Goal models have proven useful for capturing, understanding, and communicating requirements during early stages of software development. However, the utility of goal models is greatly enhanced when... Show More

Goal models have proven useful for capturing, understanding, and communicating requirements during early stages of software development. However, the utility of goal models is greatly enhanced when they can be exploited during downstream stages of the requirements analysis process (e.g. requirements elaboration, validation, planning), and can be used as part of the entire system life cycle (e.g., architectural and behavioral process design, coding, testing, monitoring, adaptation, and evolution). In order to better understand the progress that has been made in integrating goal models with downstream system development, the authors ask: what approaches exist that map/integrate/transform goal models to later stage software artifacts? To answer this question, they conduct a systematic survey, producing a roadmap of work summarizing 243 publications. Results include a categorization of the “why?” and “how?” for each approach. Furthermore, they select the 50 most prominent publications, based on citation numbers, in order to perform an in-depth literature review. Findings show that there is a wide variety of proposals with a variety of proposed goal models and targets, covering multiple paradigms, motivated by a variety of purposes. The authors conclude that although much work has been done in this area, the work is fragmented, following multiple separate strands of goal-orientation, and is often still in early stages of maturity.

Intentions play a key role in information systems engineering. Research on process modeling has highlighted that specifying intentions can expressly mitigate many problems encountered in process... Show More

Intentions play a key role in information systems engineering. Research on process modeling has highlighted that specifying intentions can expressly mitigate many problems encountered in process modeling as lack of flexibility or adaptation. Process mining approaches mine processes in terms of tasks and branching. To identify and formalize intentions from event logs, this work presents a novel approach of process mining, called Map Miner Method (MMM). This method automates the construction of intentional process models from event logs. First, MMM estimates users' strategies (i.e., the different ways to fulfill the intentions) in terms of their activities. These estimated strategies are then used to infer users' intentions at different levels of abstraction using two tailored algorithms. MMM constructs intentional process models with respect to the Map metamodel formalism. MMM is applied on a real-world dataset, i.e. event logs of developers of Eclipse UDC (Usage Data Collector). The resulting Map process model provides a precious understanding of the processes followed by the developers, and also provide feedback on the effectiveness and demonstrate scalability of MMM.

When using information retrieval systems, information related to searches is typically stored in files, which are well known as log files. By contrast, past search results of previously submitted... Show More

When using information retrieval systems, information related to searches is typically stored in files, which are well known as log files. By contrast, past search results of previously submitted queries are ignored most of the time. Nevertheless, past search results can be profitable for new searches. Some approaches in Information Retrieval exploit the previous searches in a customizable way for a single user. On the contrary, approaches that deal with past searches collectively are less common. This paper deals with such an approach, by using past results of similar past queries submitted by other users, to build the answers for new submitted queries. It proposes two Monte Carlo algorithms to build the result for a new query by selecting relevant documents associated to the most similar past query. Experiments were carried out to evaluate the effectiveness of the proposed algorithms using several dataset variants. These algorithms were also compared with the baseline approach based on the cosine measure, from which they reuse past results. Simulated datasets were designed for the experiments, following the Cranfield paradigm, well established in the Information Retrieval domain. The empirical results show the interest of our approach.