Taxonomic Trace Links: Rethinking Traceability and its Benefits

• URL: http://arxiv.org/abs/2504.20507v1
• Date: Tue, 29 Apr 2025 07:47:38 GMT
• Title: Taxonomic Trace Links: Rethinking Traceability and its Benefits
• Authors: Waleed Abdeen, Michael Unterkalmsteiner, Alexandros Chirtoglou, Christoph Paul Schimanski, Heja Goli, Krzysztof Wnuk,
• Abstract summary: Traceability greatly supports knowledge-intensive tasks, e.g., coverage check and impact analysis.<n>Despite its clear benefits, the emphpractical implementation of traceability poses significant challenges.<n>We propose a new approach -- Taxonomic Trace Links (TTL) -- which rethinks traceability and its benefits.
• Score: 40.588683959176116
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traceability greatly supports knowledge-intensive tasks, e.g., coverage check and impact analysis. Despite its clear benefits, the \emph{practical} implementation of traceability poses significant challenges, leading to a reduced focus on the creation and maintenance of trace links. We propose a new approach -- Taxonomic Trace Links (TTL) -- which rethinks traceability and its benefits. With TTL, trace links are created indirectly through a domain-specific taxonomy, a simplified version of a domain model. TTL has the potential to address key traceability challenges, such as the granularity of trace links, the lack of a common data structure among software development artifacts, and unclear responsibility for traceability. We explain how TTL addresses these challenges and perform an initial validation with practitioners. We identified six challenges associated with TTL implementation that need to be addressed. Finally, we propose a research roadmap to further develop and evaluate the technical solution of TTL. TTL appears to be particularly feasible in practice where a domain taxonomy is already established

Related papers

• Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks [3.831690197072918]
  Signal Temporal Logic (STL) is a powerful specification language for describing complex temporal behaviors of continuous signals. generating executable plans for STL tasks is challenging, as it requires consideration of the coupling between the task specification and the system dynamics. We propose a new planning framework that uses only task-agnostic data during the offline training stage, enabling zero-shot generalization to new STL tasks.
  arXiv  Detail & Related papers  (2025-01-23T08:15:52Z)
• Large Language Models as Realistic Microservice Trace Generators [54.85489678342595]
  Workload traces are essential to understand complex computer systems' behavior and manage processing and memory resources.<n>This paper proposes a first-of-a-kind approach that relies on training a large language model to generate synthetic workload traces.<n>Our model adapts to downstream trace-related tasks, such as predicting key trace features and infilling missing data.
  arXiv  Detail & Related papers  (2024-12-16T12:48:04Z)
• Constrained LTL Specification Learning from Examples [8.544277223210894]
  We propose a new type of learning problem called constrained learning.<n>In addition to positive and negative examples, the user can specify one or more constraints over the properties of the formula.<n>An experimental evaluation demonstrates that ATLAS is able to solve new types of learning problems while performing better than or competitively with the state-of-the-art tools in learning.
  arXiv  Detail & Related papers  (2024-12-03T23:15:27Z)
• CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoning [0.0]
  CoT-TL is a data-efficient in-context learning framework for translating natural language specifications into representations. CoT-TL achieves state-of-the-art accuracy across three diverse datasets in low-data scenarios.
  arXiv  Detail & Related papers  (2024-10-21T17:10:43Z)
• Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers [55.46413719810273]
  rich-temporal information is crucial to the complicated target appearance in visual tracking. Our method improves the tracker's performance on six popular tracking benchmarks.
  arXiv  Detail & Related papers  (2024-03-15T02:39:26Z)
• Towards Unified Token Learning for Vision-Language Tracking [65.96561538356315]
  We present a vision-language (VL) tracking pipeline, termed textbfMMTrack, which casts VL tracking as a token generation task. Our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target.
  arXiv  Detail & Related papers  (2023-08-27T13:17:34Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT)<n>We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)
• Requirements Traceability: Recovering and Visualizing Traceability Links Between Requirements and Source Code of Object-oriented Software Systems [0.0]
  Requirement-to-Code Traceability Links (RtC-TLs) shape the relations between requirement and source code artifacts. This paper introduces YamenTrace, an automatic approach and implementation to recover and visualize RtC-TLs.
  arXiv  Detail & Related papers  (2023-07-09T11:01:16Z)
• Object Tracking through Residual and Dense LSTMs [67.98948222599849]
  Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative. DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
  arXiv  Detail & Related papers  (2020-06-22T08:20:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.