Background: Building Information Modelling (BIM) is becoming a standard in Architecture, Building and Construction (ABC) industries. It provides a method for a unified representation of information about building throughout its lifecycle – from the design phase to the building maintenance. At the design phase the focus is usually on fulfilling the requirements and applicable norms and standards. One of the issues that is considered to be very important deals with the accessibility of buildings. While the design tools usually support the legal requirements for accessibility for disabled persons, little effort has been observed to address the quality of the access routes in terms of time, length and convenience of the route to be taken. Methods: This paper introduces a method of searching routes accessible for disabled people in public buildings, which is based on the building-related knowledge. This knowledge is stored in graph-based representations of buildings. A graph model of the considered building is effectively created by extracting information about the building from the IFC file, which is an interoperable BIM standard. The information about the topology of spatial layouts of buildings is stored in the graph representation together with characteristics of elements ensuring access between spaces. This model allows for homogenous encoding of both spatial and non-spatial information of different types, which is necessary for route calculation. Attributes assigned to graph nodes encode types of spaces and their sizes. Attributes of graph edges represent costs of moving between spaces, which depend on width of openings, equipping stairs with ramps, door types and their opening directions. As for people moving around on wheelchairs easiness of passing a route between two places can depend on the route direction, the directed graphs are used to represent building layouts. The optimal routes accessible for disabled are specified using the modified algorithm of graph search for single-source minimum-cost paths, where costs of passing through different spaces and between them are considered. This approach is applied to find best routes to a chosen room starting from all possible building entrances. Results: The proposed application offers intelligent knowledge-based navigation, which is based on the graph representation of buildings. The system is aimed to aid people to decide about the routes they should take using mobile devices. It enables them to select from all accessible routes the one which requires the lowest effort in traversing it. Apart from the user interface allowing for visualization the results of design analysis in respect to the accessible routes, a visual tool dedicated for designers, which shows on the floor layout the actual route a disabled person has to take, is proposed. Offering a designer such a visualization can shift the focus of the design from mere existence of accessible routes to existence of quality routes for disabled. The presented examples illustrate the proposed approach by depicting the found accessible routes on the building layout. Conclusions: Building-specific knowledge, which is extracted from the IFC file and stored in the graph building representation, is used to find the best routes accessible for disabled people. The search for these routes is performed by a knowledge-based graph-search procedure, which takes into consideration costs of passing through different spaces and between them. There is a possibility of adding in future more elements which can be treated as different obstacles in moving around the building.

This paper deals with an approach based on the similarity of mutants. This similarity is used to reduce the number of mutants to be executed. In order to calculate such a similarity among mutants their structure is used. Each mutant is converted into a hierarchical graph, which represents the program’s flow, variables and conditions. On the basis of this graph form a special graph kernel is defined to calculate similarity among programs. It is then used to predict whether a given test would detect a mutant or not. The prediction is carried out with the help of a classification algorithm. This approach should help to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented in this paper. An example of a program used in experiments is described and the results obtained, especially classification errors, are presented.