by Anamaria-Roberta Preda, Christoph Mayr-Dorn, Atif Mashkoor, Wesley K. G. Assunção, Alexander Egyed
Abstract:
Efficient software maintenance and evolution rely heavily on effective software traceability, which is crucial for understanding the relationships between code elements and their corresponding requirements. However, ensuring the accuracy of trace links, whether manually or automatically, is a significant challenge due to the labor-intensive and error-prone nature of traceability tasks. The granularity issue in traceability compounds this challenge, as most existing research focuses on class-level traceability, while fine-grained dependencies (e.g., method-level traces) are more pertinent in daily development practices. Our primary aim is to facilitate the checking of requirement-to-method traces. To this end, we investigate an approach that utilizes the method's calling information and textual embeddings of requirement-to-method traces to identify inaccuracies in trace links. Our preliminary results are promising. By leveraging a Random Forest (RF) classifier, we have achieved notable improvements in both precision (≈10%) and recall (≈30%) compared to existing methods. This advancement highlights the potential of our method in enhancing the accuracy and efficiency of traceability processes in software development.
Reference:
Towards Leveraging Fine-Grained Dependencies to Check Requirements Traceability Correctness (Anamaria-Roberta Preda, Christoph Mayr-Dorn, Atif Mashkoor, Wesley K. G. Assunção, Alexander Egyed), ACM, 2024.
Bibtex Entry:
@InProceedings{Preda2024,
author = {Anamaria-Roberta Preda and Christoph Mayr-Dorn and Atif Mashkoor and Wesley K. G. Assunção and Alexander Egyed},
booktitle = {Proceedings of the 2024 {IEEE/ACM} 46th International Conference on Software Engineering: Companion Proceedings, {ICSE} Companion 2024, Lisbon, Portugal, April 14-20, 2024},
title = {Towards Leveraging Fine-Grained Dependencies to Check Requirements Traceability Correctness},
year = {2024},
pages = {292--293},
publisher = {{ACM}},
abstract = {Efficient software maintenance and evolution rely heavily on effective software traceability, which is crucial for understanding the relationships between code elements and their corresponding requirements. However, ensuring the accuracy of trace links, whether manually or automatically, is a significant challenge due to the labor-intensive and error-prone nature of traceability tasks. The granularity issue in traceability compounds this challenge, as most existing research focuses on class-level traceability, while fine-grained dependencies (e.g., method-level traces) are more pertinent in daily development practices.
Our primary aim is to facilitate the checking of requirement-to-method traces. To this end, we investigate an approach that utilizes the method's calling information and textual embeddings of requirement-to-method traces to identify inaccuracies in trace links. Our preliminary results are promising. By leveraging a Random Forest (RF) classifier, we have achieved notable improvements in both precision (≈10%) and recall (≈30%) compared to existing methods. This advancement highlights the potential of our method in enhancing the accuracy and efficiency of traceability processes in software development.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/icse/PredaMMAE24.bib},
doi = {10.1145/3639478.3643091},
timestamp = {Fri, 31 May 2024 21:05:16 +0200},
url = {https://doi.org/10.1145/3639478.3643091},
}