications

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“…In addition, we only collected useful publicly available domain documents from the Internet from five main domains. Although these numbers are comparable to similar studies in the area of empirical software engineering (Genero et al 2008;Serrano et al 2008) and can provide a basis for understanding the behavior of the decision support platform, they may pose threats to the drawn conclusions. For instance, the use of a larger number of domain documents from a wider set of domains could impact the coverage and accuracy of our decision support platform.…”

Section: Conclusion Validitysupporting

confidence: 57%

Decision support for the software product line domain engineering lifecycle

2012

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Software product line engineering is a paradigm that advocates the reusability of software engineering assets and the rapid development of new applications for a target domain. These objectives are achieved by capturing the commonalities and variabilities between the applications of the target domain and through the development of comprehensive and variability-covering feature models. The feature models developed within the software product line development process need to cover the relevant features and aspects of the target domain. In other words, the feature models should be elaborate representations of the feature space of that domain. Given that feature models, i.e., software product line feature models, are developed mostly by domain analysts by sifting through domain documentation, corporate records and transcribed interviews, the process is a cumbersome and error-prone one. In this paper, we propose a decision support platform that assists domain analysts throughout the domain engineering lifecycle by: (1) automatically performing natural language processing tasks over domain documents and identifying important information for the domain analysts such as the features and integrity constraints that exist in the domain documents; (2) providing a collaboration platform around the domain documents such that multiple domain analysts can collaborate with each other during the process using a Wiki; (3) formulating semantic links between domain terminology with external widely used ontologies such as WordNet in order to disambiguate the terms used in domain documents; and (4) developing traceability links between the unstructured information available in the domain documents and Autom Softw Eng (2012) 19:335-377 their formal counterparts within the formal feature model representations. Results obtained from our controlled experimentations show that the decision support platform is effective in increasing the performance of the domain analysts during the domain engineering lifecycle in terms of both the coverage and accuracy measures.

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Section: Conclusion Validitysupporting

confidence: 57%

Decision support for the software product line domain engineering lifecycle

2012

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“…We have tried to cover as many structural characteristics of a feature model as possible. To achieve this, structural metrics proposed in the areas of object-oriented design, entity-relationship diagrams, and even business process models have been considered; the empirical results of which were taken into account as guidelines for designing the most useful set of structural metrics for our purpose (Genero et al 2008;Cruz-Lemus et al 2009Serrano et al 2008). In addition, based on Ocam's Razor, and also empirical results (e.g., although LOC is a naïve and simple measure, it performs well in some circumstances ) the design of simple metrics has been our secondary objective.…”

Section: Proposed Structural Metricsmentioning

confidence: 99%

“…Subsequently, these subjective values are used as measures for the dependent variables (Cruz-Lemus et al 2009;Genero et al 2008). The second approach is to define a set of tasks for the experts and then record the time taken for them to complete the tasks (time-to-complete), which would serve as an indication for the dependent variable (Cruz-Lemus et al 2010;Serrano et al 2008). We do not use this second approach for three reasons: (1) we are focused on understanding the relation between the subjective perception of model analysts about maintainability and the metric values.…”

Section: Dependent Variablesmentioning

confidence: 99%

Assessing the maintainability of software product line feature models using structural metrics

2011

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A software product line is a unified representation of a set of conceptually similar software systems that share many common features and satisfy the requirements of a particular domain. Within the context of software product lines, feature models are treelike structures that are widely used for modeling and representing the inherent commonality and variability of software product lines. Given the fact that many different software systems can be spawned from a single software product line, it can be anticipated that a low-quality design can ripple through to many spawned software systems. Therefore, the need for early indicators of external quality attributes is recognized in order to avoid the implications of defective and low-quality design during the late stages of production. In this paper, we propose a set of structural metrics for software product line feature models and theoretically validate them using valid measurement-theoretic principles. Further, we investigate through controlled experimentation whether these structural metrics can be good predictors (early indicators) of the three main subcharacteristics of maintainability: analyzability, changeability, and understandability. More specifically, a four-step analysis is conducted: (1) investigating whether feature model structural metrics are correlated with feature model maintainability through the employment of classical statistical correlation techniques; (2) understanding how well each of the structural metrics can serve as discriminatory references for maintainability; (3) identifying the sufficient set of structural metrics for evaluating each of the subcharacteristics of maintainability; and (4) evaluating how well different prediction models based on the proposed structural metrics can perform in indicating the maintainability of a feature model. Results obtained from the controlled experiment support the idea that useful prediction models can be built for the purpose of evaluating feature model maintainability using early structural metrics. Some of the structural metrics show significant correlation with the subjective perception of the subjects about the maintainability of the feature models.

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“…All the measures belonged to either ordinal or superior scale. Serrano et al [11] conducted an experiment to assess the relationship between four schema level measures put forth by Calero et al [3] and conceptual model understandability. They also corroborated the metrics using Briand's property based framework [5] and the measurement theory based frameworks of Zuse [8] and Poels and Dedene [7].…”

Section: Related Workmentioning

confidence: 99%

Formal Validation of Data Warehouse Complexity Metrics using Distance Framework

Aggarwal¹,

Sabharwal²

2017

IJISA

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Abstract-DataWarehouse is the cornerstone for organizations that base their strategic decisions on the large scale processing of numerical data. The success of the organization depends on these decisions and hence it becomes extremely important to have a quality data warehouse. Conceptual models have been widely recognized as a key determinant of data warehouse quality during the early stages of design. Recently, metrics have been proposed by authors based on hierarchies to quantify the complexity and inturn quality of the conceptual models of data warehouse. They have formally corroborated the measures against Briand's property based framework to ensure their validity. However, Briand's set of properties for software measures are a set of necessary but not sufficient measure axioms. They are advantageous to refute software metrics but not to validate them. Thus, we focus on the theoretical validation of the data warehouse conceptual model metrics using the Distance framework whose sufficiency is ensured by the measurement theory. The results indicate that the metrics are valid measures of the complexity of data warehouse conceptual models. Besides, validation by Distance framework assures that the metrics are in the ratio scale which further aids in data analysis.

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Empirical studies to assess the understandability of data warehouse schemas using structural metrics

Cited by 43 publications

References 17 publications

Decision support for the software product line domain engineering lifecycle

Decision support for the software product line domain engineering lifecycle

Assessing the maintainability of software product line feature models using structural metrics

Formal Validation of Data Warehouse Complexity Metrics using Distance Framework

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