Understanding the Challenges of Deploying Live-Traceability Solutions
- URL: http://arxiv.org/abs/2306.10972v1
- Date: Mon, 19 Jun 2023 14:34:16 GMT
- Title: Understanding the Challenges of Deploying Live-Traceability Solutions
- Authors: Alberto D. Rodriguez, Katherine R. Dearstyne, Jane Cleland-Huang
- Abstract summary: SAFA.ai is a startup focusing on fine-tuning project-specific models that deliver automated traceability in a near real-time environment.
This paper describes the challenges that characterize commercializing software traceability and highlights possible future directions.
- Score: 45.235173351109374
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software traceability is the process of establishing and maintaining
relationships between artifacts in a software system. This process is crucial
to many engineering processes, particularly for safety critical projects;
however, it is labor-intensive and error-prone. Automated traceability has been
a long awaited tool for project managers of these systems, and due to the
semantic similarities between linked artifacts, NLP techniques, such as
transformer models, may be leveraged to accomplish this task. SAFA.ai is a
startup focusing on fine-tuning project-specific models that deliver automated
traceability in a near real-time environment. The following paper describes the
challenges that characterize commercializing software traceability and
highlights possible future directions.
Related papers
- Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - Natural Language Processing for Requirements Traceability [47.93107382627423]
Traceability plays a crucial role in requirements and software engineering, particularly for safety-critical systems.
Natural language processing (NLP) and related techniques have made considerable progress in the past decade.
arXiv Detail & Related papers (2024-05-17T15:17:00Z) - Automated User Story Generation with Test Case Specification Using Large Language Model [0.0]
We developed a tool "GeneUS" to automatically create user stories from requirements documents.
The output is provided in format leaving the possibilities open for downstream integration to the popular project management tools.
arXiv Detail & Related papers (2024-04-02T01:45:57Z) - Automating SBOM Generation with Zero-Shot Semantic Similarity [2.169562514302842]
A Software-Bill-of-Materials (SBOM) is a comprehensive inventory detailing a software application's components and dependencies.
We propose an automated method for generating SBOMs to prevent disastrous supply-chain attacks.
Our test results are compelling, demonstrating the model's strong performance in the zero-shot classification task.
arXiv Detail & Related papers (2024-02-03T18:14:13Z) - Defining and executing temporal constraints for evaluating engineering
artifact compliance [56.08728135126139]
Process compliance focuses on ensuring that the actual engineering work is followed as closely as possible to the described engineering processes.
Checking these process constraints is still a daunting task that requires a lot of manual work and delivers feedback to engineers only late in the process.
We present an automated constraint checking approach that can incrementally check temporal constraints across inter-related engineering artifacts upon every artifact change.
arXiv Detail & Related papers (2023-12-20T13:26:31Z) - 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) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - Technology Readiness Levels for AI & ML [79.22051549519989]
Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results.
We propose a proven systems engineering approach for machine learning development and deployment.
arXiv Detail & Related papers (2020-06-21T17:14:34Z)
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.