EDBT 2026 Demo / reviewers in the wild / expert
Seyedehzahra Khoshmanesh
dblp:230/2364
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2019
0000-0002-7449-546XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 2 first-authorSoftware engineering, systems software and programming languages · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution
traceability |
0.4 | 1 | 2019 | Leveraging artifact trees to evolve and reuse safety cases · ICSE 2019 |
Methods — techniques the papers use, named apart from their topics
traceability analysis · 0.4design science · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Leveraging artifact trees to evolve and reuse safety casesabstractSafety Assurance Cases (SACs) are increasingly used to guide and evaluate the safety of software-intensive systems. They are used to construct a hierarchically organized set of claims, arguments, and evidence in order to provide a structured argument that a system is safe for use. However, as the system evolves and grows in size, a SAC can be difficult to maintain. In this paper we utilize design science to develop a novel solution for identifying areas of a SAC that are affected by changes to the system. Moreover, we generate actionable recommendations for updating the SAC, including its underlying artifacts and trace links, in order to evolve an existing safety case for use in a new version of the system. Our approach, Safety Artifact Forest Analysis (SAFA), leverages traceability to automatically compare software artifacts from a previously approved or certified version with a new version of the system. We identify, visualize, and explain changes in a Delta Tree. We evaluate our approach using the Dronology system for monitoring and coordinating the actions of cooperating, small Unmanned Aerial Vehicles. Results from a user study show that SAFA helped users to identify changes that potentially impacted system safety and provided information that could be used to help maintain and evolve a SAC. Ankit Agrawal 0002, Seyedehzahra Khoshmanesh, Michael Vierhauser, Mona Rahimi, Jane Cleland-Huang, Robyn R. Lutz |
ICSE | 2 |
| 2019 | Leveraging Feature Similarity for Earlier Detection of Unwanted Feature Interactions in Evolving Software Product Lines
Seyedehzahra Khoshmanesh, Robyn R. Lutz |
SISAP | 1 |
| 2019 | Feature Similarity: A Method to Detect Unwanted Feature Interactions Earlier in Software Product Lines
Seyedehzahra Khoshmanesh, Robyn R. Lutz |
SISAP | 1 |