Object-Oriented Software Metrics.

This paper presents some initial results from a twelve-month empirical research study of model driven engineering (MDE). Using largely qualitative questionnaire and interview methods we investigate and document a range of technical, organizational and social factors that apparently influence organizational responses to MDE: specifically, its perception as a successful or unsuccessful organizational intervention. We then outline a range of lessons learned. Whilst, as with all qualitative research, these lessons should be interpreted with care, they should also be seen as providing a greater understanding of MDE practice in industry, as well as shedding light on the varied, and occasionally surprising, social, technical and organizational factors that affect success and failure. We conclude by suggesting how the next phase of the research will attempt to investigate some of these issues from a different angle and in greater depth.

Empirical assessment of MDE in industry

Metrics and Models in Software Quality Engineering

Despite that formal and informal quality aspects are of significantimportance to business process modeling, there is only little empiricalwork reported on process model quality and its impact factors. Inthis paper we investigate understandability as a proxy for quality of processmodels and focus on its relations with personal and model characteristics.We used a questionnaire in classes at three European universitiesand generated several novel hypotheses from an exploratory data analysis.Furthermore, we interviewed practitioners to validate our findings.The results reveal that participants tend to exaggerate the differences inmodel understandability, that self-assessment of modeling competenceappears to be invalid, and that the number of arcs in models has animportant influence on understandability.

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What makes process models understandable

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Size is an important attribute of software artefacts; for most artefact types exists a body of measurement knowledge. As software engineering is becoming more and more model-centric, it is surprising that there exists only little work on model size metrics (MoSMe). In this position paper we identify the goals justifying the need for MoSMe, such as prediction, description and progress measurement. Additionally, we identify challenges that make it difficult to measure the size of UML models and that MoSMe have to deal with. Finally, we propose a classification of MoSMe and concrete examples of metrics for the size of UML models.

Model size matters

As the role of models during software development and maintenance is becoming more and more important, techniques are needed to control a model's quality during evolution. We present our tool MetricViewEvolution as a step towards managing model quality during development and evolution. Six views are implemented in MetricViewEvolution that aid the user in tasks such as model understanding, identification of quality problems and evolution trends. The views combine structural model information with metrics data from inside the model and external sources. MetricViewEvolution has been applied successfully in industrial case studies

MetricViewEvolution: UML-based Views for Monitoring Model Evolution and Quality

UML sequence diagrams describe the interaction between objects of a system. In this paper, we present four approaches to analyze the quality of sequence diagrams. First, we propose formal model checking techniques and visualization to analyze the possible interaction traces and to identify ambiguities in a sequence diagram. Second, we identified patterns of common interactive behavior and we present an approach to identify these patterns. This approach is useful to identify implicit patterns and make them explicit. Third, we propose a series of checks to identify syntactic defects in sequence diagrams. The fourth approach consists of metrics describing properties such as coverage. The proposed approaches complement each other rather than being competing alternatives. We implemented these approaches in our SquAT tool and conducted a multi case study on three industrial and five research models to validate them. These models showed a large number of defects which were acknowledged by their designers.

Four Automated Approaches to Analyze the Quality of UML Sequence Diagrams

The goal of our research is to develop industry-proof software architecture and design metrics. We identify a number of problems that arise in computing software architecture and design metrics in industrial settings that were not encountered in computing source-code metrics. These problems include the absence of a single, unifying representation for architectures and they arise from the fact that architecture diagrams are used in an informal manner. We describe our approach towards defining metrics for architectures and designs which are represented in the 4+1 views paradigm using UML. We report our experiences with architectural metrics in industrial settings.

Investigations in applying metrics to multi-view architecture models

The UML is a collection of 13 diagram notations to describe different views of a software system. The existing diagram types display model elements and their relations. Software engineering is becoming more and more model-centric, such that software engineers start using UML models for more tasks than just describing the system. Tasks such as analysis or prediction of system properties require additional information such as metrics of the UML model or from external sources, e.g. a version control system. In this paper we identify tasks of model-centric software engineering and information that is required to fulfill these tasks. We propose views to visualize the information to support fulfilling the tasks. This paper reports on a large-scale controlled experiment to validate the usefulness of the proposed views that are implemented in our MetricView Evolution tool. The results of the experiment with 100 participants are statistically significant and show that the correctness of comprehension is improved by 4.5% and that the time needed is reduced by 20%.

Christian F. J. Lange

Papers

Model size matters

MetricViewEvolution: UML-based Views for Monitoring Model Evolution and Quality

Four Automated Approaches to Analyze the Quality of UML Sequence Diagrams

Investigations in applying metrics to multi-view architecture models

Supporting task-oriented modeling using interactive UML views