http://sedici.unlp.edu.ar:80/handle/10915/134650 2026-06-09T12:07:46Z 2026-06-09T12:07:46Z Díaz Pace, J. Andrés Colla, Pedro E. http://sedici.unlp.edu.ar:80/handle/10915/135270 2022-04-29T04:02:47Z 2014-06-04T00:00:00Z

Contribucion a revista Electronic Journal of SADIO; vol. 13 Editorial al volumen 13 del Electronic Journal of SADIO.

2014-06-04T00:00:00Z Editorial al volumen 13 del Electronic Journal of SADIO. Rago, Alejandro Frade, Paula Ruiva, Miguel Marcos, Claudia A. http://sedici.unlp.edu.ar:80/handle/10915/135235 2022-04-29T04:02:49Z 2014-06-04T00:00:00Z

Articulo Electronic Journal of SADIO; vol. 13 Carrying out requirements capture and modeling activities successfully is not easy, often requiring a thoughtful analysis of clients needs and demanding an adequate expertise from analysts. To ensure a fluid communication among stakeholders, analysts must take advantage of modeling techniques while describing requirements and exploit reuse and abstraction practices so as to avoid redundancy (for instance, using relations between use cases). Unfortunately, these practices are seldom applied because inspecting requirements such as textual use cases by hand, looking out for faulty or duplicate functionalities, is a challenging and error-prone activity. In this context, we introduce an assistive approach called ReUse that searches redundancy eficiencies in use case specifications and allows to fix them with relation-based refactorings. Our approach makes use of text processing and sequence alignment techniques to discover deficiencies (e.g., duplicate functionality). We have evaluated ReUse in five case studies, achieving promising results.

2014-06-04T00:00:00Z Carrying out requirements capture and modeling activities successfully is not easy, often requiring a thoughtful analysis of clients needs and demanding an adequate expertise from analysts. To ensure a fluid communication among stakeholders, analysts must take advantage of modeling techniques while describing requirements and exploit reuse and abstraction practices so as to avoid redundancy (for instance, using relations between use cases). Unfortunately, these practices are seldom applied because inspecting requirements such as textual use cases by hand, looking out for faulty or duplicate functionalities, is a challenging and error-prone activity. In this context, we introduce an assistive approach called ReUse that searches redundancy eficiencies in use case specifications and allows to fix them with relation-based refactorings. Our approach makes use of text processing and sequence alignment techniques to discover deficiencies (e.g., duplicate functionality). We have evaluated ReUse in five case studies, achieving promising results. Ayub, María Celeste Cian, Ayelén Caliusco, María Laura Reynares, Emiliano http://sedici.unlp.edu.ar:80/handle/10915/135233 2022-04-29T04:02:51Z 2014-06-04T00:00:00Z

Articulo XIV Simposio Argentino de Ingeniería de Software (ASSE) - JAIIO 42 (2013); Electronic Journal of SADIO; vol. 13; http://sedici.unlp.edu.ar/handle/10915/76373 Recently, Team Recommender Systems (TRS) have become ex-tremely common because they are software tools and techniques that helps to organizations to composite team needed to carry out a task requiring multiple skills. TRS have two important problems: (1) managing semantic heterogeneity that occurs when the data describing the same entities related to the real world is represented in different ways, and (2) specialization excess leading to display the objects of highest similarity with the user specified instead of a wide range of options leaving out of consideration the highest possible user interest infor-mation. In recent years, the ontology-based information systems have gained the attention of the researchers and practitioners since they handle the semantic heterogeneity problem. Despite of the advance done, building methodologies for developing ontology-based systems is still a research area. In this paper, we report our experience in developing an ontology-based TRS by using the EDON method. The developed TRS analyses human resource information to recom-mend a work team for a software development project.

2014-06-04T00:00:00Z Recently, Team Recommender Systems (TRS) have become ex-tremely common because they are software tools and techniques that helps to organizations to composite team needed to carry out a task requiring multiple skills. TRS have two important problems: (1) managing semantic heterogeneity that occurs when the data describing the same entities related to the real world is represented in different ways, and (2) specialization excess leading to display the objects of highest similarity with the user specified instead of a wide range of options leaving out of consideration the highest possible user interest infor-mation. In recent years, the ontology-based information systems have gained the attention of the researchers and practitioners since they handle the semantic heterogeneity problem. Despite of the advance done, building methodologies for developing ontology-based systems is still a research area. In this paper, we report our experience in developing an ontology-based TRS by using the EDON method. The developed TRS analyses human resource information to recom-mend a work team for a software development project. Martínez, Cristian Díaz, Nicolás Gonnet, Silvio M. Leone, Horacio P. http://sedici.unlp.edu.ar:80/handle/10915/135225 2022-04-29T04:02:52Z 2014-06-04T00:00:00Z

Articulo Electronic Journal of SADIO; vol. 13 Variability is defined as the possibility that a system has to be extended, changed, localized or configured in order to be used in a particular context. Variability specification in a software product line (SPL) is a main activity where product families are specified in terms of variants and dependencies. One way of defining the variability of a SPL is through a feature model (FM). However, the product families obtained can present feasibility problems, for instance, inclusion rules that can result contradictory which is translated in a set of features impossible to be incorporated into any product. Such inconveniences may come from the initial feature model developed as well from modifications introduced to satisfy new demands. In this paper a tool based on Petri nets is proposed in order to represent and analyze FMs as well as detecting the problems mentioned before.

2014-06-04T00:00:00Z Variability is defined as the possibility that a system has to be extended, changed, localized or configured in order to be used in a particular context. Variability specification in a software product line (SPL) is a main activity where product families are specified in terms of variants and dependencies. One way of defining the variability of a SPL is through a feature model (FM). However, the product families obtained can present feasibility problems, for instance, inclusion rules that can result contradictory which is translated in a set of features impossible to be incorporated into any product. Such inconveniences may come from the initial feature model developed as well from modifications introduced to satisfy new demands. In this paper a tool based on Petri nets is proposed in order to represent and analyze FMs as well as detecting the problems mentioned before. Diván, Mario José Olsina, Luis http://sedici.unlp.edu.ar:80/handle/10915/135219 2022-04-29T04:02:54Z 2014-06-04T00:00:00Z

Articulo Electronic Journal of SADIO; vol. 13 The data stream processing strategy based on measurement metadata, is an enhanced approach that relies on a measurement and evaluation conceptual framework. The strategy uses measures (data sets) and their linked metadata to incorporate detective and predictive behavior as well as to trigger alarms in a proactive way. This work discusses the process formalization for its data stream processing components, and their interactions. The process specification is based on the SPEM language, and the main specified activities ranges from data source configuration of heterogeneous data sources, to statistical analysis and classifiers for decision making. As result, the proposed strategy becomes more consistent, repeatable and communicable from the process modeling viewpoint. Also, to illustrate the approach, excerpts of a developed outpatient monitoring system are used.

2014-06-04T00:00:00Z The data stream processing strategy based on measurement metadata, is an enhanced approach that relies on a measurement and evaluation conceptual framework. The strategy uses measures (data sets) and their linked metadata to incorporate detective and predictive behavior as well as to trigger alarms in a proactive way. This work discusses the process formalization for its data stream processing components, and their interactions. The process specification is based on the SPEM language, and the main specified activities ranges from data source configuration of heterogeneous data sources, to statistical analysis and classifiers for decision making. As result, the proposed strategy becomes more consistent, repeatable and communicable from the process modeling viewpoint. Also, to illustrate the approach, excerpts of a developed outpatient monitoring system are used.