INDIRECT: Intent-driven Requirements-to-Code Traceability
Author: Hey, Tobias
Conference: ICSE'19 DS: Doctoral Symposium of the 2019 IEEE/ACM 41th International Conference on Software Engineering, May 28th, 2019
Paper
Abstract: Traceability information is important for software maintenance, change impact analysis, software reusability, and other software engineering tasks. However, manually generating this information is costly. State-of-the-art automation approaches suffer from their imprecision and domain dependence. I propose INDIRECT, an intent-driven approach to automated requirements-to-code traceability. It combines natural language understanding and program analysis to generate intent models for both requirements and source code. Then INDIRECT learns a mapping between the two intent models. I expect that using the two intent models as base for the mapping poses a more precise and general approach. The intent models contain information such as the semantics of the statements, underlying concepts, and relations between them. The generation of the requirements intent model is divided into smaller subtasks by using an iterative natural language understanding. Likewise, the intent model for source code is built iteratively by identifying and understanding semantically related source code chunks.
@inproceedings{hey_indirect_2019,
title = {{{INDIRECT}}: {{Intent}}-{{Driven Requirements}}-to-{{Code Traceability}}},
shorttitle = {{{INDIRECT}}},
booktitle = {2019 {{IEEE}}/{{ACM}} 41st {{International Conference}} on {{Software Engineering}}: {{Companion Proceedings}} ({{ICSE}}-{{Companion}})},
author = {Hey, Tobias},
year = {2019},
month = may,
pages = {190--191},
doi = {10.1109/ICSE-Companion.2019.00078}
}