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UML diagrams describe different views of one software. These diagrams strongly depend on each other and must therefore be consistent with one another, since inconsistencies between diagrams may be a source of faults during software development activities that rely on these diagrams. It is therefore paramount that consistency rules be defined and that inconsistencies be detected, analyzed and fixed. The relevant literature shows that authors typically define their own consistency rules, sometimes defining the same rules and sometimes defining rules that are already in the UML standard. The reason might be that no consolidated set of rules that are relevant by authors can be found to date. The aim of our research is to provide an up to date, consolidated set of UML consistency rules and obtain a detailed overview of the current research in this area. We therefore followed a systematic procedure in order to collect from literature up to March 2017 and analyze UML consistency rules. We then consolidated a set of 119 UML consistency rules (avoiding redundant definitions or definitions already in the UML standard), which can be used as an important reference for UML-based software development activities, for teaching UML-based software development, and for further research.

UML is a commonly-used graphical language for the modelling of software. Works regarding UML’s effectiveness have studied projects that develop software systems from scratch. Yet the maintenance of software consumes a large share of the overall time and effort required to develop software systems. This study, therefore, focuses on the use of UML in software maintenance. We wish to elicit the practices of the software modelling used during maintenance in industry and understand what are perceived as hurdles and benefits when using modelling. In order to achieve a high level of realism, we performed a case study in a multinational company’s ICT department. The analysis is based on 31 interviews with employees who work on software maintenance projects. The interviewees played different roles and provided complementary views about the use, hurdles and benefits of software modelling and the use of UML. Our study uncovered a broad range of modelling-related practices, which are presented in a theoretical framework that illustrates how these practices are linked to the specific goals and context of software engineering projects. We present a list of recommended practices that contribute to the increased effectiveness of software modelling. The use of software modelling notations (like UML) is considered beneficial for software maintenance, but needs to be tailored to its context. Various practices that contribute to the effective use of modelling are commonly overlooked, suggesting that a more conscious holistic approach with which to integrate modelling practices into the overall software engineering approach is required.

UML models are standard artifacts used by software engineers for designing software. As software is designed, different UML diagram types (e.g., class diagrams and sequence diagrams) are produced by software designers. Since the various UML diagram types describe different aspects of a software system, they are not independent but strongly depend on each other, hence they must be consistent. Inconsistencies cause faults in the final software systems. It is, therefore, paramount that they get detected, analyzed, and fixed. Consistency rules are a useful tool proposed in the literature to detect inconsistencies. They categorize constraints that help in identifying inconsistencies when violated. This case study aims at collecting and analyzing UML models with OCL consistency rules proposed in the literature and at promoting the development of a reference benchmark that can be reused by the (FM-)research community. We collected 33 UML consistency rules and 206 different UML diagrams contained in 34 open-source UML models presented in the literature. We propose an FM-based encoding of the consistency rules in OCL. This encoding allows analyzing whether the consistency rules are satisfied or violated within the 34 UML models. To assess the proposed benchmark, we analyzed how the UML models, consistency rules, diagram types contained in the benchmark help in assessing the consistency of UML models, and the consistency of diagrams across the different software development phases. Our results show that the considered UML models and consistency rules allowed identifying 2731 inconsistencies and that those inconsistencies refer to different software development phases. We concluded that the considered UML models and consistency rules could be considered as an initial benchmark that can be further extended by the research community.

The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard modeling language for object-oriented software modeling and documentation. Since the various UML diagrams describe different views of one, and only one, software system under development, they strongly depend on each other in many ways. In other words, the UML diagrams describing a software system must be consistent. Inconsistencies among these diagrams may be a source of faults during software development and analysis. It is therefore paramount that these inconsistencies be detected, analyzed and -- hopefully -- fixed. The goal of this workshop was to gather input and feedbacks on UML consistency rules from the community. This workshop provided an opportunity for researchers who have been working in the area of UML consistency to interact with each other at a highly interactive venue, improve the body of knowledge on UML consistency rules and discuss ideas for further research in this area. This report summarizes details of the workshop and the results obtained that day.

Context: UML software development relies on different types of UML diagrams, which must be consistent with one another. UML Synthesis techniques suggest to generate diagram(s) from other diagram(s), thereby implicitly suggesting that input and output diagrams of the synthesis process be consistent with one another. Objective: Our aim is to provide a comprehensive summary of UML synthesis techniques as they have been described in the literature to date to then collect UML consistency rules, which can then be used to verify UML models. Method: We performed a Systematic Mapping Study by following well-known guidelines. We selected 14 studies by means of a search with seven search engines executed until January, 2018. Results: Researchers have not frequently published papers concerning UML synthesis techniques since 2004. We present a set of 47 UML consistency rules collected from the different synthesis techniques analyzed. Conclusion: Although UML diagrams synthesis doesn’t seem to be an active line of research, it is relevant since synthesis techniques rely on enforcing diagram consistency, which is an active line of research. We collected consistency rules which can be used to check UML models, specifically to verify if the diagrams of a model are consistent with one another.

Although the UML is considered to be the de facto standard notation with which to model software, there is still resistance to model-based development. UML modeling is perceived to be expensive and not necessarily cost-effective. It is therefore important to collect empirical evidence concerning the conditions under which the use of UML makes a practical difference. The focus of this paper is to investigate whether and how the Level of Detail (LoD) of UML diagrams impacts on the performance of maintenance tasks in a model-centric approach. A family of experiments consisting of one controlled experiment and three replications has therefore been carried out with 81 students with different abilities and levels of experience from 3 countries (The Netherlands, Spain, and Italy). The analysis of the results of the experiments indicates that there is no strong statistical evidence as to the influence of different LoDs. The analysis suggests a slight tendency toward better results when using low LoD UML diagrams, especially if used for the modification of the source code, while a high LoD would appear to be helpful in understanding the system. The participants in our study also favored low LoD diagrams because they were perceived as easier to read. Although the participants expressed a preference for low LoD diagrams, no statistically significant conclusions can be drawn from the set of experiments. One important finding attained from this family of experiments was that the participants minimized or avoided the use of UML diagrams, regardless of their LoD. This effect was probably the result of using small software systems from well-known domains as experimental materials.

We carried out a family of experiments to investigate whether the use of UML models produced in the requirements analysis process helps in the comprehensibility and modifiability of source code. The family consists of a controlled experiment and 3 external replications carried out with students and professionals from Italy and Spain. 86 participants with different abilities and levels of experience with UML took part. The results of the experiments were integrated through the use of meta-analysis. The results of both the individual experiments and meta-analysis indicate that UML models produced in the requirements analysis process influence neither the comprehensibility of source code nor its modifiability.

Context The Unified Modelling Language (UML) has, after ten years, become established as the de facto standard for the modelling of object-oriented software systems. It is therefore relevant to investigate whether its use is important as regards the costs involved in its implantation in industry being worthwhile. Method We have carried out a systematic mapping study to collect the empirical studies published in order to discover “What is the current existing empirical evidence with regard to the use of UML diagrams in source code maintenance and the maintenance of the UML diagrams themselves? Results We found 38 papers, which contained 63 experiments and 3 case studies. Conclusion Although there is common belief that the use of UML is beneficial for source code maintenance, since the quality of the modifications is greater when UML diagrams are available, only 3 papers concerning this issue have been published. Most research (60 empirical studies) concerns the maintainability and comprehensibility of the UML diagrams themselves which form part of the system’s documentation, since it is assumed that they may influence source code maintainability, although this has not been empirically validated. Moreover, the generalizability of the majority of the experiments is questionable given the material, tasks and subjects used. There is thus a need for more experiments and case studies to be performed in industrial contexts, i.e., with real systems and using maintenance tasks conducted by practitioners under real conditions that truly show the utility of UML diagrams in maintaining code, and that the fact that a diagram is more comprehensible or modifiable influences the maintainability of the code itself. This utility should also be studied from the viewpoint of cost and productivity, and the consistent and simultaneous maintenance of diagrams and code must also be considered in future empirical studies.

The quality of conceptual models directly affects the quality of the understanding of the application domain and the quality of the final software products that are ultimately based on them. This paper describes a systematic literature review (SLR) of peer-reviewed conference and journal articles published from 1997 through 2009 on the quality of conceptual models written in UML, undertaken to understand the state-of-the-art, and then identify any gaps in current research. Six digital libraries were searched, and 266 papers dealing specifically with the quality of UML models were identified and classified into five dimensions: type of model quality, type of evidence, type of research result, type of diagram, and research goal. The results indicate that most research focuses on semantic quality, with relatively little on semantic completeness; as such, this research examines new modeling methods vs. quality frameworks and metrics, as well as quality assurance vs. understanding quality issues. The results also indicate that more empirical research is needed to develop a theoretical understanding of conceptual model quality. The classification scheme developed in this paper can serve as a guide for both researchers and practitioners.

The goal of any modeling activity is a complete and accurate understanding of the real-world domain, within the bounds of the problem at hand and keeping in mind the goals of the stakeholders involved. High-quality representations are critical to that understanding. This paper proposes a comprehensive Conceptual Modeling Quality Framework, bringing together two well-known quality frameworks: the framework of Lindland, Sindre, and Solvberg (LSS) and that of Wand and Weber based on Bunge's ontology (BWW). This framework builds upon the strengths of the LSS and BWW frameworks, bringing together and organizing the various quality cornerstones and then defining the many quality dimensions that connect one to another. It presents a unified view of conceptual modeling quality that can benefit both researchers and practitioners.

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.

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.

This paper highlights the need of identifying a set of UML consistency rules that is as complete as possible. For that purpose, we propose to complement our previous research works, by searching for such rules directly from technical books on UML-based object-oriented software development.

Not all the model constraints can be defined using only UML graphical features due to the limited expressiveness of diagram-based UML notation. To solve this problem, the OCL language was defined as a textual add-on to the UML diagrams, allowing the specification of a wide range of constraints of objects. Aware of the lack of metrics to capture the quality aspects of UML/OCL models and the importance of models in recent initiatives of model-Driven software engineering (such as Model-Driven Development and Model-Driven Architecture), we define a set of metrics for measuring the structural properties of OCL constraints in UML/OCL models. Many of these metrics are defined in terms of navigations, a core concept of OCL that defines coupling between objects. This paper carefully describes a family of experiments we have conducted to ascertain whether any relationship exists between object coupling (defined through metrics related to navigations and collection operations) and two maintainability subcharacteristics: understandability and modifiability of OCL expressions. Empirical evidence that such a relationship exists is shown in the obtained results, however they must be considered as preliminaries results. Further validation is needed to strengthen the conclusions and external validity.

The complexity of software artifacts is widely believed to be an important determinant of maintenance effort. This paper conducts an experimental analysis of the impact of complexity on the maintenance of the Unified Modeling Language (UML) class diagrams. This represents an analysis of the effect of an internal quality attribute on an external quality attribute. A range of complexity metrics are proposed based on an ontological analysis of the UML language and previous research. The relative influence of these metrics on maintenance effort is then evaluated using a laboratory experiment. A within-subjects design was used, with subjects required to modify a range of UML class diagrams with different levels of complexity. Only two of the metrics emerged as significant determinants of maintenance effort: number of methods and number of associations. Together these explain around 28% of the variation in maintenance effort. While these findings are encouraging, further research is necessary to explore the ability of these metrics to predict maintenance effort. Copyright © 2005 John Wiley & Sons, Ltd.

This paper presents the results of an empirical study aiming at comparing the support provided by ER and UML class diagrams during maintenance of data models. We performed one controlled experiment and two replications that focused on comprehension activities (the first activity in the maintenance process) and another controlled experiment on modification activities related to the implementation of given change requests. The results achieved were analyzed at a fine-grained level aiming at comparing the support given by each single building block of the two notations. Such an analysis is used to identify weaknesses (i.e., building blocks not easy to comprehend) in a notation and/or can justify the need of preferring ER or UML for data modeling. The analysis revealed that the UML class diagrams generally provided a better support for both comprehension and modification activities performed on data models as compared to ER diagrams. Nevertheless, the former has some weaknesses related to three building blocks, i.e., multi-value attribute, composite attribute, and weak entity. These findings suggest that an extension of UML class diagrams should be considered to overcome these weaknesses and improve the support provided by UML class diagrams during maintenance of data models.

Context: The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard modeling language for object-oriented modeling and documentation. Since the various UML diagrams describe different aspects of one, and only one, software under development, they are not independent but strongly depend on each other in many ways. In other words, the UML diagrams describing a software product must be consistent. Inconsistencies between these diagrams may be a source of faults in software systems. It is therefore paramount that these inconsistencies be detected, analyzed and hopefully fixed.Objective: The aim of this article is to deliver a comprehensive summary of UML consistency rules as they are described in the literature to date to obtain an extensive and detailed overview of the current research in this area.Method: We performed a Systematic Mapping Study by following well-known guidelines. We selected 95 primary studies from a search with seven search engines performed in December 2012.Results: Different results are worth mentioning. First it appears that researchers tend to discuss very similar consistency rules, over and over again. Most rules are horizontal (98.10%) and syntactic (88.21%). The most used diagrams are the class diagram (71.58%), the sequence diagram (47.37%) and the state machine diagram (42.11%).Conclusion: The fact that many rules are duplicated in primary studies confirms the need for a well-accepted list of consistency rules. This paper is a first step in this direction. Results indicate that much more work is needed to develop consistency rules for all 14 UML diagrams, in all dimensions of consistency (e.g., semantic and syntactic on the one hand, horizontal, vertical and evolution on the other hand).

Context: UML has been the de facto standard notation for modeling object-oriented software systems since its appearance in 1997. UML diagrams are important for maintainers of a system, especially when the software was developed by a different team. These diagrams of the system are not always available, however, and are commonly recovered using Reverse Engineering (RE) techniques. When obtained through RE, UML diagrams have a high level of detail as compared to those developed in the forward design activity. Method: In this paper we report on a comparison of the attitude and performance of maintainers when using these two kinds of diagrams during the maintenance of source code. Our findings were obtained by carrying out a controlled experiment with 40 students of a Master's degree in Computer Science. Results: The results show a preference for forward design diagrams but do not display significant differences in task performance. The post-experiment survey results have led us to conclude that the subjects did not consider RE diagrams helpful; they found them difficult to understand, particularly the sequence diagrams. In the case of forward design diagrams, subjects considered sequence diagrams as useful, but they did not really employ them. Conclusions: Based on our findings, as regards performance of maintainers, there are no objective results which favor the use of one of these types of diagram in particular, i.e., UML diagrams which come from forwards design, on the one hand, and diagrams obtained from RE, on the other. Subjective opinions do, however, lead us to recommend the use of diagrams created during design. Nevertheless, we realize that the results should be considered as preliminary ones; further replications of this experiment are planned, using students and professionals, the aim being to obtain more conclusive results.

The quality of conceptual models directly affects the quality of the understanding of the application domain and the quality of the final software products that are ultimately based on them. This paper describes a systematic literature review (SLR) of peer-reviewed conference and journal articles published from 1997 through 2009 on the quality of conceptual models written in UML, undertaken to understand the state-of-the-art, and then identify any gaps in current research. Six digital libraries were searched, and 266 papers dealing specifically with the quality of UML models were identified and classified into five dimensions: type of model quality, type of evidence, type of research result, type of diagram, and research goal. The results indicate that most research focuses on semantic quality, with relatively little on semantic completeness; as such, this research examines new modeling methods vs. quality frameworks and metrics, as well as quality assurance vs. understanding quality issues. The results also indicate that more empirical research is needed to develop a theoretical understanding of conceptual model quality. The classification scheme developed in this paper can serve as a guide for both researchers and practitioners. the customer, ofdeepening the understanding of how software works, and ultimately of reducing the complexity of software, is through the use of models (Thomas, 2004). Over the years, we have come to understand that modeling offers benefits to different stakeholders in software projects (Selic, 2003). In the very early stages of a project, models aid in understanding and communicating requirements. During development, architecture and design models guide ReseaRch Review DOI: 10.4018/jdm.2011070103 Journal of Database Management, 22(3), 46-70, July-September 2011 47 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. the implementation of the system. Finally, models are used for test-generation (Offutt & Abdurazik, 1999) and for easing maintenance activities (Dzidek, Arisholm, & Briand, 2008). Software development itself is becoming more model-centric (Mohagheghi, Dehlen, & Neple, 2009). The OMG Model Driven Architecture (OMG, 2003) and the recent growth of the Model-Driven Development (MDD) software engineering paradigm (Atkinson & Kuhne, 2003) emphasizes the role of modeling in the development of software systems. MDD treats software development as a set of transformations between successive models from requirements to analysis, to design, to implementation, and to deployment (Thomas, 2004). MDD’s defining characteristic is that software development’s primary focus and products are models rather than computer programs. The dominant question is no longer “Should we do modeling?” but “How should we do modeling?” This new focus on the modeling process, rather than on the software product resulting from the development activities, puts model quality in the forefront. There has been increasing interest, both in industry and academia, on methods and techniques for quality assessment, assurance, and improvement of models in software development and maintenance (Mohagheghi, Dehlen, & Neple, 2009). While there has been a great deal of research on software quality, there has been relatively little work on the quality of models, and the concept of model quality is poorly understood. Existing knowledge on software quality has limited applicability to model quality. Models have very different characteristics than source code: models have multiple views, may be used informally and casually rather than formally and with precision, can be used throughout all phases of the project, and so on. In an effort to bring together the wide variety of modeling methods and forms, the Unified Modeling Language (UML) emerged in the 1990s as a standard modeling language for a wide spectrum of application domains (Rumbaugh, Booch, & Jacobson, 1998). This standardization has driven the advancement of modeling methods and tools and has enabled academics and practitioners alike to improve on the core structure of UML and engage in a healthy debate about the use, advancement, and basic beliefs about the modeling process. However, modeling research has tended to be more about improving UML to deal with special modeling cases than with improving model quality (Dobing & Parsons, 2006; Grossman, Aronson, & McCarthy, 2005). In order to advance the field of conceptual modelling quality research, it is useful to explore the history of the field and to determine its current state of the art by locating, evaluating, and interpreting relevant research to date that is related to model quality with a focus on UML. This paper presents a systematic literature review (SLR) of papers dealing with the quality of UML models. A proper systematic literature review follows a rigorous and systematic approach, in particular that described by Brereton, Kitchenham, Budgen, Turner, and Khalil (2007), Kitchenham (2004), and Kitchenham and Charters (2007). Six digital libraries containing thousands of academic research papers were searched, producing 266 papers dealing exclusively with UML model quality. These papers were classified in five dimensions (Table 3): quality type, type of evidence, research results, type of UML diagram, and research goals. These dimensions were chosen on the basis of previous research on model quality (though not necessarily restricted to UML) and conceptual frameworks that help define the concept of model quality. These dimensions are also useful to position new research activities appropriately, and the classified library of UML model quality research papers can serve as a valuable resource both for researchers and for practitioners. An analysis of the papers shows the progress made in advancing UML model quality and identifies where gaps exist that could be areas for further investigation. The results indicate that there is no clear view of the real state of the field, although quality of models used in software development is a “hot topic” that needs further investigation (Wand & Weber, 2002). 48 Journal of Database Management, 22(3), 46-70, July-September 2011 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Quality assurance techniques for software, such as testing, inspections, analysis, and measurement, are well established, but their application in the domain of UML models and MDD is still in an embryonic phase. However, starting in the year 2002 and continuing to the present, the topic has been well represented in international conferences such as ER (International Conference on Conceptual Modeling) and MODELS (International Conference on Model Driven Engineering Languages and Systems) and in various associated workshops, such as QiM (Quality in Modeling), MoDEVA (Model Validation), IWCMQ (International Workshop on Conceptual Modelling Quality), and QoIS (Quality of Information Systems). The remainder of the paper is structured as follows. The next section presents a brief discussion of related work. This is followed by the SLR outline, a description of the activities of the SLR process, and a discussion of the results and their implications. The paper concludes with an analysis of the threats to validity of the results, the lessons learned with respect to performing the SLR, and finally a discussion of future research possibilities.

[EN] Context: The conventional wisdom states that stereotypes are used to clarify or extend the meaning of model elements and consequently should be helpful in comprehending the diagram semantics. Objective: The main goal of this work is to present a family of experiments that we have carried out to investigate whether the use of stereotypes improves the comprehension of UML sequence diagrams. Method: The family of experiments consists of an experiment and two replications carried out with 78, 29 and 36 undergraduate Computer Science students, respectively. The comprehension of UML sequence diagrams with and without stereotypes was analyzed from three different perspectives borrowed from the Cognitive Theory of Multimedia Learning (CTML): semantic comprehension, retention and transfer. In addition, we carried out a meta-analysis study to integrate the different data samples. Results: The statistical analysis and meta-analysis of the data obtained from each experiment separately indicates that the use of the proposed stereotypes helps improving the comprehension of the diagrams, especially when the subjects are not familiar with the domain. Conclusions: The set of stereotypes presented in this work seem to be helpful for a better comprehension of UML sequence diagrams, especially with not well-known domains. Although further research is necessary for strengthening these results, introducing these stereotypes both in academia and industry could be an interesting practice for checking the validity of the results, This research is part of the following projects: "MEDUSAS (IDI-20090557)" and "Proyecto Integrado ORIGIN (IDI-2010043(1-5))" financed by CDTI and FEDER; "ECCOO (TRA2009_0074)", "MAGO/PEGASO (TIN2009-13718)" and "MULTIPLE (TIN2009-13838)" financed by MICINN; MECCA (PI12109-0075-8394) and IMPACTUM (PEII11-0330-4414) financed by JCCM and CALIMO funded by the Generalitat Valenciana.

This paper presents a research proposal on how and why we are investigating the benefits of the Unified Modelling Language (UML) in software maintenance tasks. The principal objective is to present the main research questions, along with an explanation of which research methods we propose to use to obtain the answers to these questions in an empirical manner.

This paper is aimed at evaluating the ability of novice analysts to understand models specified using a RUP extension for modeling requirements. The evaluation is guided by a theoretical model for IS design methods, the Method Evaluation Model (MEM). In this work, we present the empirical testing of the MEM in the evaluation of a RUP extension for modeling requirements. The testing was conducted through an experiment using 39 novice users. The evaluation’s primary goal was to test the users’ ability to understand requirements models. The results provide a strong indication that our RUP extension is indeed both ease to use and useful and there is an intention to use the method in the future.

Model-Driven Architecture (MDA) is a software engineering approach that promotes the use of models and model transformations as primary development artifacts. Usually; there are several ways to transform a source model into a target model. Alternative target models may have the same functionality but may differ in their quality attributes (e.g.; understandability; modifiability). This chapter presents an approach to deal with quality-driven model transformations. Specifically; it focuses on a specific set of transformations to obtain UML class diagrams from a Requirements Model. A set of alternative transformations are identified; and the selection of the best alternative is done through a controlled experiment. The goal of the experiment is to empirically validate which alternative transformation produces the UML class diagram that is the easiest to understand. This evidence can be further used to define high-quality transformation processes; as it will be based on empirical knowledge rather than on common wisdom and the intuition of the researchers and developers.

The main goal of this work is to present a family of empirical studies that we have carried out to investigate whether the use of composite states may improve the understandability of UML statechart diagrams derived from class diagrams. Our hypotheses derive from conventional wisdom, which says that hierarchical modeling mechanisms are helpful in mastering the complexity of a software system. In our research, we have carried out three empirical studies, consisting of five experiments in total. The studies differed somewhat as regards the size of the UML statechart models, though their size and the complexity of the models were chosen so that they could be analyzed by the subjects within a limited time period. The studies also differed with respect to the type of subjects (students vs. professionals), the familiarity of the subjects with the domains of the diagrams, and other factors. To integrate the results obtained from each of the five experiments, we performed a meta-analysis study which allowed us to take into account the differences between studies and to obtain the overall effect that the use of composite states has on the understandability of UML statechart diagrams throughout all the experiments. The results obtained are not completely conclusive. They cast doubts on the usefulness of composite states for a better understanding and memorizing of UML statechart diagrams. Composite states seem only to be helpful for acquiring knowledge from the diagrams. At any rate, it should be noted that these results are affected by the previous experience of the subjects on modeling, as well as by the size and complexity of the UML statechart diagrams we used, so care should be taken when generalizing our results.

Business process models are currently attaining more relevance, and more attention is therefore being paid to their quality. This situation led us to define a set of measures for the understandability of BPMN models, which is shown in a previous work. We focus on understandability since a model must be well understood before any changes are made to it. These measures were originally informally defined in natural language. As is well known, natural language is ambiguous and may lead to misunderstandings and a misinterpretation of the concepts captured by a measure and the way in which the measure value is obtained. This has motivated us to provide the formal definition of the proposed measures using OCL (Object Constraint Language) upon the BPMN (Business Process Modeling Notation) metamodel presented in this paper. The main advantages and lessons learned (which were obtained both from the current work and from previous works carried out in relation to the formal definition of other measures) are also summarized.

The main goal of this paper is to show the findings obtained through a meta-analysis study carried out with the data obtained from a family of five controlled experiments performed in academic environments. This family of experiments was carried out to validate empirically two hypotheses applied to UML class diagrams, which investigate 1) The dependence between the structural complexity and size of UML class diagrams on one hand and their cognitive complexity on the other, as well as 2) The dependence between the cognitive complexity of UML class diagrams and their comprehensibility and modifiability. We carried out a meta-analysis, as it allows us to integrate the individual findings obtained from the execution of a family of experiments carried out to test the aforementioned hypotheses. The meta-analysis reveals that the measures related to associations and generalizations have a strong correlation with the cognitive complexity, and that the cognitive complexity has a greater correlation to comprehensibility than to modifiability. These results have implications from the points of view of both modeling and teaching, revealing which UML constructs are most influential when modelers have to comprehend and modify UML class diagrams. In addition, the measures related to associations and generalizations could be used to build prediction models.

This paper reports on a controlled experiment that investigates the influence of stereotypes in UML sequence diagrams. The comprehension of UML sequence diagrams with and without stereotypes is analyzed from three different perspectives: semantic comprehension, retention and transfer. The experiment was carried out with 77 undergraduate students of Computer Science from the University of Bari in Italy. The results obtained show a slight tendency in favor of the use of stereotypes in facilitating the comprehension of UML sequence diagrams. Further replications are needed to obtain more conclusive results.

The main goal of this paper is to provide empirical evidence, through a controlled experiment, of the influence of stereotypes when modelers, developers, and maintainers have to comprehend UML sequence diagrams. The comprehension of UML sequence diagrams with and without stereotypes was analyzed from three different perspectives: semantic comprehension, retention and transfer. The experiment was carried out with 77 fourth year undergraduate students of Computer Science from the University of Bari in Italy. The results obtained show a slight tendency in favor of the use of stereotypes in facilitating the comprehension of UML sequence diagrams, although it is not statistically significant. Further replications are needed to obtain more conclusive results.

This paper is part of a project we have been developing for the past three years, related to the maintainability of expressions written with the Object Constraint language (OCL). Our objective is to find which elements of OCL influence on the comprehensibility and modifiability of OCL expressions. Coupling is recognized as a key characteristic of software artifacts, which affects comprehensibility and maintainability. In addition, whenever an expression operates in a large context, modelers need to know all the UML artifacts the expression relies on. Therefore, we believe that coupling should also affect to the comprehension of OCL expressions and, consequently, their modifiability. We believe that two main factors of the import coupling are the number of different objects which are coupled to the contextual instance and the depth of coupling. For measuring these factors we have defined two measures (the number of navigated classes -NNC- and the depth of navigation -DN-) based on two cognitive techniques (chunking and tracing). For testing if both coupling measures (and their interaction) could be indicators of the comprehensibility and modifiability of OCL expressions, we carried out a specific family of experiments which will be carefully described in this paper. The obtained results confirm our hypothesis, revealing that coupling really affects to the maintainability of OCL expressions.

The informal definition of a measure in natural language is ambiguous, so it must be accompanied by a precise and formal definition, for avoiding misunderstanding and misinterpretation. In this paper we show the formal definition of measures for UML statechart diagrams using OCL, upon the UML statechart metamodel. The use of a formal definition upon a metamodel (where the main concepts and relationships are modelled) assure that measures capture the concepts they intend for and could facilitate the implementation of measures extraction tools.

In this paper, we briefly introduce a controlled experiment to investigate the selection of alternative transformation rules through which to obtain UML class diagrams from a Requirements Model. The main goal of this experiment was to determine which of the transformation rules for structural relationships between classes (association (Al), aggregation (A2) and association class (A3)) produces the UML class diagram that is easiest to understand. More details about the transformations and about the experiment are provided. We focus upon the understandability of UML class diagrams because it is well recognized that if a model is easier to understand it will be easier to maintain, reuse, etc.

The import coupling of an object constraint language (OCL) expression increases when the expression operates within a large context of a Unified Modelling Language (UML) model. The expression would thus exhibit a decrease in its comprehensibility, and in consequence, the maintainability of the OCL expressions could be affected, due to the fact that modelers need to know all the artifacts upon which the expression relies. In order to explain the cognitive complexity of modelers dealing with OCL expressions with high import- coupling we have carried out a think aloud experiment which is described in detail in this paper. The experiment is part of a project which we are developing with the goal of obtaining valid measures through which to assess the influence of import coupling on OCL expression maintainability. The empirical results show evidence that: (1) reified objects, problem objects and their relationships constitute the main components of the cognitive complexity of modelers; (2) modelers use different cognitive approaches to comprehend an OCL expression within a UML/OCL model.

Coupling is considered one of the most complex software quality attributes in Object Oriented (OO) Systems. We belief that OCL expressions provide essential coupling information at early phases of software development. For that reason, we started to study if object coupling information obtained from OCL expressions affects the quality of the model that is finally delivered, based on the hypothesis that object coupling (an structural property) of an OCL expression within a UML/OCL model (artifacts) can influence on the cognitive complexity of modelers (subjects), and high cognitive complexity leads to OCL expressions exhibit undesirable external qualities, such as reduced comprehensibility and modifiability. In this paper we explain the influence of object coupling on the comprehensibility and modifiability of OCL expressions following a cognitive theory-based approach. We study cognitive complexity using mental and cognitive models to explain the rationale of the measures we defined for measuring object coupling. This cognitive theory-based approach was also used for obtaining a clear explanation of a family of experiments we run to confirm our hypothesis. The empirical results reveal that there is empirical evidence that object coupling is strongly correlated with the comprehensibility and modifiability of OCL expressions.

Diagram-based UML notation is limited in its expressiveness thus producing a model that would be severely underspecified. The flaws in the limitation of the UML diagrams are solved by specifying UML/OCL combined models, OCL being an essential add-on to the UML diagrams. Aware of the importance of building precise models, the main goal of this paper is to carefully describe a family of experiments we have undertaken to ascertain whether any relationship exists between object coupling (defined through metrics related to navigations and collection operations) and two maintainability sub-characteristics: understandability and modifiability of OCL expressions. If such a relationship exists, we will have found early indicators of the understandability and modifiability of OCL expressions. Even though the results obtained show empirical evidence that such a relationship exists, they must be considered as preliminaries. Further validation is needed to be performed to strengthen the conclusions and external validity

As UML statechart diagrams are the core for modeling the dynamic aspects of software systems, we have been studying their understandability for the last three years. In previous researches, we have already studied the relationship between many of the constructs of the UML statechart diagrams and the effect that they have on the understandability of the diagrams themselves. We have also performed a family of experiments whose results indicated that the use of composite states make UML statechart diagrams easier to understand. This fact motivated us to go a step further and investigate if the Nesting Level of Composites States (NLCS) has an impact on the understanding of the diagrams through a controlled experiment and a replication. In this paper, we present the experimental process and the main findings of them. Unfortunately, the obtained results are not quite conclusive and we have not been able to find an optimal use of nesting within UML statechart diagrams and further empirical research is needed, considering more complex UML statechart diagrams.

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.

The fact that the usage of metrics in the analysis and design of object oriented (OO) software can help designers make better decisions is gaining relevance in software measurement arena. Moreover, the necessity of having early indicators of external quality attributes, such as maintainability, based on early metrics is growing. In addition to this, the aim is to show how early metrics which measure internal attributes, such as structural complexity and size of UML class diagrams, can be used as early class diagram maintainability indicators. For this purpose, we present a controlled experiment and its replication, which we carried out to gather the empirical data, which in turn is the basis of the current study. From the results obtained, it seems that there is a reasonable chance that useful class diagram maintainability models could be built based on early metrics. Despite this fact, more empirical studies, especially using data taken form real projects performed in industrial settings, are needed in order to obtain a comprehensive body of knowledge and experience.

As a key early artefact in the development of OO software, the quality of class diagrams is crucial for all later design work and could be a major determinant for the quality of the software product that is finally delivered. Quantitative measurement instruments are useful to assess class diagram quality in an objective way, thus avoiding bias in the quality evaluation process. This paper presents a set of metrics - based on UML relationships $which measure UML class diagram structural complexity following the idea that it is related to the maintainability of such diagrams. Also summarized are two controlled experiments carried out in order to gather empirical evidence in this sense. As a result of all the experimental work, we can conclude that most of the metrics we proposed (NAssoc, NAgg, NaggH, MaxHAgg, NGen, NgenH and MaxDIT) are good indicators of class diagram maintainability. We cannot, however, draw such firm conclusions regarding the NDep metric.

The fact that the usage of metrics at early phases of OO development can help designers make better decisions is gaining relevance. Moreover, the necessity of having early indicators of external quality attributes, such as understandability, based on early metrics is growing. There exists several works related to metrics for UML structural diagrams such as class diagrams. However, UML behavioral diagrams metrics have been disregarded in the software measurement arena. This fact leaded us to define a set of metrics for the size and structural complexity of UML statechart diagrams. Apart from the definition of the metrics, a contribution of this study is the methodological approach that was followed to theoretically validate them and to empirically validate them as understandability indicators.

In software engineering it is widely acknowledged that the usage of metrics at the initial phases of the object oriented software life cycle can help designers to make better decisions and to predict external quality attributes, such as maintainability. Following this idea we have carried out three controlled experiments to ascertain if any correlation exists between the structural complexity and the size of UML class diagrams and their maintainability. We used 8 metrics for measuring the structural complexity of class diagrams due to the usage of UML relationships, and 3 metrics to measure their size. With the aim of determining which of these metrics are really relevant to be used as class diagrams maintainability indicators, we present in this work a study based on Principal Component Analysis. The obtained results show that the metrics related to associations, aggregations, generalizations and dependencies, are the most relevant whilst those related to size seem to be redundant.

One of the principal objectives of software engineering is to improve the quality of software products. It is widely recognised that the quality assurance of software products must be guaranteed from the early phases of development. As a key artifact produced in the early development of object-oriented (OO) information systems (OOISs), class diagram quality has a great impact on the quality of the software product which is finally delivered. Hence, class diagram quality is a crucial issue that must be evaluated (and improved if necessary) in order to obtain quality OOISs, which is the main concern of present-day software development organisations. After a thorough review of the existing OO measures that are applicable to class diagrams at a high-level design stage, M. Genero et al. (2000) presented in a set of metrics for the structural complexity of class diagrams built using the Unified Modelling Language (UML). We focus on class diagram structural complexity, an internal quality attribute which we believe could be closely correlated with one of the most critical external quality attributes, such as class diagram maintainability. Since the main goal of this paper is the empirical validation of those metrics, we present two controlled experiments carried out to corroborate if those metrics are closed to class diagram maintainability and thus could be used as early maintainability indicators. Based on data collected in the experiments, we build a prediction model for class diagram maintainability using a method for the induction of fuzzy rules.

The quality of object oriented information systems (OOIS) depends greatly on the decisions taken at the initial phases of their development. In a typical object oriented information systems development a class diagram is first built. Class diagrams lay the foundation for all later design work. So, their quality heavily affects on the product that will be ultimately implemented. Even though the appearance of the Unified Modelling Language (UML) as a standard of modelling OOIS have provided a great contribution towards building quality OOIS, it is not enough. Early availability of metrics is a key factor in the successful management of OOIS development. The goal of this work is to propose a set of metrics in order to assess the complexity of UML class diagrams. We also put the proposed metrics under empirical validation in order to provide empirical support to their practical significance and usefulness.

The quality of object oriented information systems (OOIS) depends greatly on the decisions taken at early phases of their development. As an early available artifact the quality of the class diagram is crucial to the success of system development. Class diagrams lay the foundation for all later design work. So, their quality heavily affects the product that will be ultimately implemented. Even though the appearance of the Unified Modeling Language (UML) as a standard of modelling OOIS has contributed greatly towards building quality OOIS, it is not enough. Early availability of metrics is a key factor in the successful management of OOIS development. The aim of this paper is to present a set of metrics for measuring the structural complexity of UML class diagrams and to use them for predicting their maintainability that will heavily be correlated with OOIS maintainability.

In a global and increasingly competitive market, quality is a critical success factor for all economical and organisational aspects and especially in Information Systems (IS). We can affirm that in the next millennium information quality will be an essential factor for company success in the same way product and service quality have been over the last years. It is essential to tackle the subject of information quality in order to achieve a good IS for the company; this way data become true information and knowledge. Companies must manage information as an important product, capitalise knowledge as a main asset, surviving and prospering in the digital economy (Huang et al., 1998). Improving information quality will enhance client satisfaction and, at the same time, personnel satisfaction, while improving the company as a whole. Unfortunately until a few years ago, quality approaches focused on program quality and disregarded information quality (Sneed and Foshag, 1998). Even in traditional information modeling and database design, quality related aspects have not been incorporated explicitly (Wang and Madnick, 1993). It is time to consider information quality as a main goal to pursue, instead of as a subproduct of information modeling or a database creation processes. Quality in information modelling has traditionally been a poorly understood area. Most of the work done until a few years ago was limited to listing a set of properties or desirable characteristics for conceptual data models and proposing different transformations for improving schema quality (Batini et al., 1992; Reingruber and Gregory, 1994; Boman et al., 1997). Recently, some interesting frameworks have been proposed for addressing quality in information modeling in a more systematic way (Moody and Shanks, 1994; Krogstie et al., 1995; Shanks and Darke, 1997; Moody et al., 1998). However, quality criteria alone are not enough to ensure the quality in practice because people will generally make different interpretations of the same concept. According to the Total Quality Management (TQM) literature, measurable criteria for assessing quality is necessary to avoid “arguments of style” (Zultner, 1992). Measurement is fundamental in order to apply statistical process control which is one of the key techniques in the TQM approach (Deming, 1986). Measurement is used not only for understanding, controlling, and improving development, but also for determining the best ways to help practitioners and researchers (Schneidewind, 1997). The objective should be to replace intuitive notions of quality in information modeling, with formal, quantitative measures, thus, helping to reduce subjectivity and bias in the evaluation process. In this chapter we will give an overview of the work carried out regarding quality in information modeling, and we will also propose a set of new metrics for evaluating quality in information modeling. Finally, we discuss future and emerging trends in this area and provide some concluding remarks.

Early and precise models started to play an increasingly relevant role since models themselves become the primary focus in recent initiatives of Model-Driven Engineering (such as Model-Driven Development and Model-Driven Architecture). However, a precise model cannot be obtained through the use of Unified Modeling Language (UML), due to the limited expressiveness of diagram-based UML notation. A textual add-on to the UML diagrams is needed, such as the Object Constraint Language (OCL), for reaching complete and consistent models and avoiding underspecification. Aware of the proliferation of measures for UML-based models and the lack of measures to capture the quality aspects of UML/OCL combined models we defined a set of measures for measuring the structural properties of OCL expressions. This paper carefully describes an experiment we have conducted to confirm the conclusions and strengthen the external validity of a previous family of experiments, with the purpose of investigating the relationship between object coupling in OCL expressions and the understandability and modifiability of OCL expressions. Empirical evidence that such a relationship exists is reaffirmed and consolidated.

Measuring quality is the key to developing high-quality software, and it is widely recognised that quality assurance of software products must be assessed focusing on early artifacts, such as class diagrams. After having thoroughly reviewed existing OO measures applicable to class diagrams at a high-level design stage, a set of metrics for the structural complexity of class diagrams obtained using Unified Modeling Language (UML) was defined. This paper describes a controlled experiment carried out in order to corroborate whether the metrics are closely related to UML class diagram modifiability. Based on data collected in the experiment, a prediction model for class diagram modifiability using a method for induction of fuzzy rules was built. The results of this experiment indicate that the metrics related to aggregation and generalization relationships are the determinant of class diagram modifiability. These findings are in the line with the conclusions drawn from two other similar controlled experiments.