Manuel F. Bertoa

Towards a consistent terminology for software measurement

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.

Effective use of ontologies in software measurement

Ontologies are frequently used in the context of software and technology engineering. These can be grouped into two main categories, depending on whether they are used to describe the knowledge of a domain (domain ontologies) or whether they are used as software artifacts in software development processes. This paper presents some experiences and lessons learnt from the effective use of an ontology for Software Measurement, called software measurement ontology (SMO). The SMO was developed some years ago as a result of a thorough analysis of the software measurement domain. Its use as a domain ontology is presented first, a description of how the SMO can serve as a conceptual basis for comparing international standards related to software measurement. Second, the paper describes several examples of the applications of SMO as a software artifact. In particular, we show how the SMO can be instantiated to define a data quality model for Web portals, and also how it can be used to define a Domain-Specific Language (DSL) for measuring software entities. These examples show the significant role that ontologies can play as software artifacts in the realm of model-driven engineering and domain-specific modeling.

A systematic literature review for software sustainability measures

Nowadays, sustainability is a key factor that should be considered in the software quality models. It is increasingly important how environmentally friendly is a software product, both in its execution and during its development process. Therefore, we have proposed, in a previous work, a quality model (25010+S) an extension of the ISO/IEC 25010 standard by considering aspects of sustainability on its characteristics and sub-characteristics. However, in order to make the model useful, it is necessary to identify measures for each sub-characteristic and characteristic. For that reason, the objective of this paper is to carry out a Systematic Literature Review to discover the state-of-the art in software sustainability measures.

Improving interpretation of component-based systems quality through visualisation techniques

Component-based software development is increasingly more commonplace and is widely used in the development of commercial software systems. This has led to the existence of several research works focusing on software component-based systems quality. The majority of this research proposes quality models focused on component-based systems in which different measures are proposed. In general, the result of assessing the measures is a number, which is necessary to determine the component-based system quality level. However, understanding and interpreting the data set is not an easy task. In order to facilitate the interpretation of results, this study selects and adapts a specific visual metaphor with which to show component-based systems quality. A tool has additionally been developed which permits the automatic assessment of the measures to be carried out. The tool also shows the results visually and proposes corrective actions through which to improve the level of quality. A case study is used to assess and to show the quality of a real-world component-based software system in a graphic manner.

Managing Uncertain Complex Events in Web of Things Applications

A critical issue in the Web of Things (WoT) is the need to process and analyze the interactions of Web-interconnected real-world objects. Complex Event Processing (CEP) is a powerful technology for analyzing streams of information about real-time distributed events, coming from different sources, and for extracting conclusions from them. However, in many situations these events are not free from uncertainty, due to either unreliable data sources and networks, measurement uncertainty, or to the inability to determine whether an event has actually happened or not. This short research paper discusses how CEP systems can incorporate different kinds of uncertainty, both in the events and in the rules. A case study is used to validate the proposal, and we discuss the benefits and limitations of this CEP extension.

Green Software and Software Quality

Quality is currently one of the main goals that organisations set themselves. A large number of organisations provide products that are similar to each other, thus permitting consumers to choose from a wide variety of brands. Bearing this situation in mind, companies attempt to develop products of better quality; their survival depends to an increasing extent on the quality of the products and services provided.

Green Software Measurement

Humanity is, in the twenty-first century, having to confront significant environmental challenges, from pollution to excessive energy consumption. Both industry and consumers are reacting to this need, which has led to the appearance of various initiatives for Green Technology. Software Engineering cannot remain indifferent to these needs and should direct its endeavours towards developing software products that take into account their environmental consequences and meet the needs of users. It is therefore possible to find various works in literature that propose Green-oriented quality models, as is the case of Chap. 10. However, although the definition of a Green quality model is the first step and is essential, this is not sufficient. In order to have a useful model it is necessary to define measures. Bearing this in mind, in this chapter we have focused on carrying out a study of the existing Green measures which we have classified in accordance with the product greenability quality model proposed in Chap. 10. We have analysed 192 measures, of which 74 could be applied to software products. As a result, we have obtained that, on the one hand, there is an acceptable number of measures defined to sub-characteristics of energy efficiency, resource optimization and perdurability (41 %, 23 % and 28 %, respectively), while on the other, it should be highlighted that capacity optimization sub-characteristic has a low number (8 %) of defined measures to assess it.

Expressing Measurement Uncertainty in OCL/UML Datatypes.

Uncertainty is an inherent property of any measure or estimation performed in any physical setting, and therefore it needs to be considered when modeling systems that manage real data. Although several modeling languages permit the representation of measurement uncertainty for describing certain system attributes, these aspects are not normally incorporated into their type systems. Thus, operating with uncertain values and propagating uncertainty are normally cumbersome processes, difficult to achieve at the model level. This paper proposes an extension of OCL and UML datatypes to incorporate data uncertainty coming from physical measurements or user estimations into the models, along with the set of operations defined for the values of these types.

Expressing Confidence in Models and in Model Transformation Elements

The expression and management of uncertainty, both in the information and in the operations that manipulate it, is a critical issue in those systems that work with physical environments. Measurement uncertainty can be due to several factors, such as unreliable data sources, tolerance in the measurements, or the inability to determine if a certain event has actually happened or not. In particular, this contribution focuses on the expression of one kind of uncertainty, namely the confidence on the model elements, i.e., the degree of belief that we have on their occurrence, and on how such an uncertainty can be managed and propagated through model transformations, whose rules can also be subject to uncertainty.

Measuring the Quality of Assessment Using Questions Generated from the Semantic Web

This article describes a new feature of the adaptive assessment system SIETTE that allows for the static and dynamic generation of questions from tables of data for knowledge assessment. Almost the same approach can be used to generate questions from data collected in a spreadsheet, a database query, or a semantic web query using SPARQL. The main problem faced with question generation is ensuring that the questions are valid for assessment. For this reason, most of the existing systems propose to use this mechanism only for low-stakes assessments. In this paper, we propose a methodology to control question generation quality and measure the impact of potential invalid instances on the final score as well as recommend some strategies to overcome these problems.