Details
| Original language | English |
|---|---|
| Title of host publication | Requirements Engineering |
| Subtitle of host publication | Foundation for Software Quality - 30th International Working Conference, REFSQ 2024, Proceedings |
| Editors | Daniel Mendez, Daniel Mendez, Ana Moreira, Ana Moreira |
| Publisher | Springer Nature Switzerland AG |
| Pages | 3-19 |
| Number of pages | 17 |
| ISBN (electronic) | 978-3-031-57327-9 |
| ISBN (print) | 9783031573262 |
| Publication status | Published - 30 Mar 2024 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14588 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
[Context and motivation] Explainability is a software quality aspect that is gaining relevance in the field of requirements engineering. The complexity of modern software systems is steadily growing. Thus, understanding how these systems function becomes increasingly difficult. At the same time, stakeholders rely on these systems in an expanding number of crucial areas, such as medicine and finance. [Question/problem] While a lot of research focuses on how to make AI algorithms explainable, there is a lack of fundamental research on explainability in requirements engineering. For instance, there has been little research on the elicitation and verification of explainability requirements. [Principal ideas/results] Quality models provide means and measures to specify and evaluate quality requirements. As a solid foundation for our quality model, we first conducted a literature review. Based on the results, we then designed a user-centered quality model for explainability. We identified ten different aspects of explainability and offer criteria and metrics to measure them. [Contribution] Our quality model provides metrics that enable software engineers to check whether specified explainability requirements have been met. By identifying different aspects of explainability, we offer a view from different angles that consider different goals of explanations. Thus, we provide a foundation that will improve the management and verification of explainability requirements.
Keywords
- Explainability, Literature Studies, Metrics, Quality Models, Requirements Engineering
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Requirements Engineering: Foundation for Software Quality - 30th International Working Conference, REFSQ 2024, Proceedings. ed. / Daniel Mendez; Daniel Mendez; Ana Moreira; Ana Moreira. Springer Nature Switzerland AG, 2024. p. 3-19 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14588 LNCS).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - How Explainable Is Your System? Towards a Quality Model for Explainability
AU - Deters, Hannah
AU - Droste, Jakob
AU - Obaidi, Martin
AU - Schneider, Kurt
N1 - Funding Information: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No.: 470146331, project softXplain (2022-2025).
PY - 2024/3/30
Y1 - 2024/3/30
N2 - [Context and motivation] Explainability is a software quality aspect that is gaining relevance in the field of requirements engineering. The complexity of modern software systems is steadily growing. Thus, understanding how these systems function becomes increasingly difficult. At the same time, stakeholders rely on these systems in an expanding number of crucial areas, such as medicine and finance. [Question/problem] While a lot of research focuses on how to make AI algorithms explainable, there is a lack of fundamental research on explainability in requirements engineering. For instance, there has been little research on the elicitation and verification of explainability requirements. [Principal ideas/results] Quality models provide means and measures to specify and evaluate quality requirements. As a solid foundation for our quality model, we first conducted a literature review. Based on the results, we then designed a user-centered quality model for explainability. We identified ten different aspects of explainability and offer criteria and metrics to measure them. [Contribution] Our quality model provides metrics that enable software engineers to check whether specified explainability requirements have been met. By identifying different aspects of explainability, we offer a view from different angles that consider different goals of explanations. Thus, we provide a foundation that will improve the management and verification of explainability requirements.
AB - [Context and motivation] Explainability is a software quality aspect that is gaining relevance in the field of requirements engineering. The complexity of modern software systems is steadily growing. Thus, understanding how these systems function becomes increasingly difficult. At the same time, stakeholders rely on these systems in an expanding number of crucial areas, such as medicine and finance. [Question/problem] While a lot of research focuses on how to make AI algorithms explainable, there is a lack of fundamental research on explainability in requirements engineering. For instance, there has been little research on the elicitation and verification of explainability requirements. [Principal ideas/results] Quality models provide means and measures to specify and evaluate quality requirements. As a solid foundation for our quality model, we first conducted a literature review. Based on the results, we then designed a user-centered quality model for explainability. We identified ten different aspects of explainability and offer criteria and metrics to measure them. [Contribution] Our quality model provides metrics that enable software engineers to check whether specified explainability requirements have been met. By identifying different aspects of explainability, we offer a view from different angles that consider different goals of explanations. Thus, we provide a foundation that will improve the management and verification of explainability requirements.
KW - Explainability
KW - Literature Studies
KW - Metrics
KW - Quality Models
KW - Requirements Engineering
UR - http://www.scopus.com/inward/record.url?scp=85190708404&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-57327-9_1
DO - 10.1007/978-3-031-57327-9_1
M3 - Contribution to book/anthology
SN - 9783031573262
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 19
BT - Requirements Engineering
A2 - Mendez, Daniel
A2 - Mendez, Daniel
A2 - Moreira, Ana
A2 - Moreira, Ana
PB - Springer Nature Switzerland AG
ER -