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Mario Piattini - One of the best experts on this subject based on the ideXlab platform.
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the impact of structural complexity on the Understandability of uml statechart diagrams
Information Sciences, 2010Co-Authors: Jose A Cruzlemus, Marcela Genero, Ann Maes, Geert Poels, Mario PiattiniAbstract:The effectiveness of current software development strategies, such as Model-Driven Development (MDD), depends largely on the quality of their primary artefacts, i.e. software models. As the standard modelling language for software systems is the Unified Modelling Language (UML), quality assurance of UML models is a major research field in Computer Science. Understandability, i.e. a model's ability to be easily understood, is one model quality property that is currently heavily under investigation. In particular, researchers are searching for the factors that determine an UML model's Understandability and are looking for ways to manipulate these factors. This paper presents an empirical study investigating the effect that structural complexity has on the Understandability of one particular type of UML model, i.e. the statechart diagram. Based on data collected in a family of three experiments, we have identified three dimensions of structural complexity that affect Understandability: (i) the size and control flow complexity of the statechart in terms of features such as the number of states, events, guards and state transitions; (ii) the actions that are performed when entering or leaving a state; (iii) the sequence of actions that is performed while staying within a state. Based on these structural complexity dimensions we have built an Understandability prediction model using a regression technique that is specifically recommended for data obtained through a repeated measures design. Our test results show that each of the underlying structural complexity dimensions has a significant impact on the Understandability of a statechart diagram.
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Empirical studies to assess the Understandability of data warehouse schemas using structural metrics
Software Quality Journal, 2008Co-Authors: Manuel Angel Serrano, Coral Calero, Houari A. Sahraoui, Mario PiattiniAbstract:Data warehouses are powerful tools for making better and faster decisions in organizations where information is an asset of primary importance. Due to the complexity of data warehouses, metrics and procedures are required to continuously assure their quality. This article describes an empirical study and a replication aimed at investigating the use of structural metrics as indicators of the Understandability, and by extension, the cognitive complexity of data warehouse schemas. More specifically, a four-step analysis is conducted: (1) check if individually and collectively, the considered metrics can be correlated with schema Understandability using classical statistical techniques, (2) evaluate whether Understandability can be predicted by case similarity using the case-based reasoning technique, (3) determine, for each level of Understandability, the subsets of metrics that are important by means of a classification technique, and assess, by means of a probabilistic technique, the degree of participation of each metric in the Understandability prediction. The results obtained show that although a linear model is a good approximation of the relation between structure and Understandability, the associated coefficients are not significant enough. Additionally, classification analyses reveal respectively that prediction can be achieved by considering structure similarity, that extracted classification rules can be used to estimate the magnitude of Understandability, and that some metrics such as the number of fact tables have more impact than others.
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defining and validating metrics for assessing the Understandability of entity relationship diagrams
Data and Knowledge Engineering, 2008Co-Authors: Marcela Genero, Geert Poels, Mario PiattiniAbstract:Database and data model evolution cause significant problems in the highly dynamic business environment that we experience these days. To support the rapidly changing data requirements of agile companies, conceptual data models, which constitute the foundation of database design, should be sufficiently flexible to be able to incorporate changes easily and smoothly. In order to understand what factors drive the maintainability of conceptual data models and to improve conceptual modelling processes, we need to be able to assess conceptual data model properties and qualities in an objective and cost-efficient manner. The scarcity of early available and thoroughly validated maintainability measurement instruments motivated us to define a set of metrics for Entity-Relationship (ER) diagrams. In this paper we show that these easily calculated and objective metrics, measuring structural properties of ER diagrams, can be used as indicators of the Understandability of the diagrams. Understandability is a key factor in determining maintainability as model modifications must be preceded by a thorough understanding of the model. The validation of the metrics as early Understandability indicators opens up the way for an in-depth study of how structural properties determine conceptual data model Understandability. It also allows building maintenance-related prediction models that can be used in conceptual data modelling practice.
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using practitioners for assessing the Understandability of uml statechart diagrams with composite states
International Conference on Conceptual Modeling, 2007Co-Authors: Jose A Cruzlemus, Marcela Genero, Sandro Morasca, Mario PiattiniAbstract:We have carried out a family of empirical studies to investigate whether the use of composite states improves the Understandability of UML statechart diagrams. Our hypothesis derived from conventional wisdom, which says that hierarchical modeling mechanisms are helpful to master a system's complexity. We carried out three studies that have gradually evolved in the size of the UML statechart models, the type of subjects (students vs. professionals), the familiarity of the subjects with the domains of the diagrams, and other factors. In this work we briefly review the first and second studies and present the third one, performed with practitioners as experimental subjects. Surprisingly, our results do not seem to show that the use of composite states improves the Understandability of UML statechart diagrams.
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Building measure-based prediction models for UML class diagram maintainability
Empirical Software Engineering, 2007Co-Authors: Marcela Genero, Aaron Visaggio, Esperanza Manso, Gerardo Canfora, Mario PiattiniAbstract:The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: Understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams Understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for Understandability and modifiability.
Marcela Genero - One of the best experts on this subject based on the ideXlab platform.
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the impact of structural complexity on the Understandability of uml statechart diagrams
Information Sciences, 2010Co-Authors: Jose A Cruzlemus, Marcela Genero, Ann Maes, Geert Poels, Mario PiattiniAbstract:The effectiveness of current software development strategies, such as Model-Driven Development (MDD), depends largely on the quality of their primary artefacts, i.e. software models. As the standard modelling language for software systems is the Unified Modelling Language (UML), quality assurance of UML models is a major research field in Computer Science. Understandability, i.e. a model's ability to be easily understood, is one model quality property that is currently heavily under investigation. In particular, researchers are searching for the factors that determine an UML model's Understandability and are looking for ways to manipulate these factors. This paper presents an empirical study investigating the effect that structural complexity has on the Understandability of one particular type of UML model, i.e. the statechart diagram. Based on data collected in a family of three experiments, we have identified three dimensions of structural complexity that affect Understandability: (i) the size and control flow complexity of the statechart in terms of features such as the number of states, events, guards and state transitions; (ii) the actions that are performed when entering or leaving a state; (iii) the sequence of actions that is performed while staying within a state. Based on these structural complexity dimensions we have built an Understandability prediction model using a regression technique that is specifically recommended for data obtained through a repeated measures design. Our test results show that each of the underlying structural complexity dimensions has a significant impact on the Understandability of a statechart diagram.
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defining and validating metrics for assessing the Understandability of entity relationship diagrams
Data and Knowledge Engineering, 2008Co-Authors: Marcela Genero, Geert Poels, Mario PiattiniAbstract:Database and data model evolution cause significant problems in the highly dynamic business environment that we experience these days. To support the rapidly changing data requirements of agile companies, conceptual data models, which constitute the foundation of database design, should be sufficiently flexible to be able to incorporate changes easily and smoothly. In order to understand what factors drive the maintainability of conceptual data models and to improve conceptual modelling processes, we need to be able to assess conceptual data model properties and qualities in an objective and cost-efficient manner. The scarcity of early available and thoroughly validated maintainability measurement instruments motivated us to define a set of metrics for Entity-Relationship (ER) diagrams. In this paper we show that these easily calculated and objective metrics, measuring structural properties of ER diagrams, can be used as indicators of the Understandability of the diagrams. Understandability is a key factor in determining maintainability as model modifications must be preceded by a thorough understanding of the model. The validation of the metrics as early Understandability indicators opens up the way for an in-depth study of how structural properties determine conceptual data model Understandability. It also allows building maintenance-related prediction models that can be used in conceptual data modelling practice.
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using practitioners for assessing the Understandability of uml statechart diagrams with composite states
International Conference on Conceptual Modeling, 2007Co-Authors: Jose A Cruzlemus, Marcela Genero, Sandro Morasca, Mario PiattiniAbstract:We have carried out a family of empirical studies to investigate whether the use of composite states improves the Understandability of UML statechart diagrams. Our hypothesis derived from conventional wisdom, which says that hierarchical modeling mechanisms are helpful to master a system's complexity. We carried out three studies that have gradually evolved in the size of the UML statechart models, the type of subjects (students vs. professionals), the familiarity of the subjects with the domains of the diagrams, and other factors. In this work we briefly review the first and second studies and present the third one, performed with practitioners as experimental subjects. Surprisingly, our results do not seem to show that the use of composite states improves the Understandability of UML statechart diagrams.
-
Building measure-based prediction models for UML class diagram maintainability
Empirical Software Engineering, 2007Co-Authors: Marcela Genero, Aaron Visaggio, Esperanza Manso, Gerardo Canfora, Mario PiattiniAbstract:The usefulness of measures for the analysis and design of object oriented (OO) software is increasingly being recognized in the field of software engineering research. In particular, recognition of the need for early indicators of external quality attributes is increasing. We investigate through experimentation whether a collection of UML class diagram measures could be good predictors of two main subcharacteristics of the maintainability of class diagrams: Understandability and modifiability. Results obtained from a controlled experiment and a replica support the idea that useful prediction models for class diagrams Understandability and modifiability can be built on the basis of early measures, in particular, measures that capture structural complexity through associations and generalizations. Moreover, these measures seem to be correlated with the subjective perception of the subjects about the complexity of the diagrams. This fact shows, to some extent, that the objective measures capture the same aspects as the subjective ones. However, despite our encouraging findings, further empirical studies, especially using data taken from real projects performed in industrial settings, are needed. Such further study will yield a comprehensive body of knowledge and experience about building prediction models for Understandability and modifiability.
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the impact of structural complexity on the Understandability of uml statechart diagrams
2007Co-Authors: Jose A Cruzlemus, Marcela Genero, Ann Maes, Geert Poels, Mario PiattiniAbstract:Given the relevance that UML models and their quality have gained in actual software development strategies, such as the Model Driven-Development (MDD), we present an empirical study about the effect that structural complexity has on the Understandability of UML statechart diagrams, i.e., the diagram’s ability to be easily understood. The current study is based on a family of three experiments. We have studied the data obtained in these experiments and built a preliminary Understandability prediction model by means of a regression analysis using a technique specifically recommended when the data had been obtained through a repeated measures design.
Jose A Cruzlemus - One of the best experts on this subject based on the ideXlab platform.
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the impact of structural complexity on the Understandability of uml statechart diagrams
Information Sciences, 2010Co-Authors: Jose A Cruzlemus, Marcela Genero, Ann Maes, Geert Poels, Mario PiattiniAbstract:The effectiveness of current software development strategies, such as Model-Driven Development (MDD), depends largely on the quality of their primary artefacts, i.e. software models. As the standard modelling language for software systems is the Unified Modelling Language (UML), quality assurance of UML models is a major research field in Computer Science. Understandability, i.e. a model's ability to be easily understood, is one model quality property that is currently heavily under investigation. In particular, researchers are searching for the factors that determine an UML model's Understandability and are looking for ways to manipulate these factors. This paper presents an empirical study investigating the effect that structural complexity has on the Understandability of one particular type of UML model, i.e. the statechart diagram. Based on data collected in a family of three experiments, we have identified three dimensions of structural complexity that affect Understandability: (i) the size and control flow complexity of the statechart in terms of features such as the number of states, events, guards and state transitions; (ii) the actions that are performed when entering or leaving a state; (iii) the sequence of actions that is performed while staying within a state. Based on these structural complexity dimensions we have built an Understandability prediction model using a regression technique that is specifically recommended for data obtained through a repeated measures design. Our test results show that each of the underlying structural complexity dimensions has a significant impact on the Understandability of a statechart diagram.
-
using practitioners for assessing the Understandability of uml statechart diagrams with composite states
International Conference on Conceptual Modeling, 2007Co-Authors: Jose A Cruzlemus, Marcela Genero, Sandro Morasca, Mario PiattiniAbstract:We have carried out a family of empirical studies to investigate whether the use of composite states improves the Understandability of UML statechart diagrams. Our hypothesis derived from conventional wisdom, which says that hierarchical modeling mechanisms are helpful to master a system's complexity. We carried out three studies that have gradually evolved in the size of the UML statechart models, the type of subjects (students vs. professionals), the familiarity of the subjects with the domains of the diagrams, and other factors. In this work we briefly review the first and second studies and present the third one, performed with practitioners as experimental subjects. Surprisingly, our results do not seem to show that the use of composite states improves the Understandability of UML statechart diagrams.
-
the impact of structural complexity on the Understandability of uml statechart diagrams
2007Co-Authors: Jose A Cruzlemus, Marcela Genero, Ann Maes, Geert Poels, Mario PiattiniAbstract:Given the relevance that UML models and their quality have gained in actual software development strategies, such as the Model Driven-Development (MDD), we present an empirical study about the effect that structural complexity has on the Understandability of UML statechart diagrams, i.e., the diagram’s ability to be easily understood. The current study is based on a family of three experiments. We have studied the data obtained in these experiments and built a preliminary Understandability prediction model by means of a regression analysis using a technique specifically recommended when the data had been obtained through a repeated measures design.
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evaluating the effect of composite states on the Understandability of uml statechart diagrams
Model Driven Engineering Languages and Systems, 2005Co-Authors: Jose A Cruzlemus, Marcela Genero, Esperanza M Manso, Mario PiattiniAbstract:UML statechart diagrams have become an important technique for describing the dynamic behavior of a software system. They are also a significant element of OO design, especially in code generation frameworks such as Model Driven Architecture (MDA). In previous works we have defined a set of metrics for evaluating structural properties of UML statechart diagrams and have validated them as early Understandability indicators, through a family of controlled experiments. Those experiments have also revealed that the number of composite states had, apparently, no influence on the Understandability of the diagrams. This fact seemed a bit suspicious to us and we decided to go a step further. So in this work we present a controlled experiment and a replication, focusing on the effect of composite states on the Understandability of UML statechart diagrams. The results of the experiment confirm, to some extent, our intuition that the use of composite states improves the Understandability of the diagrams, so long as the subjects of the experiment have had some previous experience in using them. There are educational implications here, as our results justify giving extra emphasis to the use of composite states in UML statechart diagrams in Software Engineering courses.
Geert Poels - One of the best experts on this subject based on the ideXlab platform.
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the impact of structural complexity on the Understandability of uml statechart diagrams
Information Sciences, 2010Co-Authors: Jose A Cruzlemus, Marcela Genero, Ann Maes, Geert Poels, Mario PiattiniAbstract:The effectiveness of current software development strategies, such as Model-Driven Development (MDD), depends largely on the quality of their primary artefacts, i.e. software models. As the standard modelling language for software systems is the Unified Modelling Language (UML), quality assurance of UML models is a major research field in Computer Science. Understandability, i.e. a model's ability to be easily understood, is one model quality property that is currently heavily under investigation. In particular, researchers are searching for the factors that determine an UML model's Understandability and are looking for ways to manipulate these factors. This paper presents an empirical study investigating the effect that structural complexity has on the Understandability of one particular type of UML model, i.e. the statechart diagram. Based on data collected in a family of three experiments, we have identified three dimensions of structural complexity that affect Understandability: (i) the size and control flow complexity of the statechart in terms of features such as the number of states, events, guards and state transitions; (ii) the actions that are performed when entering or leaving a state; (iii) the sequence of actions that is performed while staying within a state. Based on these structural complexity dimensions we have built an Understandability prediction model using a regression technique that is specifically recommended for data obtained through a repeated measures design. Our test results show that each of the underlying structural complexity dimensions has a significant impact on the Understandability of a statechart diagram.
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defining and validating metrics for assessing the Understandability of entity relationship diagrams
Data and Knowledge Engineering, 2008Co-Authors: Marcela Genero, Geert Poels, Mario PiattiniAbstract:Database and data model evolution cause significant problems in the highly dynamic business environment that we experience these days. To support the rapidly changing data requirements of agile companies, conceptual data models, which constitute the foundation of database design, should be sufficiently flexible to be able to incorporate changes easily and smoothly. In order to understand what factors drive the maintainability of conceptual data models and to improve conceptual modelling processes, we need to be able to assess conceptual data model properties and qualities in an objective and cost-efficient manner. The scarcity of early available and thoroughly validated maintainability measurement instruments motivated us to define a set of metrics for Entity-Relationship (ER) diagrams. In this paper we show that these easily calculated and objective metrics, measuring structural properties of ER diagrams, can be used as indicators of the Understandability of the diagrams. Understandability is a key factor in determining maintainability as model modifications must be preceded by a thorough understanding of the model. The validation of the metrics as early Understandability indicators opens up the way for an in-depth study of how structural properties determine conceptual data model Understandability. It also allows building maintenance-related prediction models that can be used in conceptual data modelling practice.
-
the impact of structural complexity on the Understandability of uml statechart diagrams
2007Co-Authors: Jose A Cruzlemus, Marcela Genero, Ann Maes, Geert Poels, Mario PiattiniAbstract:Given the relevance that UML models and their quality have gained in actual software development strategies, such as the Model Driven-Development (MDD), we present an empirical study about the effect that structural complexity has on the Understandability of UML statechart diagrams, i.e., the diagram’s ability to be easily understood. The current study is based on a family of three experiments. We have studied the data obtained in these experiments and built a preliminary Understandability prediction model by means of a regression analysis using a technique specifically recommended when the data had been obtained through a repeated measures design.
Allan Hanbury - One of the best experts on this subject based on the ideXlab platform.
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consumer health search on the web study of web page Understandability and its integration in ranking algorithms
Journal of Medical Internet Research, 2019Co-Authors: Guido Zuccon, João R. M. Palotti, Allan HanburyAbstract:Understandability plays a key role in ensuring that people accessing health information are capable of gaining insights that can assist them with their health concerns and choices. The access to unclear or misleading information has been shown to negatively impact the health decisions of the general public. The aim of this study was to investigate methods to estimate the Understandability of health Web pages and use these to improve the retrieval of information for people seeking health advice on the Web. Our investigation considered methods to automatically estimate the Understandability of health information in Web pages, and it provided a thorough evaluation of these methods using human assessments as well as an analysis of preprocessing factors affecting Understandability estimations and associated pitfalls. Furthermore, lessons learned for estimating Web page Understandability were applied to the construction of retrieval methods, with specific attention to retrieving information understandable by the general public. We found that machine learning techniques were more suitable to estimate health Web page Understandability than traditional readability formulae, which are often used as guidelines and benchmark by health information providers on the Web (larger difference found for Pearson correlation of .602 using gradient boosting regressor compared with .438 using Simple Measure of Gobbledygook Index with the Conference and Labs of the Evaluation Forum eHealth 2015 collection). The findings reported in this paper are important for specialized search services tailored to support the general public in seeking health advice on the Web, as they document and empirically validate state-of-the-art techniques and settings for this domain application.
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SIGIR - Ranking Health Web Pages with Relevance and Understandability
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016Co-Authors: João R. M. Palotti, Guido Zuccon, Lorraine Goeuriot, Allan HanburyAbstract:We propose a method that integrates relevance and Understandability to rank health web documents. We use a learning to rank approach with standard retrieval features to determine topical relevance and additional features based on readability measures and medical lexical aspects to determine Understandability. Our experiments measured the effectiveness of the learning to rank approach integrating Understandability on a consumer health benchmark. The findings suggest that this approach promotes documents that are at the same time topically relevant and understandable.
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ranking health web pages with relevance and Understandability
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016Co-Authors: João R. M. Palotti, Guido Zuccon, Lorraine Goeuriot, Allan HanburyAbstract:We propose a method that integrates relevance and Understandability to rank health web documents. We use a learning to rank approach with standard retrieval features to determine topical relevance and additional features based on readability measures and medical lexical aspects to determine Understandability. Our experiments measured the effectiveness of the learning to rank approach integrating Understandability on a consumer health benchmark. The findings suggest that this approach promotes documents that are at the same time topically relevant and understandable.
