A framework for the definition of metrics for actor-dependency models

Actor-dependency models are a formalism aimed at providing intentional descriptions of processes as a network of dependency relationships among actors. This kind of models is currently widely used in the early phase of requirements engineering as well as in other contexts such as organizational analysis and business process reengineering. In this paper, we are interested in the definition of a framework for the formulation of metrics over these models. These metrics are used to analyse the models with respect to some properties that are interesting for the system being modelled, such as security, efficiency or accuracy. The metrics are defined in terms of the actors and dependencies of the model. We distinguish three different kinds of metrics that are formally defined, and then we apply the framework at two different layers of a meeting scheduler system.Postprint (published version

Matching demand and offer in on-line provision: A longitudinal study of monster.com

This is the post-print version of the final published paper that is available from the link below.When considering the jobs market, changes or recurring trends for skilled employees expressed by employers' needs have a tremendous impact on the evolution of website content. On-line jobs sites adverts, academic institutions and professional development “standard bodies” all share those needs as their common driver for contents evolution. This paper aims, on one hand, to discuss and to analyse how current needs and requirements (“demand”) of IT skills in the UK job market drive the contents of different types of websites, in turn analysing whether this demand changes and how. On the other hand, it is studied what the UK higher education institutions have to offer to fulfill this demand. The results found analysing the evolution of the largest on-line job centre (www.monster.com), and the websites of selected UK academic institutions, demonstrate that often what is requested by UK industries is not clearly offered by UK institutions. Given the prominence of monster.com in the global economy, these results could provide a meaningful starting point to support curricula development in UK, as much as worldwide

A Domain Analysis to Specify Design Defects and Generate Detection Algorithms

Quality experts often need to identify in software systems design defects, which are recurring design problems, that hinder development\ud and maintenance. Consequently, several defect detection approaches\ud and tools have been proposed in the literature. However, we are not\ud aware of any approach that defines and reifies the process of generating\ud detection algorithms from the existing textual descriptions of defects.\ud In this paper, we introduce an approach to automate the generation\ud of detection algorithms from specifications written using a domain-specific\ud language. The domain-specific is defined from a thorough domain analysis.\ud We specify several design defects, generate automatically detection\ud algorithms using templates, and validate the generated detection\ud algorithms in terms of precision and recall on Xerces v2.7.0, an\ud open-source object-oriented system

Agent-based simulation of open source evolution

We present an agent-based simulation model developed to study how size, complexity and effort relate to each other in the development of open source software (OSS). In the model, many developer agents generate, extend, and re-factor code modules independently and in parallel. This accords with empirical observations of OSS development. To our knowledge, this is the first model of OSS evolution that includes the complexity of software modules as a limiting factor in productivity, the fitness of the software to its requirements, and the motivation of developers. Validation of the model was done by comparing the simulated results against four measures of software evolution (system size, proportion of highly complex modules, level of complexity control work, and distribution of changes) for four large OSS systems. The simulated results resembled the observed data, except for system size: three of the OSS systems showed alternating patterns of super-linear and sub-linear growth, while the simulations produced only super-linear growth. However, the fidelity of the model for the other measures suggests that developer motivation and the limiting effect of complexity on productivity have a significant effect on the development of OSS systems and should be considered in any model of OSS development

An Empirical Study of the Effectiveness of 'Forcing Diversity' Based on a Large Population of Diverse Programs

Use of diverse software components is a viable defence against common-mode failures in redundant softwarebased systems. Various forms of "Diversity-Seeking Decisions" (“DSDs”) can be applied to the process of developing, or procuring, redundant components, to improve the chances of the resulting components not failing on the same demands. An open question is how effective these decisions, and their combinations, are for achieving large enough reliability gains. Using a large population of software programs, we studied experimentally the effectiveness of specific "DSDs" (and their combinations) mandating differences between redundant components. Some of these combinations produced much better improvements in system probability of failure per demand (PFD) than "uncontrolled" diversity did. Yet, our findings suggest that the gains from such "DSDs" vary significantly between them and between the application problems studied. The relationship between DSDs and system PFD is complex and does not allow for simple universal rules (e.g. "the more diversity the better") to apply

A Nine Month Report on Progress Towards a Framework for Evaluating Advanced Search Interfaces considering Information Retrieval and Human Computer Interaction

This is a nine month progress report detailing my research into supporting users in their search for information, where the questions, results or even thei

From a Domain Analysis to the Specification and Detection of Code and Design Smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud on development and maintenance. Consequently, several smell detection\ud approaches and tools have been proposed in the literature. However,\ud so far, they allow the detection of predefined smells but the detection\ud of new smells or smells adapted to the context of the analysed systems\ud is possible only by implementing new detection algorithms manually.\ud Moreover, previous approaches do not explain the transition from\ud specifications of smells to their detection. Finally, the validation\ud of the existing approaches and tools has been limited on few proprietary\ud systems and on a reduced number of smells. In this paper, we introduce\ud an approach to automate the generation of detection algorithms from\ud specifications written using a domain-specific language. This language\ud is defined from a thorough domain analysis. It allows the specification\ud of smells using high-level domain-related abstractions. It allows\ud the adaptation of the specifications of smells to the context of\ud the analysed systems.We specify 10 smells, generate automatically\ud their detection algorithms using templates, and validate the algorithms\ud in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud v1.10.2, two open-source object-oriented systems.We also compare\ud the detection results with those of a previous approach, iPlasma

Decision support system for the long-term city metabolism planning problem

A Decision Support System (DSS) tool for the assessment of intervention strategies (Alternatives) in an Urban Water System (UWS) with an integral simulation model called “WaterMet²” is presented. The DSS permits the user to identify one or more optimal Alternatives over a fixed long-term planning horizon using performance metrics mapped to the TRUST sustainability criteria (Alegre et al., 2012). The DSS exposes lists of in-built intervention options and system performance metrics for the user to compose new Alternatives. The quantitative metrics are calculated by the WaterMet² model and further qualitative or user-defined metrics may be specified by the user or by external tools feeding into the DSS. A Multi-Criteria Decision Analysis (MCDA) approach is employed within the DSS to compare the defined Alternatives and to rank them with respect to a pre-specified weighting scheme for different Scenarios. Two rich, interactive Graphical User Interfaces, one desktop and one web-based, are employed to assist with guiding the end user through the stages of defining the problem, evaluating and ranking Alternatives. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple Scenarios. The efficacy of the DSS is demonstrated on a northern European case study inspired by a real-life urban water system for a mixture of quantitative and qualitative criteria. The results demonstrate how the DSS, integrated with an UWS modelling approach, can be used to assist planners in meeting their long-term, strategic level sustainability objectives