Continuous Integration is a critical problem for software maintenance in global projects, compromising companies’ performance, which tends to accumulate a high-resolution time due to the approval process, conflict resolution, tests, and validations. The process of the validation involves the commit description interpretation and can be automated by NLP-mechanisms. This paper presents an intelligent NLP-based approach to evaluate whether the commits can be integrated into a certain software release based only on their descriptions. Our experiments showed an accuracy of 92.9%.

The increasing number of options of digital board games is exciting not only from an entertainment perspective but also from an academic prospect. It enables, for example, the application of modern computational techniques and algorithms for extracting and analyzing data from a variety of games - which range from classics like Chess and Go to modern board games with complex rules like Settlers of Catan and Terra Mystica. It is intuitive that different digital board games require distinct representation schemes and data structures to save, for example, the status or snapshot of a particular game at a specific moment. The choice for a representation model and data-structures is a crucial design decision that affects the selection of an algorithmic solution as well as the suitableness for artificial intelligence agents. This survey focuses on the different schemes and data structures used to represent game states, physical components, players, actions and rules for digital board games that have been reported in the academic literature. This study aims to lay the groundwork for the development of a game-independent computational framework which includes a generic game representation to facilitate and promote the application of computational techniques such as, for example, artificial intelligence and machine learning to this domain.

This demonstration presents an infrastructure for computing multilingual semantic relatedness and correlation for twelve natural languages by using three distributional semantic models (DSMs). Our demonsrator - DInfra (Distributional Infrastructure) provides researchers and developers with a highly useful platform for processing large-scale corpora and conducting experiments with distributional semantics. We integrate several multilingual DSMs in our webservice so end user can obtain a result without worrying about the complexities involved in building DSMs. Our webservice allows the users to have easy access to a wide range of comparisons of DSMs with different parameters. In addition, users can configure and access DSM parameters using a easy to use API.

Entering 'Football Players from United States' when searching for 'American Footballers' is an example of vocabulary mismatch, which occurs when different words are used to express the same concepts. In order to address this phenomenon for entity search targeting descriptors for complex categories, we propose a compositional-distributional semantics entity search engine, which extracts semantic and commonsense knowledge from large-scale corpora to address the vocabulary gap between query and data.