In many regulated domains, traceability is established across diverse artifacts such as requirements, design, code, test cases, and hazards -- either manually or with the help of supporting tools, and the resulting trace links are used to support activities such as impact analysis, compliance verification, and safety inspections. Automated tracing techniques need to leverage the semantics of underlying artifacts in order to establish more accurate trace links and to provide explanations of links that have been created in either a manual or automated fashion. To support this, we propose an automated technique which leverages source code, project artifacts and an external domain corpus to generate a domain-specific concept model. We then use the generated concept model to improve traceability results and to provide explanations of the results. Our approach overcomes existing problems with deep-learning traceability algorithms, as it does not require a training set of existing trace links. Finally, as an initial proof-of-concept, we apply our semantically-guided approach to the Dronology project, and show that it improves over other tracing techniques that do not use a concept model.
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SAFA: A Tool for Supporting Safety Analysis in Evolving Software Systems
Many organizations seek to increase their agility in order to deliver more timely and competitive products. However, in safety-critical systems such as medical devices, autonomous vehicles, or factory floor robots, the release of new features has the potential to introduce hazards that potentially lead to run-time failures that impact software safety. As a result, many projects suffer from a phenomenon referred to as the big freeze. SAFA is designed to address this challenge. Through the use of cutting-edge deep-learning solutions, it generates trees of requirements, designs, code, tests, and other artifacts that visually depict how hazards are mitigated in the system, and it automatically warns the user when key artifacts are missing. It also uses a combination of colors, annotations, and recommendations to dynamically visualize change across software versions and augments safety cases with visual annotations to aid users in detecting and analyzing potentially adverse impacts of change upon system safety. A link to our tool demo can be found at https://www.youtube.com/watch?v=r-CwxerbSVA.
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- NSF-PAR ID:
- 10427983
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 37th {IEEE/ACM} International Conference on Automated Software Engineering, {ASE} 2022, Rochester, MI, USA, October 10-14, 2022
- Volume:
- 2022
- Page Range / eLocation ID:
- 165:1--165:4
- Format(s):
- Medium: X
- Location:
- Detroit, USA
- Sponsoring Org:
- National Science Foundation
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