ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information

Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analysts and stakeholders. In this paper, we propose a requirements ELICitation Aid tool (ELICA) to help analysts better understand the target application domain by dynamic extraction and labeling of requirements-relevant knowledge. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural language processing tasks. In addition to the information conveyed through text, ELICA captures and processes non-linguistic information about the intention of speakers such as their confidence level, analytical tone, and emotions. The extracted information is made available to the analysts as a set of labeled snippets with highlighted relevant terms which can also be exported as an artifact of the Requirements Engineering (RE) process. The application and usefulness of ELICA are demonstrated through a case study. This study shows how pre-existing relevant information about the application domain and the information captured during an elicitation meeting, such as the conversation and stakeholders' intentions, can be captured and used to support analysts achieving their tasks.Comment: 2018 IEEE 26th International Requirements Engineering Conference Workshop

Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes

As systems grow in size and complexity so do their configuration possibilities. Users of modern systems are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. In this thesis, we propose a technique to select what information to elicit from the user so that the system can recommend the maximum number of personalized configuration items. Our method is based on constructing configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Processes (MDPs). Within the model, association rules are used to automatically infer configuration decisions based on knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. We conclude by reporting results of a case study in which this method is applied to the privacy configuration of Facebook

The Semantic Portal for Supporting Research Community: a Review

Current state of the art of typical search engines like Google, Yahoo and others are delivering references in terms of web URL or links to the related website. As such the results did not deliver the right answers required to the users needs. In addition to that as soon as the users require a collection of the information obtained, these search engines failed to do so resulting in the human intervention in-combining the information from several sources. Due ot the advancement and the vast number of sites and information on the web, demands in providing higher precision results are required to aid users in obtaining the most relevant result to the search process. One of the promising areas of the Semantic Web is enhancing the query capabilities for information. Small vertical vocabularies and ontologies have emerged, and the community of people using these and generating data is growing daily. However queries or search mechanisms that utilizea the vasrt amount of vocabularies, ontologies and data in digital libraries is still very much lacking. Therefore searching over heteregoneous records, data in digital library community or the Web has become a well known problem to the mass public. As such a solution is needed for a federated search across multiple resources available. However it remains unclear on how Semantic Web or its technology is used in constructing a digital library system or aid in enhancing the quality of the search results performed. This leads to the current work proposed, as work will be conducted to provide possible components that will construct the semantic web portal. The work performed is essential to facilitate semantic searches for research community in large-scale distributed digital library system. The subject research community is chosen particularly to aid in ensuring hat result obtained are accordingly to the users relevant needs. The expected outcomes of the research are an architecture that utilizes the semantic technology that will promote semantic web portal in the digital library and a semantic search mechanism that will provide better results and a combination of useful results relevant to the users

A Social Framework for Set Recommendation in Group Recommender Systems

This research article presents a study about the background in Group Recommender Systems and how social factors are directly related to these applications. Some important group recommender systems in academia are described to exemplify their contribution in different domains. Besides, a framework that is intended to improve group recommender systems is proposed. The main idea of the framework is to enhance social cognition to help the group members agree and make a decision. Its structure includes a process where an influential group is detected among the target groups of people to recommend to. Social influence detection uses the knowledge behind online social connections and interactions. Trying to understand human behavior and ties among groups in a social network and how to use this to improve group recommender systems is considered the main challenge for future research. Combining this with the kind of item recommendation which involves a temporal sequence of ordered elements will present a novel and original path in Group Recommender Systems design. &nbsp