Search

Enterprise architecture allows companies to deal with digital transformation through the proactive presentation and alignment of business and IT in a holistic manner. One important challenge is EA ...

Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challenge the adoption of agile methods as prescribed by their creator(s), software processes in practice mutate into hybrids over time. Are these still agile? In this article, we investigate the question: what makes a software development method agile? We present an empirical study grounded in a large-scale international survey that aims to identify software development methods and practices that improve or tame agility. Based on 556 data points, we analyze the perceived degree of agility in the implementation of standard project disciplines and its relation to used development methods and practices. Our findings suggest that only a small number of participants operate their projects in a purely traditional or agile manner (under 15%). That said, most project disciplines and most practices show a clear trend towards increasing degrees of agility. Compared to the methods used to develop software, the selection of practices has a stronger effect on the degree of agility of a given discipline. Finally, there are no methods or practices that explicitly guarantee or prevent agility. We conclude that agility cannot be defined solely at the process level. Additional factors need to be taken into account when trying to implement or improve agility in a software company. Finally, we discuss the field of software process-related research in the light of our findings and present a roadmap for future research., Comment: Journal paper, 17 pages, 14 figures

Context: Programming courses are compulsory for most engineering degrees, but students’ performance on these courses is often not as good as expected. Programming is difficult for students to learn, given that it includes a lot of new, complex, and abstract topics. All of this has led experts to the conclusion that new teaching techniques are required if students are to be motivated and engaged in learning on programming courses. Gamification has come to be an effective technique in education in general, and is especially useful in programming courses. This motivated us to develop an open source gamified platform, called UDPiler, for use in a programming course. Objective: The main goal of this article is to obtain empirical evidence on the improvement of students’ learning performance when using UDPiler in comparison to a non-gamified compiler. Method: A quasi-experiment was performed with two groups of first-year engineering students at Diego Portales University in Chile, using a non-gamified compiler and a gamified platform, respectively. Results: The results reveal that the students obtained better marks when the gamified platform was used to learn C programming. In addition, there is statistical significance in favor of there being a positive effect on the learning performance of those students who used the gamified platform. Conclusions: The results allow us to conclude that gamification is an encouraging approach with which to teach C programming, a finding that is aligned with previous empirical studies concerning gamification on programming courses, carried out in academic contexts. Nonetheless, we are aware that further validation is also required to corroborate and strengthen the findings obtained and to investigate whether the kind of gamified elements (mechanics, dynamics, and aesthetics) used have any influence on students’ performance, among other issues that deserve further investigation and that are explained throughout this article.

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.

Context Lack of coordination may create significant problems between work teams, this problem is even most critical when team workers are geographically distributed as it results in cost increases and delays in the projects. There exists a technique called Socio-Technical Congruence (STC) that aims at helping to measure and control the coordination level existing in an organization at their different levels. Objective The objective of this paper is to carry out a systematic mapping of the field of socio-technical congruence. The aspects of particular interest for this article are: Socio-Technical Congruence definition, different ways to measure it, available tools to measure or that can help to measure it, the areas of application, its benefits and the case studies that analyze the effects Socio-Technical Congruence has on the organization as regards the products quality and the improvements in performance in the long term development, in an attempt to characterize the state of the art of this field identifying gaps and opportunities for further research. Therefore, companies could use this work as a starting point to apply STC measures in their work teams. Results This paper presents the results of a systematic mapping of Literature about Socio-Technical Congruence (STC) in order to investigate and classify the existing articles and conferences about the subject, as well as summarizing the most important aspects in regards to provide a general overview about the existing studies. Conclusions After analyzing the 40 papers found, we can conclude that there is no one standard measure of socio-technical congruence, although most take the proposal by Kwan et al. applying adaptations and improvements on it as regards the environment that it will be focused on. In general, most case studies talk about the benefits of STC control in organizations. However, only one paper focus on global software development where the problems of communication, coordination and control are an important risk. Moreover, there are only a few papers that explore the risks of excessively overloading users with coordination iterations when controlling STC. In fact, no case study to examine these risks and their effect on developers’ productivity has been found. The small number of studies found on STC, together with the research gaps we have pointed out, suggest that further investigation on socio-technical congruence 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.

Software development methods are usually not applied by the book. Companies are under pressure to continuously deploy software products that meet market needs and stakeholders' requests. To implement efficient and effective development processes, companies utilize multiple frameworks, methods and practices, and combine these into hybrid methods. A common combination contains a rich management framework to organize and steer projects complemented with a number of smaller practices providing the development teams with tools to complete their tasks. In this paper, based on 732 data points collected through an international survey, we study the software development process use in practice. Our results show that 76.8% of the companies implement hybrid methods. Company size as well as the strategy in devising and evolving hybrid methods affect the suitability of the chosen process to reach company or project goals. Our findings show that companies that combine planned improvement programs with process evolution can increase their process' suitability by up to 5%., Conference, 10 pages, 4 figures, 10 tables

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.

Despite its many successes, requirement engineering (RE) still presents a series of challenges and limitations. Many of the challenges identified almost a decade ago are still present; in some cases, they are accentuated by the increasing complexity, extension and depth of software systems. Design Thinking (DT) is a process that emerged from design areas; due to its effectiveness in solving certain types of problems (wicked problems), its use has spread to other domains. The RE has historically been enriched by the contribution of other domains, theoretical models and frameworks. The contribution of this paper is its reflections on the potential benefits of DT techniques in solving current challenges in the practice of RE. Our contribution is based mainly on our experience as practitioners who train companies in DT, and in the analysis on recent research on the use of DT in RE.

Processes and practices are tools that organizations use to improve their capabilities. Agile transformations are very popular, as are process and practice improvement and adoption initiatives, but they face many challenges, including low adoption rates. Improving process and practice usability might increase adoption rates and effective use. This idea led us to define a Usability Model for Software development Process and Practice (UMP), consisting of characteristics and metrics, in the quest to improve the work experience of software development practitioners and the effectiveness of process and practice adoption initiatives. The goal of this paper is two-fold: (1) to present the refined version of the UMP and (2) to describe a study on the application of the UMP to the Visual Milestone Planning (VMP) method in order to evaluate UMP’s utility, specifically its ability to produce useful feedback in a real-life scenario. The study produced preliminary confirmation that the UMP is applicable to the VMP, along with specific feedback on improvement opportunities for the VMP. An interview with the VMP creator confirmed that the UMP model and the evaluation feedback were valuable for enhancing VMP adoption. In summary, we can conclude that the empirical results obtained show that UMP can be useful. Nonetheless, more studies are needed to provide further confirmation in different scenarios.

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 tremendous prosperity of big data systems that has occurred in recent years has made its understanding crucial for both research and industrial communities. Big Data is expected to generate an economy of 15 billion euros over the next few years and to have repercussions that will more or less directly change the way in which we live. It is, therefore, important for organizations to have quality Database Management Systems (DBMSs) that will allow them to manage large volumes of data in real time and according to their needs. The last decade has witnessed an explosion of new Database Management Systems (DBMSs) which deal not only with relational Data Bases but also with non-relational Data Bases. Companies need to assess DBMS quality in order, for example, to select which DBMS is most appropriate for their needs. The main research question formulated in this research is, therefore, “What is the state of the art of Big Data DBMS assessment?”, which we attempt to answer by following a well-known methodology called “Systematic Mapping Studies” (SMS). This paper describes an SMS of papers published until May 2016. Five digital libraries were searched, and 19 papers were identified and classified into five dimensions: quality characteristics of Big Data DBMSs, techniques and measures used to assess the quality characteristics, DBMSs whose quality has been measured, evolution over time and research methods utilized. The results indicate that there are several benchmarks, which are principally focused on the performance of MongoDB and Cassandra, and that the interest in Big Data DBMS quality is growing. Nonetheless, more research is needed in order to define and validate a quality model that will bring together all the relevant characteristics of DBMSs for Big Data and their respective measures. This quality model will then be employed as a basis on which to build benchmarks for DBMSs, covering not only the diversity of DBMSs and application scenarios and types of applications, but also diverse and representative real-world data sets.

Context/Background: Process and practice adoption is a key element in modern software process improvement initiatives, and many of them fail.

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 aim of this paper is to present the definition of the measures for the import-coupling of OCL expressions, along with the empirical validation of these as early indicators of the maintainability of OCL expressions. This empirical validation has been carried out by means of an experiment and its replica, conducted with undergraduate students of Spanish and Argentinean universities, respectively. To perform this experimental activity, we have followed a cognitive theory-based approach, since the understanding of the cognitive demands that OCL expressions place on software engineers will allow us to examine in greater depth the real influence of import-coupling on the maintainability of OCL expressions. The empirical results, obtained through the analysis of the data from the experiment and its replica, first of all separately and then together after a meta-analysis study, reveal evidence suggesting that import-coupling exerts a certain amount of influence on the comprehensibility and modifiability of OCL expressions. The measures that have most influence on OCL expression comprehensibility are those related to problem objects (Number of Navigated Relationships (NNR), Weighted Number of Navigations (WNN), Depth of Navigations (DN) and Number of Attributes referred through Navigations (NAN)), relationships between problem objects (Number of Navigated Classes (NNC) and Number of Explicit Iterator variables (NEI)), as well as reified objects (Weighted Number of Collections Operations (WNCO)). On the other hand, it is only measures related to relationships between problem objects that are the main influence on OCL expression modifiability. The influence of import-coupling on the comprehensibility and modifiability of OCL expressions was reaffirmed through the cognitive complexity (i.e. we show that import-coupling affects the cognitive complexity and that the latter influences the comprehension and modification of OCL expressions). These results may have educational implications, apart from what they might mean for practitioners in the industry, as is explained in the conclusions.

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.

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.

This paper has the following goals: (1) To introduce a preliminary version of a Serious Game Quality Model (QSGame-Model), (2) To present an example of the application of this model and (3) To outline the future empirical studies that we plan to perform in order to refine and validate the model.

Navigability, defined as the efficiency, effectiveness and satisfaction with which a user navigates through the system in order to fulfil her goals under specific conditions, has a definite impact on the overall success of Web applications. This quality attribute can be measured based on the navigational model provided by Web Engineering methodologies. Most of the measures currently defined for navigational models are tightly coupled with particular Web Engineering methodologies, however. Furthermore, modifications to the design of the navigational model, carried out with the aim of improving navigability, are performed manually. Both practices have seriously hampered the reusability and adoption of proposed navigability measures and improvement techniques. In this paper we present a Model-Driven Engineering approach to solving these problems. On the one hand, we propose a generic approach which defines navigability measurement models that can be integrated into any Web Engineering methodology. On the other hand, we present a model-driven improvement process for the navigational model design which incurs no increase in costs or in timeto- market of Web applications. This process is divided into two phases: evaluation (i.e. assessment of the model through objective measures) and evolution (i.e. transformation of the model when the measurement results do not accomplish certain quality decision criteria that have been defined previously).

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.

Gamified software is currently very popular, and it is expected that it will be widely adopted over the coming years. The social impact of gamified software will probably be very high, and we therefore believe that the assessment and improvement of gamified software quality may be necessary. The aim of this paper is to present a systematic mapping study (SMS) carried out to discover the current state of the research on software gamification quality, in order to identify gaps that merit rigorous future investigation. This SMS allowed us to select 35 papers found in five digital libraries up to April 2014. This paper summarizes the main issues of the planning and the conducting of the SMS. The main results of the data synthesis are detailed and future work is also outlined.

This paper presents the findings of a survey on the use of UML in software maintenance, carried out with 178 professionals working on software maintenance projects in 12 different countries. As part of long-term research we are carrying out to investigate the benefits of using UML in software maintenance, the main objectives of this survey are: 1) to explore whether UML diagrams are being used in software industry maintenance projects; 2) to see what UML diagrams are the most effective for software maintenance; 3) to find out what the perceived benefits of using UML diagrams are; and 4) to contextualize the kind of companies that use UML documentation in software maintenance. Some complementary results based on the way the documentation is used (whether it is UML-based or not) during software maintenance are also presented.

A Serious Game (SG) is a game for purposes other than mere entertainment. SGs are currently in widespread use and their popularity has begun to increase steadily. The number of users of these systems is also growing day-by-day, signifying that their social impact is very high; it is precisely for this reason that SG quality evaluation is of the utmost importance. The principal objective of our long-term research, initiated one year and a half ago, is therefore to define and validate a quality model adapted specifically to SGs that has been agreed on by experts and is useful in practice, in order to allow SG designers and developers to ensure, evaluate and improve the quality of the SGs they build from the early stages of its development. The main goal of this paper is to present the construction processes of a preliminary version of the SG Quality Model (QSGame-Model) adapted from the ISO/IEC 25010 standard, which is a product quality model that can be applied to any kind of SG and will be validated in the near future.

Although software measurement plays an increasingly important role in Software Engineering, there is no consensus yet on many of the concepts and terminology used in this field. Even worse, vocabulary conflicts and inconsistencies can be frequently found amongst the many sources and references commonly used by software measurement researchers and practitioners. This article presents an analysis of the current situation, and provides a comparison framework that can be used to identify and address the discrepancies, gaps, and terminology conflicts that current software measurement proposals present. A basic software measurement ontology is introduced, that aims at contributing to the harmonization of the different software measurement proposals and standards, by providing a coherent set of common concepts used in software measurement. The ontology is also aligned with the metrology vocabulary used in other more mature measurement engineering disciplines.

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.

Several authors have pointed out that current conceptual models have two main shortcomings. First, they are clearly oriented to a specific development paradigm (structured, objects, etc.). Second, once the conceptual models have been obtained, it is really difficult to switch to another development paradigm, because the model orientation to a specific development approach. This fact induces problems during development, since practitioners are encouraged to think in terms of a solution before the problem at hand is well understood, thus anticipating perhaps bad design decisions.An appropriate analysis task requires models that are independent of any implementation issues. In concrete, models should support developers to understand the problem and its constraints before any solution is identified. This paper proposes such an alternative approach to conceptual modelling, called "problem-oriented analysis method".

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).

It is generally accepted in the information system (IS) field that IS quality is highly dependent on the decisions made early in the development life cycle. The construction of conceptual data models is often an important task of this early development. Therefore, improving the quality of conceptual data models will be a major step towards the quality improvement of the IS development. Several quality frameworks for conceptual data models have been proposed, but most of them lack valid quantitative measures in order to evaluate the quality of conceptual data models in an objective way. In this article we will define measures for the structural complexity (internal attribute) of entity relationship diagrams (ERD) and use them for predicting their maintainability (external attribute). We will theoretically validate the proposed metrics following Briand et al.'s framework with the goal of demonstrating the properties that characterise each metric. We will also show how it is possible to predict each of the maintainability sub-characteristics using a prediction model generated using a novel method for induction of fuzzy rules.

Databases are the core of Information Systems (IS). It is, therefore, necessary to ensure the quality of the databases in order to ensure the quality of the IS. Metrics are useful mechanisms for controlling database quality. This paper presents two metrics related to referential integrity, number of foreign keys (NFK) and depth of the referential tree (DRT) for controlling the quality of a relational database. However, to ascertain the practical utility of the metrics, experimental validation is necessary. This validation can be carried out through controlled experiments or through case studies. The controlled experiments must also be replicated in order to obtain firm conclusions. With this objective in mind, we have undertaken different empirical work with metrics for relational databases. As a part of this empirical work, we have conducted a case study with some metrics for relational databases and a controlled experiment with two metrics presented in this paper. The detailed experiment described in this paper is a replication of the later one. The experiment was replicated in order to confirm the results obtained from the first experiment. As a result of all the experimental works, we can conclude that the NFK metric is a good indicator of relational database complexity. However, we cannot draw such firm conclusions regarding the DRT metric.

Developing and selecting high quality software applications are fundamental. It is important that the software applications can be evaluated for every relevant quality characteristic using validated metrics. Software engineers have been putting forward hundreds of quality metrics for software programs, disregarding databases. However, software data aspects are important because the size of data and their system nature contribute to many aspects of a systems quality. In this paper, we proposed some internal metrics to measure relational databases which influence its complexity. Considering the main characteristics of a relational table, we can propose the number of attributes (NA) of a table, the depth of the referential tree (DRT) of a table, and the referential degree (RD) of a table. These measures are characterized using measurement theory, particularly the formal framework proposed by Zuse. As many important issues faced by the software engineering community can only be addressed by experimentation, an experiment has been carried out in order to validate these metrics.

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.

Most software development projects apply modelling in some stages of development and to various degrees in order to take advantage of the many and varied benefits of it. Modelling is, for example, applied for facilitating communication by hiding technical details, analysing a system from different perspectives, specifying its structure and behaviour in an understandable way, or even for enabling simulations and generating test cases in a mode-driven engineering approach. Thus, the evaluation of modelling techniques, languages and tools is needed in order to assess their advantages and disadvantages, to ensure their applicability to different contexts, their ease of use, and other issues such as required skills and costs; either isolated or in comparison with other methods.

The transfer of research results into production systems requires, among other things, to make these results explicit and understandable for stakeholders. Many researchers have been studying means to favour knowledge acquisition on the behalf of stakeholders using not only scientific papers. In fact, scientific papers have limitations in the transferability and reusability of knowledge owing to: size limitations that do not permit all the necessary knowledge to be exploited; structure and contents heterogeneity that could cause conflicts; and redundancy and incoherence between papers dealing with same topic. We therefore propose the concept of the Knowledge Experience Package (KEP) that contains both the conceptual “model(s) of the research results which make up the innovation, including all the necessary documentation ranging from papers or book chapters, and the” [42] appropriately structured experience acquired from the business processes. The KEP conceptual model has been implemented in a Knowledge Experience Base (KEB) called PROMETHEUS. The principal goal of this work is to assess whether the knowledge transferred by a KEP, and delivered by PROMETHEUS, adds comprehensibility to the knowledge transferred by scientific papers. A controlled experiment was therefore carried out with 14 IT professionals contracted in projects led by the Alarcos research group at the University of Castilla-La Mancha (Spain), all of whom have degrees in Computer Science. The comprehension of the knowledge transferred was analyzed from three different perspectives: semantic comprehension, retention and transfer. The evidence collected permits us to state that the structure of the KEP increases the ability to retain knowledge in a significant manner than occurs with scientific papers only. The results have encouraged us to carry out further studies in order to refine the KEP structure, although we are conscious that the results should be considered as preliminary.

[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.

Numerous methods and techniques have been proposed for requirements modeling, although very few have had widespread use in practice. One drawback of requirements modeling methods is that they lack proper empirical evaluations. This means that there is a need for evaluation methods that consider both the theoretical and practical aspects of this type of methods and techniques. In this paper, we present a method for evaluating the quality of requirements modeling methods based on user perceptions. The evaluation method consists of a theoretical model that explains the relevant dimensions of quality for requirements modeling methods, along with a practical instrument with which to measure these quality dimensions. Basically, it allows us to predict the acceptance of a particular requirements modeling method in practice, based on the effort of applying the method, the quality of the requirements artifacts produced, and the user perceptions with regard to the quality of the method. The paper also presents an empirical test of the proposed method for evaluating a Rational Unified Process (RUP) extension for requirements modeling. That test was carried out through a family of experiments conducted with students and practitioners and provides evidence of the usefulness of the evaluation method proposed. © 2011 Elsevier Inc. All rights reserved., This research has been funded by the following projects: MULTIPLE (MICINN TIN2009-13838), MEDUSAS (CDTI-MICINN and FEDER IDI-20090557), ORIGIN (CDTI-MICINN and FEDER IDI-2010043(1-5)), PEGASO/MAGO (MICINN and FEDER, TIN2009-13718-C02-01), EECCOO (MICINN TRA2009_0074), MECCA (JCMM PII2109-0075-8394) and IMPACTUM (JCCM PEII11-0330-4414).

Although some methodologies for evaluating the quality of software artifacts exist, all of these are isolated proposals, which focus on specific artifacts and apply specific evaluation techniques. There is no generic and flexible methodology that allows quality evaluation of any kind of software artifact, regardless of type, much less a tool that supports this. To tackle that problem in this paper, we propose the CQA Environment, consisting of a methodology (CQA-Meth) and a tool that implements it (CQA-Tool). We began applying this environment in the evaluation of the quality of UML models (use cases, class and statechart diagrams). To do so, we have connected CQA-Tool to the different tools needed to assess the quality of models, which we also built ourselves. CQA-Tool, apart from implementing the methodology, provides the capacity for building a catalogue of evaluation techniques that integrates the evaluation techniques (e.g. metrics, checklists, modeling conventions, guidelines, etc.) which are available for each software artifact. CQA Environment is suitable for use by companies that offer software quality evaluation services, especially for clients who are software development organizations and who are outsourcing software construction. They will obtain an independent quality evaluation of the software products they acquire. Software development organizations that perform their own evaluation will be able to use it as well.

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.

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.