2022
DOI: 10.1109/ms.2022.3163011
|View full text |Cite
|
Sign up to set email alerts
|

OSSARA: Abandonment Risk Assessment for Embedded Open Source Components

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
11
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
3
1
1

Relationship

0
5

Authors

Journals

citations
Cited by 6 publications
(11 citation statements)
references
References 13 publications
0
11
0
Order By: Relevance
“…A similar problem with transitive OSS dependencies is known from license violations which can be automatically detected [14] and addressed by implementing best-practices for open-source governance and compliance in companies [16]. Interestingly, there is an explicit call for future work to build a precise model for the automatic identification of unmaintained libraries [27,28] which motivates my work, but neglects to distinguish between the different maintenance activity states identified [9,10,12,18,22,36]. I plan to address this gap by moving from a binary to an multi-class approach.…”
Section: Rq2: Classifying Maintenance Activitiesmentioning
confidence: 97%
See 4 more Smart Citations
“…A similar problem with transitive OSS dependencies is known from license violations which can be automatically detected [14] and addressed by implementing best-practices for open-source governance and compliance in companies [16]. Interestingly, there is an explicit call for future work to build a precise model for the automatic identification of unmaintained libraries [27,28] which motivates my work, but neglects to distinguish between the different maintenance activity states identified [9,10,12,18,22,36]. I plan to address this gap by moving from a binary to an multi-class approach.…”
Section: Rq2: Classifying Maintenance Activitiesmentioning
confidence: 97%
“…Due to space limitations, concrete features for maintenance activities are not mentioned here, as a first literature review found over 30 sources. As maintenance activity labels, the states active, feature complete, dormant and inactive have been identified [9,10,12,18,22,36]. To better understand the underlying data, Choi et al proposed an unsupervised clustering approach ( -means) [7].…”
Section: Rq1: Characterizing Maintenance Activitiesmentioning
confidence: 99%
See 3 more Smart Citations