Context: Because functional changes are inevitable throughout the software life-cycle, it is essential to assess and evaluate whether a Functional Change (FC) can be handled within the estimated project budget and time. Thus, a well-defined change measure and control process is vital for the success of the development project. In fact, the size measurement is widely used for effort and cost estimation. This paper explores the COSMIC Functional Size Measurement (FSM) method to evaluate a FC.

Objective: The objective of this paper is to assess a FC in terms of COSMIC Function Point units in order to identify changes incurring potential impact on the software development progress. Thus, we suppose that the functional size of a FC is one of the factors that must be taken into consideration to analyze the impact of changes on the development progress. This helps managers in making decisions to answer a FC request.

Method: The method proposed in this paper analyzes the FC impact in use case diagrams, the de-facto standard for modeling the Functional User Requirements (FUR). It distinguishes between internal and external FC to a use case. It proposes to quantify the FC impact on the functional size of a use case diagram in terms of COSMIC Function Point (CFP) units. In addition, it evaluates the status of a FC according to its functional size. Furthermore, we propose an algorithm for prioritizing changes in the case where more than one functional change is proposed. The algorithm is based on a set of heuristics where the objective is to minimize the effort required to answer the changes. It accounts for the most important factors when prioritizing changes (the functional size of the FC, and the preference of the change requester). These two factors are identified based on an investigation with the international COSMIC community.

Originality: Several techniques have been used to identify and analyze the impact of a FC during various phases of the software life-cycle. For example, some researches used the colored commit graphs (at the implementation phase), other studies used the consistency rules between UML diagrams (at the design phase), etc. Compared to other approaches focused on impact change analysis, our proposed approach uses the COSMIC-FSM method. Quantifying the functional size of a FC in terms of CFP units allows us to evaluate more realistically the impact of a FC on the software development progress.

Results: We propose six categories of FC status according to their sizes. Based on the FC status, it is possible to identify the FC impact on the software development progress. The FC status will help not only designers/developers in analyzing the impact of FC on the size of their work products but also managers to decide to accept or forgo a FC and maintain the development schedule. Moreover, the experiment conducted with 30 computer engineering students showed that students' ratings can be approximately evaluated when measuring FUR in terms of CFP units based on the FC status.

Conclusion: Based on the finding of this study, it is now feasible to identify FC leading to potential impact on the software development progress. More specifically, evaluating FC in the use case diagrams will help in making quick decisions about accepting or rejecting a FC. Regardless of the used software life-cycle process, use cases are popular for specifying the software functionality. In practice, our proposed approach is applicable not only in the software development industry but also in academia.

Ontologies provide shared and reusable pieces of knowledge about a specific domain. Building an ontology by hand is a very hard and prone to er-rors task. Ontology learning from existing resources provides a good solution to this issue. Databases are widely used to store data. They were often considered as the most reliable sources for knowledge extraction. NOSQL databases are more and more used to store data. MongoDB database is emerging as the fastest growing NOSQL database in the world. It belongs to the document oriented da-tabases variant. This paper proposes an approach to learn OWL ontology from data in MongoDB database and describes a tool implementing transformation rules.

Ontologies are the main component of the semantic Web. Constructing large ontologies typically requires to reuse previously existing ones. Modularity is a key requirement for collaborative ontology engineering and for distributed ontology reuse on the Web. In this paper, we propose an approach to automatically compose ontology modules into a global ontology. Our approach is based on similarity measures computed against concept names, attributes and relationships. We also detail the algorithm allowing to merge the ontology modules into a global ontology.

With the immense growth of online social applications, trust plays a more and more important role in connecting users to each other, sharing their personal information and attracting him to receive recommendations. Therefore, how to obtain trust relationships through mining online social networks became a critical issue. To calculate the level of trust between two users, many computational trust models are proposed which mainly rely on the social network structure, the explicit trust from user to another, the users’ behaviors, or the users’ similarity, etc. However, the majority of these models ignored the temporal factor. In this paper, we propose a trust relationship detection mechanism from an egocentric social network in order to compute the trust level between an active user and his directed friends. We propose a Level of social Trust model, that we called LoTrust, which is suitable for personalized recommendation purpose. This computational model founded on novel trust metric which is based not only on the users’ interests similarity according to their semantic social profiles (RDF/FOAF), but also takes into account the time factor of the users’ active interactions (e.g comments, share photo, wall posts, messages). We perform experiments on real life dataset extracted from Facebook. The experimental results demonstrated how our LoTrust model produces satisfactory results than other computational models.