72 research outputs found
Chain Communication Systems
No full textThis article elaborates on large-scale chain communication between autonomous organisations and professionals focused on solving social problems. We introduce a dynamic chain concept combined with a special chain information strategy that may help us to better understand and anticipate the complexities of large-scale information exchange - with or without ICT – in a chain context lacking sufficient overall co-coordinating and enforcing authority. Many chain projects fail and large-scale systems produce unexpected negative side-effects or even backfire. We use the criminal law enforcement chain to explain how these adversities and negative side effects may disrupt a large-scale social system. This example stands as a model for other vital, large-scale systems such as identity management and health care management. At the core of such chains we have a chain communication system enabling the chain partners to co-operate effectively. Chain research at Utrecht University – now covering more than twenty three social chains in the Netherlands – has led to seven valuable insights and breaking views on the theory, practice and the content of chain analysis. We indicate ten challenges for information science and its practitioners as suggestions for future research and development
Editorial on the third founding article "Chain Communication Systems"
No full textSome comments on the significance of this third founding article in conjunction with the other two founding articles of JC
Scientists' needs in modelling software ecosystems
No full textCurrently the landscape of software ecosystem modelling methods and languages is like Babel after the fall of the tower: There are many methods and languages available and interchanging data between researchers and organizations that actively govern their ecosystem, is practically impossible. The lack of a universally accepted set of modelling methods is hampering the advancement of software ecosystems research. Using a literature study and a set of interviews amongst peers, we aim to establish a set of understandings and requirements for a universally accepted set of software ecosystem modelling methods. The work is an initial push in a larger research initiative that has the goal of advancing the maturity of (software) ecosystems modelling. The success of such an initiative will be found in the availability of common databases, better interchange formats between researchers, and more capable software ecosystem modelling tools
A Search-Based Approach to Multi-view Clustering of Software Systems
No full textAbstract—Unsupervised software clustering is the problem of automatically decomposing the software system into meaningful units. Some approaches solely rely on the structure of the system, such as the module dependency graph, to decompose the software systems into cohesive groups of modules. Other techniques focus on the informal knowledge hidden within the source code itself to retrieve the modular architecture of the system. However both techniques in the case of large systems fail to produce decompositions that correspond to the actual architecture of the system. To overcome this problem, we propose a novel approach to clustering software systems by incorporating knowledge from different viewpoints of the system, such as the knowledge embedded within the source code as well as the structural dependencies within the system, to produce a clustering. In this setting, we adopt a search-based approach to the encoding of multi-view clustering and investigate two approaches to tackle this problem, one based on a linear combination of objectives into a single objective, the other a multi-objective approach to clustering. We evaluate our approach against a set of substantial software systems. The two approaches are evaluated on a dataset comprising of 10 Java open source projects. Finally, we propose two techniques based on interpolation and hierarchical clustering to combine different results obtained to yield a single result for single-objective and multi-objective encodings, respectively
Reality cannot be caught within one model: Plea to study the economy and the climate at system level is based on several faulty assumptions
No full textApplications of Multi-view Learning Approaches for Software Comprehension
No full textProgram comprehension concerns the ability of an individual to make an understanding of an existing software system to extend or transform it. Software systems comprise of data that are noisy and missing, which makes program understanding even more difficult. A software system consists of various views including the module dependency graph, execution logs, evolutionary information and the vocabulary used in the source code, that collectively defines the software system. Each of these views contain unique and complementary information; together which can more accurately describe the data. In this paper, we investigate various techniques for combining different sources of information to improve the performance of a program comprehension task. We employ state-of-the-art techniques from learning to 1) find a suitable similarity function for each view, and 2) compare different multi-view learning techniques to decompose a software system into high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search. The experiments conducted on 10 relatively large Java software systems show that by fusing knowledge from different views, we can guarantee a lower bound on the quality of the modularization and even improve upon it. We proceed by integrating different sources of information to give a set of high-level recommendations as to how to refactor the software system. Furthermore, we demonstrate how learning a joint subspace allows for performing cross-modal retrieval across views, yielding results that are more aligned with what the user intends by the query. The multi-view approaches outlined in this paper can be employed for addressing problems in software engineering that can be encoded in terms of a learning problem, such as software bug prediction and feature location
Inzicht in keteninitiatieven: Ontwikkeling van een instrument waarmee de slaagkans van initiatieven in maatschappelijke ketens kan worden ingeschat en vergroot
No full textIn the public sector, large-scale ICT projects are implemented to “enhance a community’s attractiveness for citizens and businesses, as a means to increase the transparency of democratic processes or to increase the efficiency of the administration” (Rombach & Steffens, 2009, p. 1634). Various studies have shown that in many cases these high expectations are not met and that large-scale ICT projects cost more time and money than estimated at first: “the overall result is a massive wastage of financial, human and political resources, and an inability to deliver the potential gains from e-government to its beneficiaries” (Heeks, 2006, p. 3). One of the reasons is that current approaches make no distinction regarding the context in which ICT projects are implemented. Chain initiatives are large-scale (ICT) projects that are implemented in the context of a social chain. As this context has its own characteristics, the current general approaches to large-scale ICT projects are not adequate. I define a social chain as: a temporary, but structural, cooperation pattern of a large number of independent parties concerning a dominant chain problem, geared to producing a social product. The dominant chain problem is the operational problem that obstructs the chain parties when trying to achieve the chain challenge and which none of the chain parties can solve on its own (Grijpink, 2010). This dependence among the chain parties is one of the characteristics of social chains, which means that effective cooperation and decision-making do not come about automatically (Gray, 2010; Grijpink, 1997). In this research, I therefore developed an instrument for chain initiatives: an instrument that takes account of the characteristics of social chains and contains criteria for ‘doing the right things’ in that context. With this instrument professionals and researchers can estimate the probability of success of a chain initiative: before, during and after execution. The instrument also points to improvements for the chain initiative and/or the social chain. In this way, the instrument can contribute to producing more chain initiatives that are successful. The instrument has been assessed in a multi-case study. Given the nature of my research, it can only be concluded that the instrument seems valid as the case study has not yielded any results that suggest that the instrument is not valid. The results of my research provide insights into the success and failure of chain initiatives. The use of these results, and specifically the use of the instrument for promising chain initiatives, may contribute to more chain initiatives being successful
A Lazy Language Needs a Lazy Type System: Introducing Polymorphic Contexts
No full textMost type systems that support polymorphic functions are based on a version of System-F. We argue that this limits useful programming paradigms for languages with lazy evaluation. We motivate an extension of System-F alleviating this limitation. First, using a sequence of examples, we show that for lazily evaluated languages current type systems may force one to write a program in an unnatural way; in particular, we argue that in such languages the relationship between polymorphic and existential types can be made more systematic by allowing to pass back (part of) an existential result of a function call as an argument to the function call that produced that value. After presenting our extension to System-F we show how we can implement the strict-state thread monad ST by using a returned existential type to instantiating a polymorphic function that returns that type. Currently this monad is built-in into the runtime system of GHC and as such has become part of the language. Our proposed language extension, i.e. the introduction of polymorphic contexts, reverses the relationship between the context of a function call and the called function with respect to where it is decided with which type to instantiate a type variable
Towards the adoption of DevOps in software product organizations: A maturity model approach
No full textFull Text or Abstract?: Examining Topic Coherence Scores Using Latent Dirichlet Allocation
No full textThis paper assesses topic coherence and human topic ranking of uncovered latent topics from scientific publications when utilizing the topic model latent Dirichlet allocation (LDA) on abstract and full-text data. The coherence of a topic, used as a proxy for topic quality, is based on the distributional hypothesis that states that words with similar meaning tend to co-occur within a similar context. Although LDA has gained much attention from machine-learning researchers, most notably with its adaptations and extensions, little is known about the effects of different types of textual data on generated topics. Our research is the first to explore these practical effects and shows that document frequency, document word length, and vocabulary size have mixed practical effects on topic coherence and human topic ranking of LDA topics. We furthermore show that large document collections are less affected by incorrect or noise terms being part of the topic-word distributions, causing topics to be more coherent and ranked higher. Differences between abstract and full-text data are more apparent within small document collections, with differences as large as 90% high-quality topics for full-text data, compared to 50% high-quality topics for abstract data
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