fischJan

UNHCR-Backend

Automated_CEG_Creation

This project aims at developing an algorithm for the automated transfer of structured requirements / pseudo code into Cause Effect Graphs (CEG). The algorithm comprises two functionalities: 1) syntax analysis of the pseudo code by means of ANTLR and 2) semantic analysis of the parse tree by a implemented visitor pattern.

CiRA

System behavior is often expressed by causal relations in requirements (e.g. if event 1 then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language (NL) is still an open research challenge as existing approaches fail to extract causality with reasonable performance.

fold

Deep learning with dynamic computation graphs in TensorFlow

specmate

Web-based Modeling and Test-Generation Tool

Fine-Grained-Causality-Extraction-From-NL-Requirements

fischJan/CiRA

System behavior is often expressed by causal relations in requirements (e.g. if event 1 then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language (NL) is still an open research challenge as existing approaches fail to extract causality with reasonable performance.

fischJan/Automated_CEG_Creation

This project aims at developing an algorithm for the automated transfer of structured requirements / pseudo code into Cause Effect Graphs (CEG). The algorithm comprises two functionalities: 1) syntax analysis of the pseudo code by means of ANTLR and 2) semantic analysis of the parse tree by a implemented visitor pattern.

fischJan/fold

Deep learning with dynamic computation graphs in TensorFlow