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Auflistung nach Autor:in "Cleland-Huang, Jane"

1 - 4 von 4
  • Konferenzbeitrag
    Automated Process-Centric Quality Constraints Checking for Quality Assurance in Safety-critical Systems
    (Software Engineering 2022, 2022) Mayr-Dorn, Christoph; Vierhauser, Michael; Bichler, Stefan; Keplinger, Felix; Cleland-Huang, Jane; Egyed, Alexander; Mehofer, Thomas
    This abstract summarizes the work published as an ICSE 2021 research track paper ''Supporting Quality Assurance with Automated Process-Centric Quality Constraints Checking'' available at https://doi.org/10.1109/ICSE43902.2021.00118 . We propose an approach that, on the one hand, assists in checking compliance with traceability requirements but, on the other hand, allows engineers to temporarily deviate from the prescribed software engineering process. Through the observation of developer activities in the form of changes to engineering artifacts in tools such as Jira or Jama, we build up a representation of the ongoing process progress. This tracking in the background does not force the software developer to work only on activities as defined in a process description. At the same time, it enables us to provide timely feedback to the developer on whether tasks fulfill all QA criteria. This approach lifts the burden off QA engineers in manually checking QA constraints, often a time-consuming, tedious, and error-prone task where feedback reaches developers usually very late. We evaluate our approach by applying it to two different case studies; one open source community system and a safety-critical system in the air-traffic control domain. Results from the analysis show that trace links are often corrected or completed after the fact and thus timely and automated constraint checking support has significant potential on reducing rework.
  • Konferenzbeitrag
    A Requirements-Driven Platform for Validating Field Operations of Small Uncrewed Aerial Vehicles
    (Software Engineering 2024 (SE 2024), 2024) Agrawal, Ankit; Zhang, Bohan; Shivalingaiah, Yashaswini; Vierhauser, Michael; Cleland-Huang, Jane
  • Konferenzbeitrag
    Traceability gap analysis for assessing the conformance of software traceabilityto relevant guidelines
    (Software-engineering and management 2015, 2015) Rempel, Patrick; Mäder, Patrick; Kuschke, Tobias; Cleland-Huang, Jane
    Many guidelines for safety-critical industries such as aeronautics, medical devices, and railway communications, specify that traceability must be used to demonstrate that a rigorous process has been followed and to provide evidence that the system is safe for use. In practice, there is a gap between what is prescribed by guidelines and what is implemented in practice, making it difficult for organizations and certifiers to fully evaluate the safety of the software system. We present an approach, which parses a guideline to extract a Traceability Model depicting software artifact types and their prescribed traces. It then analyzes the traceability data within a project to identify areas of traceability failure. Missing traceability paths, redundant and/or inconsistent data, and other problems are highlighted. We used our approach to evaluate the traceability of seven safety-critical software systems and found that none of the evaluated projects contained traceability that fully conformed to its relevant guidelines.
  • Konferenzbeitrag
    Traceability in the Wild: Automatically Augmenting Incomplete Trace Links
    (Software Engineering and Software Management 2019, 2019) Rath, Michael; Rendall, Jacob; Guo, Jin L.C.; Cleland-Huang, Jane; Mäder, Patrick
    This paper was published at the 40th International Conference on Software Engineering (ICSE) in May 2018. The authors propose a novel approach to establish trace links among software development artifacts. In particular, it allows to automatically link commits made in a projects’ version control systems (such as git) to respective issues in the projects’ issue tracker (e. g. Atlassian Jira). Besides augmenting an existing code base with additional trace links, the approach enables active recommendation of issues to the developer while performing a new commit to the version control system. This simplifies the overall development workflow. The proposed method is based on state-of-the-art machine learning techniques and serves as a basic building block in establishing project wide traceability. It’s feasibility, completeness, and usefulness was successfully evaluated through six empirical studies as well as one human study. The work was honored with an ACM SIGSOFT Distinguished Paper Award.