Fine-grained Traceability Link Recovery (FTLR)

This repository contains the code and datasets used for the dissertation "Automatische Wiederherstellung von Nachverfolgbarkeit zwischen Anforderungen und Quelltext" (engl.: Automatic recovery of traceability between requirements and source code) by Tobias Hey

Docker Image

The provided docker image includes all files (code, datasets and models) needed to replicate the results. The image can be loaded by

docker image load < FTLR_docker_image.tar.gz

Via

docker run -it tobhey:FTLR bash 

you end in the working directory of FTLR and are able to run the tool either via CLI

python FTLR.py -h

a single script like

python App.py

or with one of the evaluation scripts in /evaluation/scripts.

Attribution (of datasets used):

The original SMOS and eAnci dataset can be attributed to Gethers et al., On integrating orthogonal information retrieval methods to improve traceability recovery. In 2011 27th IEEE International Conference on Software Maintenance (ICSM), Sep. 2011. Available: https://doi.org/10.1109/ICSM.2011.6080780

The original eTour dataset was provided for the TEFSE challenge at 6th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), 2011 and was retrieved from http://coest.org/

The iTrust dataset was retrieved from http://coest.org/

The LibEST dataset can be attributed to Moran et al., Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks. In 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE), May 2020 and was retrieved from https://gitlab.com/SEMERU-Code-Public/Data/icse20-comet-data-replication-package

The Albergate dataset can be attributed to Antoniol et al., Recovering traceability links between code and documentation. In IEEE Trans. on Software Eng., 28(10):970–983, 2002 and was retrieved from http://coest.org/