Software modelling languages: A wish list

© 2015 IEEE. Contemporary software engineering modelling tends to rely on general-purpose languages, such as the Unified Modeling Language. However, such languages are practice-based and seldom underpinned with a solid theory-be it mathematical, ontological or concomitant with language use. The future of software modelling deserves research to evaluate whether a language base that is compatible with these various elements as well as being philosophically coherent offers practical advantages to software developers

Scientific History of Incipit in the period 2010-2016

Historial de la actividad científica y técnica del Instituto de Ciencias del Patrimonio (Incipit) del CSIC, basado en Santiago de Compostela, desde su fecha de creación (2010) hasta el año 2016. Se presentan la misión y las líneas de investigación del Incipit, centradas principalmente en el estudio de los procesos de patrimonialización y de valorización social del patrimonio cultural realizadas con una perspectiva transdisciplinar. Se relacionan las publicaciones, proyectos de investigación, actividades de ciencia pública, eventos de comunicación y productos de divulgación que su personal investigador ha producido a lo largo de estos años.General introduction to the Incipit. Presentation of the Research Line: Cultural Heritage Studies: Sub-Theme: Landscape Archaeology and Cultural Landscapes, Sub-theme: Heritagization Processes: Memory, Power and Ethnicity, Sub-theme: Socioeconomics of Cultural Heritage, Sub-theme: Archaeology of the Contemporary Past, Sub-theme: Material culture and formalization processes of cultural heritage. Scientific Contributions. Transfer of Knowledge. International Activities. Other Activities and Results. Scientific DisseminationN

A Novel Approach for Process Mining : Intentional Process Models Discovery

International audienceSo far, process mining techniques have suggested to model processes in terms of tasks that occur during the enactment of a process. However, research on method engineering and guidance has illustrated that many issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. This paper presents a novel approach of process mining, called Map Miner Method (MMM). This method is designed to automate the construction of intentional process models from process logs. MMM uses Hidden Markov Models to model the relationship between users' activities logs and the strategies to fulfill their intentions. The method also includes two specific algorithms developed to infer users' intentions and construct intentional process model (Map) respectively. MMM can construct Map process models with different levels of abstraction (fine-grained and coarse-grained process models) with respect to the Map metamodel formalism (i.e., metamodel that specifies intentions and strategies of process actors). This paper presents all steps toward the construction of Map process models topology. The entire method is applied on a large-scale case study (Eclipse UDC) to mine the associated intentional process. The likelihood of the obtained process model shows a satisfying efficiency for the proposed method

Supervised Intentional Process Models Discovery using Hidden Markov Models

Best Paper AwardInternational audienceSince several decades, discovering process models is a subject of interest in the Information System (IS) community. Approaches have been proposed to recover process models, based on the recorded sequential tasks (traces) done by IS' actors. However, these approaches only focused on activities and the process models identified are, in consequence, activity-oriented. Intentional process models focuses on intentions rather than activities, in order to offer a better guidance through the processes, based on the reasoning behind the activities. Unfortunately, the existing process-mining approaches do not take into account the hidden aspect of intentions behind the recorded users' activities. We think that we can discover the intentional process models underlying user activities by using Intention mining techniques. The aim of this paper is to propose the use of probabilistic models to evaluate the most likely intentions behind traces of activities, namely Hidden Markov Models (HMMs). This paper focuses on a supervised approach that allows discovering the intentions behind the users' activities traces and to compare them to the prescribed intentional process model

Contextual Recommendations using Intention Mining on Process Traces

International audienceNowadays, digital traces are omnipresent in Information System (IS). Companies track IS interactions to retrieve and compile information about actors. Researchers of various streams, within IT and beyond, focused on recording actor interactions with systems and the technical possibilities to identify record and store these interactions. Tracing functionality has appeared in almost all common computer applications. This PhD project will focus on the establishment of a trace-based system and propose recommendations to actors regarding to their context. The objective of this thesis is to study process traces to propose recommendations to the actors by identifying a set of generic processes adaptable to the current actors' context. Thus, any actor, expert or novice, will be able to use this knowledge that gives contextual clues to identify the potential steps he could perform

The Role of Gamification in Privacy Protection and User Engagement

The interaction between users and several technologies has rapidly increased. In people’s daily habits, the use of several applications for different reasons has been introduced. The provision of attractive services is an important aspect that it should be considered during their design. The implementation of gamification supports this, while game elements create a more entertaining and appealing environment. At the same time, due to the collection and record of users’ information within them, security and privacy are needed to be considered as well, in order for these technologies to ensure a minimum level of security and protection of users’ information. Users, on the other hand, should be aware of their security and privacy, so as to recognize how they can be protected, while using gamified services. In this work, the relation between privacy and gamified applications, regarding both the software developers and the users, is discussed, leading to the necessity not only of designing privacy-friendly systems but also of educating users through gamification on privacy issues

DREQUS: an approach for the Discovery of REQuirements Using Scenarios

ABSTRACT: Requirements engineering is recognized as a complex cognitive problem-solving process that takes place in an unstructured and poorly-understood problem context. Requirements elicitation is the activity generally regarded as the most crucial step in the requirements engineering process. The term “elicitation” is preferred to “capture”, to avoid the suggestion that requirements are out there to be collected. Information gathered during requirements elicitation often has to be interpreted, analyzed, modeled, and validated before the requirements engineer can feel confident that a complete set of requirements of a system have been obtained. Requirements elicitation comprises the set of activities that enable discovering, understanding, and documenting the goals and motives for building a proposed software system. It also involves identifying the requirements that the resulting system must satisfy in to achieve these goals. The requirements to be elicited may range from modifications to well-understood problems and systems (i.e. software upgrades), to hazy understandings of new problems being automated, to relatively unconstrained requirements that are open to innovation (e.g. mass-market software). Requirements elicitation remains problematic; missing or mistaken requirements still delay projects and cause cost overruns. No firm definition has matured for requirements elicitation in comparison to other areas of requirements engineering. This research is aimed to improve the results of the requirements elicitation process directly impacting the quality of the software products derived from them

Understanding requirements dependency in requirements prioritization: a systematic literature review

Requirement prioritization (RP) is a crucial task in managing requirements as it determines the order of implementation and, thus, the delivery of a software system. Improper RP may cause software project failures due to over budget and schedule as well as a low-quality product. Several factors influence RP. One of which is requirements dependency. Handling inappropriate handling of requirements dependencies can lead to software development failures. If a requirement that serves as a prerequisite for other requirements is given low priority, it affects the overall project completion time. Despite its importance, little is known about requirements dependency in RP, particularly its impacts, types, and techniques. This study, therefore, aims to understand the phenomenon by analyzing the existing literature. It addresses three objectives, namely, to investigate the impacts of requirements dependency on RP, to identify different types of requirements dependency, and to discover the techniques used for requirements dependency problems in RP. To fulfill the objectives, this study adopts the Systematic Literature Review (SLR) method. Applying the SLR protocol, this study selected forty primary articles, which comprise 58% journal papers, 32% conference proceedings, and 10% book sections. The results of data synthesis indicate that requirements dependency has significant impacts on RP, and there are a number of requirements dependency types as well as techniques for addressing requirements dependency problems in RP. This research discovered various techniques employed, including the use of Graphs for RD visualization, Machine Learning for handling large-scale RP, decision making for multi-criteria handling, and optimization techniques utilizing evolutionary algorithms. The study also reveals that the existing techniques have encountered serious limitations in terms of scalability, time consumption, interdependencies of requirements, and limited types of requirement dependencies