Abstract: Tools that focus on static code analysis for early error detection are of utmost importance in software development, especially since the propagation of errors is strongly related to higher costs in the development process. Formal Concept Analysis is a prominent field of applied mathematics that uses conceptual landscapes to discover and represent maximal clusters of data. Its expressive visualization method makes it suitable for exploratory analyses in different fields. In this paper we present a Formal Concept Analysis framework for static code analysis that can serve as a model for quantitative and qualitative exploration and interpretation of such results.

Abstract: Static code analysis is widely used to detect code quality issues before execution. Thus, it can provide information that is important for improving programming skills. Such tools have been successfully used in teaching programming courses. In this paper we present algorithms for combining Formal Concept Analysis (FCA) with Pylint, a static code analysis tool, in order to detect and assess behavioral patterns in students’ programming styles. We design a generalized framework that can be subsequently used to analyze any category/subset of errors. We apply the approach for detecting common errors related to design by considering two scenarios that tackle object oriented design and increased code complexity. We argue how the results can be used to understand common mistakes and to improve the teaching content and methods.

Abstract: Among the major advances in Artificial Intelligence we can mention Knowledge Discovery, Processing and Representation. Since in our modern society the healthcare system plays an important role and has a major impact in our daily lives, it lies at hand to apply the aforementioned methods in order to discover relevant patterns in healthcare databases and then to represent them in a way which supports reasoning, decision making, and communication. We approach this task by using two complementary directions, which are then interlinked. On the one hand we make use of the graphical representation capabilities of Formal Concept Analysis (FCA) and its powerful algorithms for conceptual knowledge discovery and processing. On the other, we use graph databases as a complementary visualization method of the extracted knowledge patterns. We exemplify this approach on a particular medical dataset, highlighting a 3D representation of conceptual hierarchies by using virtual reality (VR).