chore: update CITATION.cff

Author: lars-reimann

CITATION.cff

@@ -2,52 +2,57 @@
 # Visit https://bit.ly/cffinit to generate yours today!
 
 cff-version: 1.2.0
-title: >-
-  Improving the Learnability of Machine Learning APIs
-  by Semi-Automated API Wrapping
-message: Please cite this software using these metadata.
-type: conference-paper
-authors:
-  - given-names: Lars
-    family-names: Reimann
-    email: [email protected]
-    affiliation: >-
-      Institute for Computer Science III, University
-      of Bonn, Germany
-    orcid: 'https://orcid.org/0000-0002-5129-3902'
-  - affiliation: >-
-      Institute for Computer Science III, University
-      of Bonn, Germany
-    given-names: Günter
-    family-names: Kniesel-Wünsche
-identifiers:
-  - type: doi
-    value: 10.1109/ICSE-NIER55298.2022.9793507
-    description: IEEE Xplore
-  - type: doi
-    value: 10.48550/arXiv.2203.15491
-    description: arXiv (preprint)
-repository-code: 'https://github.com/lars-reimann/api-editor'
-abstract: >-
-  A major hurdle for students and professional
-  software developers who want to enter the world of
-  machine learning (ML), is mastering not just the
-  scientific background but also the available ML
-  APIs, Therefore, we address the challenge of
-  creating APIs that are easy to learn and use,
-  especially by novices. However, it is not clear how
-  this can be achieved without compromising
-  expressiveness. We investigate this problem for
-  scikit-learn, a widely used ML API. In this paper,
-  we analyze its use by the Kaggle community,
-  identifying unused and apparently useless parts of
-  the API that can be eliminated without affecting
-  client programs. In addition, we discuss usability
-  issues in the remaining parts, propose related
-  design improvements and show how they can be
-  implemented by semi-automated wrapping of the
-  existing third-party API.
-keywords:
-  - >-
-    APIs , libraries , usability , learnability ,
-    machine learning
+message: >-
+  Please cite this software using the metadata from
+  'preferred-citation'.
+type: software
+title: API-Editor
+repository-code: https://github.com/lars-reimann/api-editor
+preferred-citation:
+  type: conference-paper
+  title: >-
+    Improving the Learnability of Machine Learning APIs
+    by Semi-Automated API Wrapping
+  authors:
+    - given-names: Lars
+      family-names: Reimann
+      email: [email protected]
+      affiliation: >-
+        Institute for Computer Science III, University
+        of Bonn, Germany
+      orcid: 'https://orcid.org/0000-0002-5129-3902'
+    - affiliation: >-
+        Institute for Computer Science III, University
+        of Bonn, Germany
+      given-names: Günter
+      family-names: Kniesel-Wünsche
+  identifiers:
+    - type: doi
+      value: 10.1109/ICSE-NIER55298.2022.9793507
+      description: IEEE Xplore
+    - type: doi
+      value: 10.48550/arXiv.2203.15491
+      description: arXiv (preprint)
+  abstract: >-
+    A major hurdle for students and professional
+    software developers who want to enter the world of
+    machine learning (ML), is mastering not just the
+    scientific background but also the available ML
+    APIs, Therefore, we address the challenge of
+    creating APIs that are easy to learn and use,
+    especially by novices. However, it is not clear how
+    this can be achieved without compromising
+    expressiveness. We investigate this problem for
+    scikit-learn, a widely used ML API. In this paper,
+    we analyze its use by the Kaggle community,
+    identifying unused and apparently useless parts of
+    the API that can be eliminated without affecting
+    client programs. In addition, we discuss usability
+    issues in the remaining parts, propose related
+    design improvements and show how they can be
+    implemented by semi-automated wrapping of the
+    existing third-party API.
+  keywords:
+    - >-
+      APIs , libraries , usability , learnability ,
+      machine learning