We investigate inconsistency problem in textual functional requirements specifications. We argue that some sources of inconsistency can be revealed during the very first steps of textual requirements analysis. In this paper, we focus on those facts and rules that domain experts find so obvious that they do not even mention them to the analysts during the discussions about the product to be constructed. However, what is very obvious for stakeholders may not be obvious for analysts. We call such rules default consistency rules. We argue that the lack of the default consistency rules leads to incompleteness in the requirements, and it causes incon- sistency with all its unpleasant consequences. In this contribution, we describe our approach to the problem of how the missing information can be both identified in the original requirements and found in external sources. We show a motivational example and explain our method.

Educational data can help us to personalise university information systems. In this paper, we show how educational data can be used to improve the performance of interaction-based recommender systems. Educational data is transformed to student profiles helping to prevent cold start problems when recommending projects to students with few user interactions. Our results show that our hybrid interaction based recommender boosted by educational profiles significantly outperforms bestseller recommendation, which is a mainstream recommendation method for cold start users.