Logo des Repositoriums

Auflistung nach Autor:in "Hey, Tobias"

1 - 2 von 2
  • Zeitschriftenartikel
    Automated Traceability Link Recovery Between Requirements and Source Code
    (Softwaretechnik-Trends Band 44, Heft 2, 2024) Hey, Tobias
    Efficient development, maintenance, and management of software systems rely heavily on understanding the relationships between the various software artifacts. Manual creation and maintenance of traceability information between these artifacts incur high costs due to the required human expertise. Often this results in a deficiency in available traceability information that hampers the efficiency of the software projects. The FTLR approach presented in this dissertation aims to enhance automatic traceability link recovery between requirements and source code by leveraging a fine-grained semantic similarity comparison using pre trained word embeddings and Word Movers Distance. FTLR achieves significant improvements in traceabil ity connection identification by employing the fine grained mapping with subsequent majority vote-based aggregation. Additionally, it employs a novel approach for filtering irrelevant parts of requirements using a large language model-based classifier called NoRBERT, which achieves promising results on unseen projects. Furthermore, this dissertation explores the integration of bimodal large language models into FTLR but finds no significant performance increase over word embeddings. A comparative analysis showed that FTLR outperforms existing approaches in mean average precision and F1-scores, especially on projects with object-oriented source code. However, challenges, remain to fully automate traceability link recovery, particularly in large-scale projects.
  • ConferencePaper
    Programming in Natural Language with fuSE: Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding
    (Software Engineering 2021, 2021) Weigelt, Sebastian; Steurer, Vanessa; Hey, Tobias; Tichy, Walter
    With fuSE laypeople can create simple programs: one can teach intelligent systems new functions using plain English. fuSE uses deep learning to synthesize source code: it creates method signatures (for newly learned functions) and generates API calls (to form the body). In an evaluation on an unseen dataset fuSE synthesized 84.6% of the signatures and 66.9% of the API calls correctly.