Lidia López

Making explicit some implicit i * language decisions

The i* (i-star) framework is one of the most widely adopted modelling approaches by several communities (business modelling, requirements engineering, ...). Probably due to its highly strategic nature, the definition of the modelling language offered by the framework does not make explicit the full behaviour of some basic constructs, leaving them thus open to several interpretations. This looseness may not be important in some contexts, even it may be beneficial since it leaves room for researchers to customize the framework to their needs. However, it becomes an obstacle in other situations, e.g., model interoperability and model-driven development. In this paper we identify ambiguities and silences in the i* language definition in a systematic manner, and then we propose an interpretation to deal with them. In some cases, the proposal may include the addition of some annotation into some language construct. The result is a formal definition taking the form of a UML conceptual data diagram (a metamodel) with several important integrity constraints.

Goal-Driven Adaptation of Service-Based Systems from Runtime Monitoring Data

Service-based systems need to provide flexibility to adapt both to evolving requirements from multiple, often conflicting, ephemeral and unknown stakeholders, as well as to changes in the runtime behavior of their component services. Goal-oriented models allow representing the requirements of the system whilst keeping information about alternatives. We present the MAESoS approach which uses i* diagrams to identify quality of service requirements over services. The alternatives are extracted and kept in a variability model. A monitoring infrastructure identifies changes in runtime behavior that can propagate up to the level of stakeholder goals and trigger the required adaptations. We illustrate the approach with a scenario of use.

Adoption of OSS components

Open Source Software (OSS) has become a strategic asset for a number of reasons, such as short time-to-market software delivery, reduced development and maintenance costs, and its customization capabilities. Therefore, organizations are increasingly becoming OSS adopters, either as a result of a strategic decision or because it is almost unavoidable nowadays, given the fact that most commercial software also relies at some extent in OSS infrastructure. The way in which organizations adopt OSS affects and shapes their businesses. Therefore, knowing the impact of different OSS adoption strategies in the context of an organization may help improving the processes undertaken inside this organization and ultimately pave the road to strategic moves. In this paper, we propose to model OSS adoption strategies using a goal-oriented notation, in which different actors state their objectives and dependencies on each other. These models describe the consequences of adopting one such strategy or another: which are the strategic and operational goals that are supported, which are the resources that emerge, etc. The models rely on an OSS ontology, built upon a systematic literature review, which comprises the activities and resources that characterize these strategies. Different OSS adoption strategy models arrange these ontology elements in diverse ways. In order to assess which is the OSS adoption strategy that better fits the organization needs, the notion of model coverage is introduced, which allows to measure the degree of concordance among every strategy with the model of the organization by comparing the respective models. The approach is illustrated with an example of application in a big telecommunications company.

HiME: Hierarchical i* Modeling Editor

In this paper, we present HiME, a tool for editing i* models. The distinguishing characteristic of HiME is its ability to deal with inheritance. It includes specific operations for declaring an actor as heir of another and then stating the relationships between the intentional elements of both actors.

Specialization in i * strategic rationale diagrams

The specialization relationship is offered by the i* modeling language through the is-a construct defined over actors (a subactor is-a superactor). Although the overall meaning of this construct is highly intuitive, its semantics when it comes to the fine-grained level of strategic rationale (SR) diagrams is not defined, hampering seriously its appropriate use. In this paper we provide a formal definition of the specialization relationship at the level of i* SR diagrams. We root our proposal over existing work in conceptual modeling in general, and object-orientation in particular. Also, we use the results of a survey conducted in the i* community that provides some hints about what i* modelers expect from specialization. As a consequence of this twofold analysis, we identify, define and specify two specialization operations, extension and refinement, that can be applied over SR diagrams. Correctness conditions for them are also clearly stated. The result of our work is a formal proposal of specialization for i* that allows its use in a well-defined manner.

OSSAP: A situational method for defining open source software adoption processes

Organizations are increasingly becoming Open Source Software (OSS) adopters, either as a result of a strategic decision or just as a consequence of technological choices. The strategy followed to adopt OSS shapes organizations’ businesses; therefore methods to assess such impact are needed. In this paper, we propose OSSAP, a method for defining OSS Adoption business Processes, built using a Situational Method Engineering (SME) approach. We use SME to combine two well-known modelling methods, namely goal-oriented models (using i*) and business process models (using BPMN), with a pre-existing catalogue of goal-oriented OSS adoption strategy models. First, we define a repository of reusable method chunks, including the guidelines to apply them. Then, we define OSSAP as a composition of those method chunks to help organizations to improve their business processes in order to integrate the best fitting OSS adoption strategy. We illustrate it with an example of application in a telecommunications company.

The RISCOSS Platform for Risk Management in Open Source Software Adoption

Managing risks related to OSS adoption is a must for organizations that need to smoothly integrate OSS-related practices in their development processes. Adequate tool support may pave the road to effective risk management and ensure the sustainability of such activity. In this paper, we present the RISCOSS platform for managing risks in OSS adoption. RISCOSS builds upon a highly configurable data model that allows customization to several types of scopes. It implements two different working modes: exploration, where the impact of decisions may be assessed before making them; and continuous assessment, where risk variables (and their possible consequences on business goals) are continuously monitored and reported to decision-makers. The blackboard-oriented architecture of the platform defines several interfaces for the identified techniques, allowing new techniques to be plugged in.

Data-Driven Requirements Engineering in Agile Projects: The Q-Rapids Approach

Requirements identification, specification and management are key activities in the software development process. In the last years, many approaches to these activities have emerged, based on the exploitation of huge amounts of data gathered from software repositories and system usage. The Q-Rapids project proposes the collection and analysis of such data and its consolidation into a set of strategic indicators as product quality, time to market and team productivity. These indicators are visualized through a dashboard designed to support decision-makers. In this paper, we present the ongoing research undertaken in this project. We use the concept of blocking situation to exemplify the Q-Rapids approach.

Towards an OSS Adoption Business Impact Assessment

Nowadays, the adoption of Open Source Software (OSS) by organizations is becoming a strategic need in a wide variety of application areas. Organizations adopt OSS in very diverse ways. The way in which they adopt OSS affects and shapes their businesses. Therefore, knowing the impact of different OSS adoption strategies in the context of an organization may help improving the processes undertaken inside this organization and ultimately pave the road to strategic moves. However, there is a lack of support for assessing the impact of the OSS adoption over the business of the adopter organizations. Based on the goal-oriented characterization of some OSS adoption strategies, in this paper, we propose a preliminary approach to assess the business impact of the OSS adoption strategies over the adopter organizations. The proposal is based on the Business Model Canvas and graph theory notions to support the elicitation and assessment of the impact of each goal over the adopter organization. We illustrate the application of the approach in the context of a telecommunications company.

Modelling and applying OSS adoption strategies

Increasing adoption of Open Source Software (OSS) in information system engineering has led to the emergence of different OSS business strategies that affect and shape organizations’ business models. In this context, organizational modeling needs to reconcile efficiently OSS adoption strategies with business strategies and models. In this paper, we propose to embed all the knowledge about each OSS adoption strategy into an i* model that can be used in the intentional modeling of the organization. These models describe the consequences of adopting one such strategy or another: which are the business goals that are supported, which are the resources that emerge, etc. To this aim, we first enumerate the main existing OSS adoption strategies, next we formulate an ontology that comprises the activities and resources that characterise these strategies, then based on the experience of 5 industrial partners of the RISCOSS EU-funded project, we explore how these elements are managed in each strategy and formulate the corresponding model using the i* framework.