---
_id: '64823'
abstract:
- lang: eng
text: "Current legal frameworks enforce that Android developers accurately report
the data their apps collect. However, large codebases can make this reporting
challenging. This paper employs an empirical approach to understand developers'
experience with Google Play Store's Data Safety Section (DSS) form.\r\n\r\nWe
first survey 41 Android developers to understand how they categorize privacy-related
data into DSS categories and how confident they feel when completing the DSS form.
To gain a broader and more detailed view of the challenges developers encounter
during the process, we complement the survey with an analysis of 172 online developer
discussions, capturing the perspectives of 642 additional developers. Together,
these two data sources represent insights from 683 developers.\r\n\r\nOur findings
reveal that developers often manually classify the privacy-related data their
apps collect into the data categories defined by Google-or, in some cases, omit
classification entirely-and rely heavily on existing online resources when completing
the form. Moreover, developers are generally confident in recognizing the data
their apps collect, yet they lack confidence in translating this knowledge into
DSS-compliant disclosures. Key challenges include issues in identifying privacy-relevant
data to complete the form, limited understanding of the form, and concerns about
app rejection due to discrepancies with Google's privacy requirements.\r\nThese
results underscore the need for clearer guidance and more accessible tooling to
support developers in meeting privacy-aware reporting obligations. "
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Mohamed Aboubakr Mohamed
full_name: Soliman, Mohamed Aboubakr Mohamed
id: '102489'
last_name: Soliman
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Khedkar M, Schlichtig M, Soliman MAM, Bodden E. Challenges in Android Data
Disclosure: An Empirical Study. In: Proceedings of the IEEE/ACM 13th International
Conference on Mobile Software Engineering and Systems (MOBILESoft ’26). Association
for Computing Machinery, New York, NY, USA, 65–68. ; 2026.'
apa: 'Khedkar, M., Schlichtig, M., Soliman, M. A. M., & Bodden, E. (2026). Challenges
in Android Data Disclosure: An Empirical Study. Proceedings of the IEEE/ACM
13th International Conference on Mobile Software Engineering and Systems (MOBILESoft
’26). Association for Computing Machinery, New York, NY, USA, 65–68. 13th
International Conference on Mobile Software Engineering and Systems 2024, Rio
de Janeiro, Brazil.'
bibtex: '@inproceedings{Khedkar_Schlichtig_Soliman_Bodden_2026, title={Challenges
in Android Data Disclosure: An Empirical Study.}, booktitle={Proceedings of the
IEEE/ACM 13th International Conference on Mobile Software Engineering and Systems
(MOBILESoft ’26). Association for Computing Machinery, New York, NY, USA, 65–68.},
author={Khedkar, Mugdha and Schlichtig, Michael and Soliman, Mohamed Aboubakr
Mohamed and Bodden, Eric}, year={2026} }'
chicago: 'Khedkar, Mugdha, Michael Schlichtig, Mohamed Aboubakr Mohamed Soliman,
and Eric Bodden. “Challenges in Android Data Disclosure: An Empirical Study.”
In Proceedings of the IEEE/ACM 13th International Conference on Mobile Software
Engineering and Systems (MOBILESoft ’26). Association for Computing Machinery,
New York, NY, USA, 65–68., 2026.'
ieee: 'M. Khedkar, M. Schlichtig, M. A. M. Soliman, and E. Bodden, “Challenges in
Android Data Disclosure: An Empirical Study.,” presented at the 13th International
Conference on Mobile Software Engineering and Systems 2024, Rio de Janeiro, Brazil,
2026.'
mla: 'Khedkar, Mugdha, et al. “Challenges in Android Data Disclosure: An Empirical
Study.” Proceedings of the IEEE/ACM 13th International Conference on Mobile
Software Engineering and Systems (MOBILESoft ’26). Association for Computing Machinery,
New York, NY, USA, 65–68., 2026.'
short: 'M. Khedkar, M. Schlichtig, M.A.M. Soliman, E. Bodden, in: Proceedings of
the IEEE/ACM 13th International Conference on Mobile Software Engineering and
Systems (MOBILESoft ’26). Association for Computing Machinery, New York, NY, USA,
65–68., 2026.'
conference:
end_date: 2026-04-18
location: Rio de Janeiro, Brazil
name: 13th International Conference on Mobile Software Engineering and Systems 2024
start_date: 2026-04-12
date_created: 2026-03-04T08:10:43Z
date_updated: 2026-03-13T12:10:10Z
department:
- _id: '76'
external_id:
arxiv:
- '2601.20459'
keyword:
- static analysis
- data collection
- data protection
- privacy-aware reporting
language:
- iso: eng
publication: Proceedings of the IEEE/ACM 13th International Conference on Mobile Software
Engineering and Systems (MOBILESoft '26). Association for Computing Machinery, New
York, NY, USA, 65–68.
status: public
title: 'Challenges in Android Data Disclosure: An Empirical Study.'
type: conference
user_id: '88024'
year: '2026'
...
---
_id: '64821'
article_number: '56'
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Nihad
full_name: Atakishiyev, Nihad
last_name: Atakishiyev
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Khedkar M, Schlichtig M, Atakishiyev N, Bodden E. Between Law and Code: Challenges
and Opportunities for Automating Privacy Assessments. Automated Software Engineering
. 2026;33(2). doi:10.1007/s10515-026-00601-4'
apa: 'Khedkar, M., Schlichtig, M., Atakishiyev, N., & Bodden, E. (2026). Between
Law and Code: Challenges and Opportunities for Automating Privacy Assessments.
Automated Software Engineering , 33(2), Article 56. https://doi.org/10.1007/s10515-026-00601-4'
bibtex: '@article{Khedkar_Schlichtig_Atakishiyev_Bodden_2026, title={Between Law
and Code: Challenges and Opportunities for Automating Privacy Assessments}, volume={33},
DOI={10.1007/s10515-026-00601-4},
number={256}, journal={Automated Software Engineering }, publisher={Springer US},
author={Khedkar, Mugdha and Schlichtig, Michael and Atakishiyev, Nihad and Bodden,
Eric}, year={2026} }'
chicago: 'Khedkar, Mugdha, Michael Schlichtig, Nihad Atakishiyev, and Eric Bodden.
“Between Law and Code: Challenges and Opportunities for Automating Privacy Assessments.”
Automated Software Engineering 33, no. 2 (2026). https://doi.org/10.1007/s10515-026-00601-4.'
ieee: 'M. Khedkar, M. Schlichtig, N. Atakishiyev, and E. Bodden, “Between Law and
Code: Challenges and Opportunities for Automating Privacy Assessments,” Automated
Software Engineering , vol. 33, no. 2, Art. no. 56, 2026, doi: 10.1007/s10515-026-00601-4.'
mla: 'Khedkar, Mugdha, et al. “Between Law and Code: Challenges and Opportunities
for Automating Privacy Assessments.” Automated Software Engineering , vol.
33, no. 2, 56, Springer US, 2026, doi:10.1007/s10515-026-00601-4.'
short: M. Khedkar, M. Schlichtig, N. Atakishiyev, E. Bodden, Automated Software
Engineering 33 (2026).
date_created: 2026-03-04T08:03:14Z
date_updated: 2026-03-13T12:10:38Z
department:
- _id: '76'
doi: 10.1007/s10515-026-00601-4
intvolume: ' 33'
issue: '2'
language:
- iso: eng
publication: 'Automated Software Engineering '
publication_identifier:
unknown:
- 1573-7535
publisher: Springer US
status: public
title: 'Between Law and Code: Challenges and Opportunities for Automating Privacy
Assessments'
type: journal_article
user_id: '88024'
volume: 33
year: '2026'
...
---
_id: '64909'
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Khedkar M, Schlichtig M, Bodden E. Source Code-Driven GDPR Documentation:
Supporting RoPA with Assessor View. In: IEEE International Conference on Software
Analysis, Evolution and Reengineering (SANER 2026). ; 2026.'
apa: 'Khedkar, M., Schlichtig, M., & Bodden, E. (2026). Source Code-Driven GDPR
Documentation: Supporting RoPA with Assessor View. IEEE International Conference
on Software Analysis, Evolution and Reengineering (SANER 2026).'
bibtex: '@inproceedings{Khedkar_Schlichtig_Bodden_2026, title={Source Code-Driven
GDPR Documentation: Supporting RoPA with Assessor View}, booktitle={IEEE International
Conference on Software Analysis, Evolution and Reengineering (SANER 2026)}, author={Khedkar,
Mugdha and Schlichtig, Michael and Bodden, Eric}, year={2026} }'
chicago: 'Khedkar, Mugdha, Michael Schlichtig, and Eric Bodden. “Source Code-Driven
GDPR Documentation: Supporting RoPA with Assessor View.” In IEEE International
Conference on Software Analysis, Evolution and Reengineering (SANER 2026),
2026.'
ieee: 'M. Khedkar, M. Schlichtig, and E. Bodden, “Source Code-Driven GDPR Documentation:
Supporting RoPA with Assessor View,” 2026.'
mla: 'Khedkar, Mugdha, et al. “Source Code-Driven GDPR Documentation: Supporting
RoPA with Assessor View.” IEEE International Conference on Software Analysis,
Evolution and Reengineering (SANER 2026), 2026.'
short: 'M. Khedkar, M. Schlichtig, E. Bodden, in: IEEE International Conference
on Software Analysis, Evolution and Reengineering (SANER 2026), 2026.'
date_created: 2026-03-13T12:16:09Z
date_updated: 2026-03-13T12:17:01Z
department:
- _id: '76'
language:
- iso: eng
main_file_link:
- url: https://mugdhak30.github.io/assets/Preprints/RoPA_SANER2026.pdf
publication: IEEE International Conference on Software Analysis, Evolution and Reengineering
(SANER 2026)
status: public
title: 'Source Code-Driven GDPR Documentation: Supporting RoPA with Assessor View'
type: conference
user_id: '88024'
year: '2026'
...
---
_id: '65017'
abstract:
- lang: eng
text: Static Application Security Testing (SAST) tools play a vital role in modern
software development by automatically detecting potential vulnerabilities in source
code. However, their effectiveness is often limited by a high rate of false positives,
which wastes developer's effort and undermines trust in automated analysis. This
work presents a Graph Convolutional Network (GCN) model designed to predict SAST
reports as true and false positive. The model leverages Code Property Graphs (CPGs)
constructed from static analysis results to capture both, structural and semantic
relationships within code. Trained on the CamBenchCAP dataset, the model achieved
an accuracy of 100% on the test set using an 80/20 train-test split. Evaluation
on the CryptoAPI-Bench benchmark further demonstrated the model's practical applicability,
reaching an overall accuracy of up to 96.6%. A detailed qualitative inspection
revealed that many cases marked as misclassifications corresponded to genuine
security weaknesses, indicating that the model effectively reflects conservative,
security-aware reasoning. Identified limitations include incomplete control-flow
representation due to missing interprocedural connections. Future work will focus
on integrating call graphs, applying graph explainability techniques, and extending
training data across multiple SAST tools to improve generalization and interpretability.
author:
- first_name: Tom
full_name: Ohlmer, Tom
last_name: Ohlmer
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: Ohlmer T, Schlichtig M, Bodden E. FP-Predictor - False Positive Prediction
for Static Analysis Reports. arXiv:260310558. Published online 2026.
apa: Ohlmer, T., Schlichtig, M., & Bodden, E. (2026). FP-Predictor - False Positive
Prediction for Static Analysis Reports. In arXiv:2603.10558.
bibtex: '@article{Ohlmer_Schlichtig_Bodden_2026, title={FP-Predictor - False Positive
Prediction for Static Analysis Reports}, journal={arXiv:2603.10558}, author={Ohlmer,
Tom and Schlichtig, Michael and Bodden, Eric}, year={2026} }'
chicago: Ohlmer, Tom, Michael Schlichtig, and Eric Bodden. “FP-Predictor - False
Positive Prediction for Static Analysis Reports.” ArXiv:2603.10558, 2026.
ieee: T. Ohlmer, M. Schlichtig, and E. Bodden, “FP-Predictor - False Positive Prediction
for Static Analysis Reports,” arXiv:2603.10558. 2026.
mla: Ohlmer, Tom, et al. “FP-Predictor - False Positive Prediction for Static Analysis
Reports.” ArXiv:2603.10558, 2026.
short: T. Ohlmer, M. Schlichtig, E. Bodden, ArXiv:2603.10558 (2026).
date_created: 2026-03-16T17:38:33Z
date_updated: 2026-03-16T17:40:31Z
department:
- _id: '76'
external_id:
arxiv:
- '2603.10558'
language:
- iso: eng
publication: arXiv:2603.10558
status: public
title: FP-Predictor - False Positive Prediction for Static Analysis Reports
type: preprint
user_id: '32312'
year: '2026'
...
---
_id: '65030'
author:
- first_name: Luis
full_name: Amaral, Luis
last_name: Amaral
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Wagner
full_name: Emanuel, Wagner
last_name: Emanuel
- first_name: Joilton
full_name: Almeida, Joilton
last_name: Almeida
- first_name: Carine
full_name: Ferreira, Carine
last_name: Ferreira
- first_name: Jérôme
full_name: Kempf, Jérôme
last_name: Kempf
- first_name: Rodrigo
full_name: Bonifácio, Rodrigo
last_name: Bonifácio
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
- first_name: Laerte
full_name: Peotta, Laerte
last_name: Peotta
- first_name: Gustavo
full_name: Pinto, Gustavo
last_name: Pinto
- first_name: Márcio
full_name: Ribeiro, Márcio
last_name: Ribeiro
citation:
ama: 'Amaral L, Schlichtig M, Emanuel W, et al. From Legacy Designs to Vulnerability
Fixes: Understanding SAST Adoption in Non-Technological Companies. In: 2026
IEEE International Conference on Software Analysis, Evolution and Reengineering
(SANER). ; 2026.'
apa: 'Amaral, L., Schlichtig, M., Emanuel, W., Almeida, J., Ferreira, C., Kempf,
J., Bonifácio, R., Bodden, E., Peotta, L., Pinto, G., & Ribeiro, M. (2026).
From Legacy Designs to Vulnerability Fixes: Understanding SAST Adoption in Non-Technological
Companies. 2026 IEEE International Conference on Software Analysis, Evolution
and Reengineering (SANER).'
bibtex: '@inproceedings{Amaral_Schlichtig_Emanuel_Almeida_Ferreira_Kempf_Bonifácio_Bodden_Peotta_Pinto_et
al._2026, title={From Legacy Designs to Vulnerability Fixes: Understanding SAST
Adoption in Non-Technological Companies}, booktitle={2026 IEEE International Conference
on Software Analysis, Evolution and Reengineering (SANER)}, author={Amaral, Luis
and Schlichtig, Michael and Emanuel, Wagner and Almeida, Joilton and Ferreira,
Carine and Kempf, Jérôme and Bonifácio, Rodrigo and Bodden, Eric and Peotta, Laerte
and Pinto, Gustavo and et al.}, year={2026} }'
chicago: 'Amaral, Luis, Michael Schlichtig, Wagner Emanuel, Joilton Almeida, Carine
Ferreira, Jérôme Kempf, Rodrigo Bonifácio, et al. “From Legacy Designs to Vulnerability
Fixes: Understanding SAST Adoption in Non-Technological Companies.” In 2026
IEEE International Conference on Software Analysis, Evolution and Reengineering
(SANER), 2026.'
ieee: 'L. Amaral et al., “From Legacy Designs to Vulnerability Fixes: Understanding
SAST Adoption in Non-Technological Companies,” 2026.'
mla: 'Amaral, Luis, et al. “From Legacy Designs to Vulnerability Fixes: Understanding
SAST Adoption in Non-Technological Companies.” 2026 IEEE International Conference
on Software Analysis, Evolution and Reengineering (SANER), 2026.'
short: 'L. Amaral, M. Schlichtig, W. Emanuel, J. Almeida, C. Ferreira, J. Kempf,
R. Bonifácio, E. Bodden, L. Peotta, G. Pinto, M. Ribeiro, in: 2026 IEEE International
Conference on Software Analysis, Evolution and Reengineering (SANER), 2026.'
date_created: 2026-03-17T11:59:09Z
date_updated: 2026-03-17T12:02:14Z
department:
- _id: '76'
language:
- iso: eng
publication: 2026 IEEE International Conference on Software Analysis, Evolution and
Reengineering (SANER)
status: public
title: 'From Legacy Designs to Vulnerability Fixes: Understanding SAST Adoption in
Non-Technological Companies'
type: conference
user_id: '32312'
year: '2026'
...
---
_id: '65018'
abstract:
- lang: eng
text: "Android applications collecting data from users must protect it according
to the current legal frameworks. Such data protection has become even more important
since in 2018 the European Union rolled out the General Data Protection Regulation
(GDPR). Since app developers are not legal experts, they find it difficult to
integrate privacy-aware practices into source code development. Despite these
legal obligations, developers have limited tool support to reason about data protection
throughout their app development process.\r\n This paper explores the use of
static program slicing and software visualization to analyze privacy-relevant
data flows in Android apps. We introduce SliceViz, a web tool that analyzes an
Android app by slicing all privacy-relevant data sources detected in the source
code on the back-end. It then helps developers by visualizing these privacy-relevant
program slices.\r\n We conducted a user study with 12 participants demonstrating
that SliceViz effectively aids developers in identifying privacy-relevant properties
in Android apps.\r\n Our findings indicate that program slicing can be employed
to identify and reason about privacy-relevant data flows in Android applications.
With further usability improvements, developers can be better equipped to handle
privacy-sensitive information."
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Santhosh
full_name: Mohan, Santhosh
last_name: Mohan
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: Khedkar M, Schlichtig M, Mohan S, Bodden E. Visualizing Privacy-Relevant Data
Flows in Android Applications. arXiv:250316640. Published online 2025.
apa: Khedkar, M., Schlichtig, M., Mohan, S., & Bodden, E. (2025). Visualizing
Privacy-Relevant Data Flows in Android Applications. In arXiv:2503.16640.
bibtex: '@article{Khedkar_Schlichtig_Mohan_Bodden_2025, title={Visualizing Privacy-Relevant
Data Flows in Android Applications}, journal={arXiv:2503.16640}, author={Khedkar,
Mugdha and Schlichtig, Michael and Mohan, Santhosh and Bodden, Eric}, year={2025}
}'
chicago: Khedkar, Mugdha, Michael Schlichtig, Santhosh Mohan, and Eric Bodden. “Visualizing
Privacy-Relevant Data Flows in Android Applications.” ArXiv:2503.16640,
2025.
ieee: M. Khedkar, M. Schlichtig, S. Mohan, and E. Bodden, “Visualizing Privacy-Relevant
Data Flows in Android Applications,” arXiv:2503.16640. 2025.
mla: Khedkar, Mugdha, et al. “Visualizing Privacy-Relevant Data Flows in Android
Applications.” ArXiv:2503.16640, 2025.
short: M. Khedkar, M. Schlichtig, S. Mohan, E. Bodden, ArXiv:2503.16640 (2025).
date_created: 2026-03-16T17:39:12Z
date_updated: 2026-03-16T17:40:56Z
department:
- _id: '76'
external_id:
arxiv:
- '2503.16640'
language:
- iso: eng
publication: arXiv:2503.16640
status: public
title: Visualizing Privacy-Relevant Data Flows in Android Applications
type: preprint
user_id: '32312'
year: '2025'
...
---
_id: '52663'
abstract:
- lang: eng
text: "Context\r\nStatic analyses are well-established to aid in understanding bugs
or vulnerabilities during the development process or in large-scale studies. A
low false-positive rate is essential for the adaption in practice and for precise
results of empirical studies. Unfortunately, static analyses tend to report where
a vulnerability manifests rather than the fix location. This can cause presumed
false positives or imprecise results.\r\nMethod\r\nTo address this problem, we
designed an adaption of an existing static analysis algorithm that can distinguish
between a manifestation and fix location, and reports error chains. An error chain
represents at least two interconnected errors that occur successively, thus building
the connection between the fix and manifestation location. We used our tool CogniCryptSUBS
for a case study on 471 GitHub repositories, a performance benchmark to compare
different analysis configurations, and conducted an expert interview.\r\nResult\r\nWe
found that 50 % of the projects with a report had at least one error chain. Our
runtime benchmark demonstrated that our improvement caused only a minimal runtime
overhead of less than 4 %. The results of our expert interview indicate that with
our adapted version participants require fewer executions of the analysis.\r\nConclusion\r\nOur
results indicate that error chains occur frequently in real-world projects, and
ignoring them can lead to imprecise evaluation results. The runtime benchmark
indicates that our tool is a feasible and efficient solution for detecting error
chains in real-world projects. Further, our results gave a hint that the usability
of static analyses may benefit from supporting error chains."
author:
- first_name: Anna-Katharina
full_name: Wickert, Anna-Katharina
last_name: Wickert
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Marvin
full_name: Vogel, Marvin
last_name: Vogel
- first_name: Lukas
full_name: Winter, Lukas
last_name: Winter
- first_name: Mira
full_name: Mezini, Mira
last_name: Mezini
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: Wickert A-K, Schlichtig M, Vogel M, Winter L, Mezini M, Bodden E. Supporting
Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability.;
2024.
apa: Wickert, A.-K., Schlichtig, M., Vogel, M., Winter, L., Mezini, M., & Bodden,
E. (2024). Supporting Error Chains in Static Analysis for Precise Evaluation
Results and Enhanced Usability.
bibtex: '@book{Wickert_Schlichtig_Vogel_Winter_Mezini_Bodden_2024, title={Supporting
Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability},
author={Wickert, Anna-Katharina and Schlichtig, Michael and Vogel, Marvin and
Winter, Lukas and Mezini, Mira and Bodden, Eric}, year={2024} }'
chicago: Wickert, Anna-Katharina, Michael Schlichtig, Marvin Vogel, Lukas Winter,
Mira Mezini, and Eric Bodden. Supporting Error Chains in Static Analysis for
Precise Evaluation Results and Enhanced Usability, 2024.
ieee: A.-K. Wickert, M. Schlichtig, M. Vogel, L. Winter, M. Mezini, and E. Bodden,
Supporting Error Chains in Static Analysis for Precise Evaluation Results and
Enhanced Usability. 2024.
mla: Wickert, Anna-Katharina, et al. Supporting Error Chains in Static Analysis
for Precise Evaluation Results and Enhanced Usability. 2024.
short: A.-K. Wickert, M. Schlichtig, M. Vogel, L. Winter, M. Mezini, E. Bodden,
Supporting Error Chains in Static Analysis for Precise Evaluation Results and
Enhanced Usability, 2024.
date_created: 2024-03-20T09:28:36Z
date_updated: 2024-03-20T09:32:29Z
department:
- _id: '76'
keyword:
- Static analysis
- error chains
- false positive re- duction
- empirical studies
language:
- iso: eng
main_file_link:
- url: https://arxiv.org/abs/2403.07808
status: public
title: Supporting Error Chains in Static Analysis for Precise Evaluation Results and
Enhanced Usability
type: misc
user_id: '32312'
year: '2024'
...
---
_id: '56140'
abstract:
- lang: eng
text: " Android apps collecting data from users must comply with legal frameworks
to ensure data protection. This requirement has become even more important since
the implementation of the General Data Protection Regulation (GDPR) by the European
Union in 2018. Moreover, with the proposed Cyber Resilience Act on the horizon,
stakeholders will soon need to assess software against even more stringent security
and privacy standards. Effective privacy assessments require collaboration among
groups with diverse expertise to function effectively as a cohesive unit.\r\n
\ This paper motivates the need for an automated approach that enhances understanding
of data protection in Android apps and improves communication between the various
parties involved in privacy assessments. We propose the Assessor View, a tool
designed to bridge the knowledge gap between these parties, facilitating more
effective privacy assessments of Android applications. "
author:
- first_name: Mugdha
full_name: Khedkar, Mugdha
id: '88024'
last_name: Khedkar
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Khedkar M, Schlichtig M, Bodden E. Advancing Android Privacy Assessments with
Automation. In: In Proceedings of the 39th IEEE/ACM International Conference
on Automated Software Engineering Workshops (ASEW ’24). ; 2024. doi:10.1145/3691621.3694953'
apa: Khedkar, M., Schlichtig, M., & Bodden, E. (2024). Advancing Android Privacy
Assessments with Automation. In Proceedings of the 39th IEEE/ACM International
Conference on Automated Software Engineering Workshops (ASEW ’24). 39th IEEE/ACM
International Conference on Automated Software Engineering (ASE 2024), Sacramento,
California. https://doi.org/10.1145/3691621.3694953
bibtex: '@inproceedings{Khedkar_Schlichtig_Bodden_2024, title={Advancing Android
Privacy Assessments with Automation}, DOI={10.1145/3691621.3694953},
booktitle={In Proceedings of the 39th IEEE/ACM International Conference on Automated
Software Engineering Workshops (ASEW ’24)}, author={Khedkar, Mugdha and Schlichtig,
Michael and Bodden, Eric}, year={2024} }'
chicago: Khedkar, Mugdha, Michael Schlichtig, and Eric Bodden. “Advancing Android
Privacy Assessments with Automation.” In In Proceedings of the 39th IEEE/ACM
International Conference on Automated Software Engineering Workshops (ASEW ’24),
2024. https://doi.org/10.1145/3691621.3694953.
ieee: 'M. Khedkar, M. Schlichtig, and E. Bodden, “Advancing Android Privacy Assessments
with Automation,” presented at the 39th IEEE/ACM International Conference on Automated
Software Engineering (ASE 2024), Sacramento, California, 2024, doi: 10.1145/3691621.3694953.'
mla: Khedkar, Mugdha, et al. “Advancing Android Privacy Assessments with Automation.”
In Proceedings of the 39th IEEE/ACM International Conference on Automated Software
Engineering Workshops (ASEW ’24), 2024, doi:10.1145/3691621.3694953.
short: 'M. Khedkar, M. Schlichtig, E. Bodden, in: In Proceedings of the 39th IEEE/ACM
International Conference on Automated Software Engineering Workshops (ASEW ’24),
2024.'
conference:
end_date: 2024-11-01
location: Sacramento, California
name: 39th IEEE/ACM International Conference on Automated Software Engineering (ASE
2024)
start_date: 2024-10-27
date_created: 2024-09-16T08:55:34Z
date_updated: 2026-03-13T12:12:45Z
ddc:
- '000'
department:
- _id: '76'
doi: 10.1145/3691621.3694953
external_id:
arxiv:
- '2409.06564'
file:
- access_level: closed
content_type: application/pdf
creator: khedkarm
date_created: 2024-09-16T08:55:23Z
date_updated: 2024-09-16T08:55:23Z
file_id: '56141'
file_name: 2409.06564v1.pdf
file_size: 1207856
relation: main_file
success: 1
file_date_updated: 2024-09-16T08:55:23Z
has_accepted_license: '1'
language:
- iso: eng
publication: In Proceedings of the 39th IEEE/ACM International Conference on Automated
Software Engineering Workshops (ASEW ’24)
status: public
title: Advancing Android Privacy Assessments with Automation
type: conference
user_id: '32312'
year: '2024'
...
---
_id: '52662'
abstract:
- lang: eng
text: Static analysis tools support developers in detecting potential coding issues,
such as bugs or vulnerabilities. Research emphasizes technical challenges of such
tools but also mentions severe usability shortcomings. These shortcomings hinder
the adoption of static analysis tools, and user dissatisfaction may even lead
to tool abandonment. To comprehensively assess the state of the art, we present
the first systematic usability evaluation of a wide range of static analysis tools.
We derived a set of 36 relevant criteria from the literature and used them to
evaluate a total of 46 static analysis tools complying with our inclusion and
exclusion criteria - a representative set of mainly non-proprietary tools. The
evaluation against the usability criteria in a multiple-raters approach shows
that two thirds of the considered tools off er poor warning messages, while about
three-quarters provide hardly any fix support. Furthermore, the integration of
user knowledge is strongly neglected, which could be used for instance, to improve
handling of false positives. Finally, issues regarding workflow integration and
specialized user interfaces are revealed. These findings should prove useful in
guiding and focusing further research and development in user experience for static
code analyses.
author:
- first_name: Marcus
full_name: Nachtigall, Marcus
id: '41213'
last_name: Nachtigall
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Nachtigall M, Schlichtig M, Bodden E. Evaluation of Usability Criteria Addressed
by Static Analysis Tools on a Large Scale. In: Software Engineering 2023.
Gesellschaft für Informatik e.V.; 2023:95–96.'
apa: Nachtigall, M., Schlichtig, M., & Bodden, E. (2023). Evaluation of Usability
Criteria Addressed by Static Analysis Tools on a Large Scale. In Software Engineering
2023 (pp. 95–96). Gesellschaft für Informatik e.V.
bibtex: '@inbook{Nachtigall_Schlichtig_Bodden_2023, place={Bonn}, title={Evaluation
of Usability Criteria Addressed by Static Analysis Tools on a Large Scale}, booktitle={Software
Engineering 2023}, publisher={Gesellschaft für Informatik e.V.}, author={Nachtigall,
Marcus and Schlichtig, Michael and Bodden, Eric}, year={2023}, pages={95–96} }'
chicago: 'Nachtigall, Marcus, Michael Schlichtig, and Eric Bodden. “Evaluation of
Usability Criteria Addressed by Static Analysis Tools on a Large Scale.” In Software
Engineering 2023, 95–96. Bonn: Gesellschaft für Informatik e.V., 2023.'
ieee: 'M. Nachtigall, M. Schlichtig, and E. Bodden, “Evaluation of Usability Criteria
Addressed by Static Analysis Tools on a Large Scale,” in Software Engineering
2023, Bonn: Gesellschaft für Informatik e.V., 2023, pp. 95–96.'
mla: Nachtigall, Marcus, et al. “Evaluation of Usability Criteria Addressed by Static
Analysis Tools on a Large Scale.” Software Engineering 2023, Gesellschaft
für Informatik e.V., 2023, pp. 95–96.
short: 'M. Nachtigall, M. Schlichtig, E. Bodden, in: Software Engineering 2023,
Gesellschaft für Informatik e.V., Bonn, 2023, pp. 95–96.'
date_created: 2024-03-20T09:26:29Z
date_updated: 2024-03-20T09:27:41Z
department:
- _id: '76'
keyword:
- Automated static analysis
- Software usability
language:
- iso: eng
main_file_link:
- url: https://dl.gi.de/items/5afe477f-2f6a-4b3d-b391-f024baf0b7a5
page: 95–96
place: Bonn
publication: Software Engineering 2023
publication_identifier:
isbn:
- 978-3-88579-726-5
publisher: Gesellschaft für Informatik e.V.
status: public
title: Evaluation of Usability Criteria Addressed by Static Analysis Tools on a Large
Scale
type: book_chapter
user_id: '32312'
year: '2023'
...
---
_id: '52660'
abstract:
- lang: eng
text: Application Programming Interfaces (APIs) are the primary mechanism developers
use to obtain access to third-party algorithms and services. Unfortunately, APIs
can be misused, which can have catastrophic consequences, especially if the APIs
provide security-critical functionalities like cryptography. Understanding what
API misuses are, and how they are caused, is important to prevent them, eg, with
API misuse detectors. However, definitions for API misuses and related terms in
literature vary. This paper presents a systematic literature review to clarify
these terms and introduces FUM, a novel Framework for API Usage constraint and
Misuse classification. The literature review revealed that API misuses are violations
of API usage constraints. To address this, we provide unified definitions and
use them to derive FUM. To assess the extent to which FUM aids in determining
and guiding the improvement of an API misuses detector’s capabilities, we performed
a case study on the state-of the-art misuse detection tool CogniCrypt. The study
showed that FUM can be used to properly assess CogniCrypt’s capabilities, identify
weaknesses and assist in deriving mitigations and improvements.
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Steffen
full_name: Sassalla, Steffen
last_name: Sassalla
- first_name: Krishna
full_name: Narasimhan, Krishna
last_name: Narasimhan
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Schlichtig M, Sassalla S, Narasimhan K, Bodden E. Introducing FUM: A Framework
for API Usage Constraint and Misuse Classification. In: Software Engineering
2023. Gesellschaft für Informatik e.V.; 2023:105–106.'
apa: 'Schlichtig, M., Sassalla, S., Narasimhan, K., & Bodden, E. (2023). Introducing
FUM: A Framework for API Usage Constraint and Misuse Classification. In Software
Engineering 2023 (pp. 105–106). Gesellschaft für Informatik e.V.'
bibtex: '@inbook{Schlichtig_Sassalla_Narasimhan_Bodden_2023, place={Bonn}, title={Introducing
FUM: A Framework for API Usage Constraint and Misuse Classification}, booktitle={Software
Engineering 2023}, publisher={Gesellschaft für Informatik e.V.}, author={Schlichtig,
Michael and Sassalla, Steffen and Narasimhan, Krishna and Bodden, Eric}, year={2023},
pages={105–106} }'
chicago: 'Schlichtig, Michael, Steffen Sassalla, Krishna Narasimhan, and Eric Bodden.
“Introducing FUM: A Framework for API Usage Constraint and Misuse Classification.”
In Software Engineering 2023, 105–106. Bonn: Gesellschaft für Informatik
e.V., 2023.'
ieee: 'M. Schlichtig, S. Sassalla, K. Narasimhan, and E. Bodden, “Introducing FUM:
A Framework for API Usage Constraint and Misuse Classification,” in Software
Engineering 2023, Bonn: Gesellschaft für Informatik e.V., 2023, pp. 105–106.'
mla: 'Schlichtig, Michael, et al. “Introducing FUM: A Framework for API Usage Constraint
and Misuse Classification.” Software Engineering 2023, Gesellschaft für
Informatik e.V., 2023, pp. 105–106.'
short: 'M. Schlichtig, S. Sassalla, K. Narasimhan, E. Bodden, in: Software Engineering
2023, Gesellschaft für Informatik e.V., Bonn, 2023, pp. 105–106.'
date_created: 2024-03-20T09:22:27Z
date_updated: 2024-03-20T09:25:46Z
department:
- _id: '76'
keyword:
- API misuses API usage constraints
- classification framework
- API misuse detection
- static analysis
language:
- iso: eng
main_file_link:
- url: https://dl.gi.de/items/c4825557-cf3d-4038-933a-d8f95fd324a2
page: 105–106
place: Bonn
publication: Software Engineering 2023
publication_identifier:
isbn:
- 978-3-88579-726-5
publisher: Gesellschaft für Informatik e.V.
status: public
title: 'Introducing FUM: A Framework for API Usage Constraint and Misuse Classification'
type: book_chapter
user_id: '32312'
year: '2023'
...
---
_id: '32409'
abstract:
- lang: eng
text: 'Context: Cryptographic APIs are often misused in real-world applications.
Therefore, many cryptographic API misuse detection tools have been introduced.
However, there exists no established reference benchmark for a fair and comprehensive
comparison and evaluation of these tools. While there are benchmarks, they often
only address a subset of the domain or were only used to evaluate a subset of
existing misuse detection tools. Objective: To fairly compare cryptographic API
misuse detection tools and to drive future development in this domain, we will
devise such a benchmark. Openness and transparency in the generation process are
key factors to fairly generate and establish the needed benchmark. Method: We
propose an approach where we derive the benchmark generation methodology from
the literature which consists of general best practices in benchmarking and domain-specific
benchmark generation. A part of this methodology is transparency and openness
of the generation process, which is achieved by pre-registering this work. Based
on our methodology we design CamBench, a fair "Cryptographic API Misuse Detection
Tool Benchmark Suite". We will implement the first version of CamBench limiting
the domain to Java, the JCA, and static analyses. Finally, we will use CamBench
to compare current misuse detection tools and compare CamBench to related benchmarks
of its domain.'
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Anna-Katharina
full_name: Wickert, Anna-Katharina
last_name: Wickert
- first_name: Stefan
full_name: Krüger, Stefan
last_name: Krüger
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
- first_name: Mira
full_name: Mezini, Mira
last_name: Mezini
citation:
ama: Schlichtig M, Wickert A-K, Krüger S, Bodden E, Mezini M. CamBench -- Cryptographic
API Misuse Detection Tool Benchmark Suite.; 2022. doi:10.48550/ARXIV.2204.06447
apa: Schlichtig, M., Wickert, A.-K., Krüger, S., Bodden, E., & Mezini, M. (2022).
CamBench -- Cryptographic API Misuse Detection Tool Benchmark Suite. https://doi.org/10.48550/ARXIV.2204.06447
bibtex: '@book{Schlichtig_Wickert_Krüger_Bodden_Mezini_2022, title={CamBench --
Cryptographic API Misuse Detection Tool Benchmark Suite}, DOI={10.48550/ARXIV.2204.06447},
author={Schlichtig, Michael and Wickert, Anna-Katharina and Krüger, Stefan and
Bodden, Eric and Mezini, Mira}, year={2022} }'
chicago: Schlichtig, Michael, Anna-Katharina Wickert, Stefan Krüger, Eric Bodden,
and Mira Mezini. CamBench -- Cryptographic API Misuse Detection Tool Benchmark
Suite, 2022. https://doi.org/10.48550/ARXIV.2204.06447.
ieee: M. Schlichtig, A.-K. Wickert, S. Krüger, E. Bodden, and M. Mezini, CamBench
-- Cryptographic API Misuse Detection Tool Benchmark Suite. 2022.
mla: Schlichtig, Michael, et al. CamBench -- Cryptographic API Misuse Detection
Tool Benchmark Suite. 2022, doi:10.48550/ARXIV.2204.06447.
short: M. Schlichtig, A.-K. Wickert, S. Krüger, E. Bodden, M. Mezini, CamBench --
Cryptographic API Misuse Detection Tool Benchmark Suite, 2022.
date_created: 2022-07-25T07:56:59Z
date_updated: 2022-07-25T10:23:44Z
department:
- _id: '76'
doi: 10.48550/ARXIV.2204.06447
keyword:
- cryptography
- benchmark
- API misuse
- static analysis
language:
- iso: eng
related_material:
link:
- relation: confirmation
url: https://arxiv.org/abs/2204.06447
status: public
title: CamBench -- Cryptographic API Misuse Detection Tool Benchmark Suite
type: misc
user_id: '32312'
year: '2022'
...
---
_id: '32410'
abstract:
- lang: eng
text: "Static analysis tools support developers in detecting potential coding issues,
such as bugs or vulnerabilities. Research on static analysis emphasizes its technical
challenges but also mentions severe usability shortcomings. These shortcomings
hinder the adoption of static analysis tools, and in some cases, user dissatisfaction
even leads to tool abandonment.\r\nTo comprehensively assess the current state
of the art, this paper presents the first systematic usability evaluation in a
wide range of static analysis tools. We derived a set of 36 relevant criteria
from the scientific literature and gathered a collection of 46 static analysis
tools complying with our inclusion and exclusion criteria - a representative set
of mainly non-proprietary tools. Then, we evaluated how well these tools fulfill
the aforementioned criteria.\r\nThe evaluation shows that more than half of the
considered tools offer poor warning messages, while about three-quarters of the
tools provide hardly any fix support. Furthermore, the integration of user knowledge
is strongly neglected, which could be used for improved handling of false positives
and tuning the results for the corresponding developer. Finally, issues regarding
workflow integration and specialized user interfaces are proved further.\r\nThese
findings should prove useful in guiding and focusing further research and development
in the area of user experience for static code analyses."
author:
- first_name: Marcus
full_name: Nachtigall, Marcus
id: '41213'
last_name: Nachtigall
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Nachtigall M, Schlichtig M, Bodden E. A Large-Scale Study of Usability Criteria
Addressed by Static Analysis Tools. In: Proceedings of the 31st ACM SIGSOFT
International Symposium on Software Testing and Analysis. ACM; 2022:532-543.
doi:10.1145/3533767'
apa: Nachtigall, M., Schlichtig, M., & Bodden, E. (2022). A Large-Scale Study
of Usability Criteria Addressed by Static Analysis Tools. Proceedings of the
31st ACM SIGSOFT International Symposium on Software Testing and Analysis,
532–543. https://doi.org/10.1145/3533767
bibtex: '@inproceedings{Nachtigall_Schlichtig_Bodden_2022, title={A Large-Scale
Study of Usability Criteria Addressed by Static Analysis Tools}, DOI={10.1145/3533767},
booktitle={Proceedings of the 31st ACM SIGSOFT International Symposium on Software
Testing and Analysis}, publisher={ACM}, author={Nachtigall, Marcus and Schlichtig,
Michael and Bodden, Eric}, year={2022}, pages={532–543} }'
chicago: Nachtigall, Marcus, Michael Schlichtig, and Eric Bodden. “A Large-Scale
Study of Usability Criteria Addressed by Static Analysis Tools.” In Proceedings
of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis,
532–43. ACM, 2022. https://doi.org/10.1145/3533767.
ieee: 'M. Nachtigall, M. Schlichtig, and E. Bodden, “A Large-Scale Study of Usability
Criteria Addressed by Static Analysis Tools,” in Proceedings of the 31st ACM
SIGSOFT International Symposium on Software Testing and Analysis, 2022, pp.
532–543, doi: 10.1145/3533767.'
mla: Nachtigall, Marcus, et al. “A Large-Scale Study of Usability Criteria Addressed
by Static Analysis Tools.” Proceedings of the 31st ACM SIGSOFT International
Symposium on Software Testing and Analysis, ACM, 2022, pp. 532–43, doi:10.1145/3533767.
short: 'M. Nachtigall, M. Schlichtig, E. Bodden, in: Proceedings of the 31st ACM
SIGSOFT International Symposium on Software Testing and Analysis, ACM, 2022, pp.
532–543.'
date_created: 2022-07-25T08:02:36Z
date_updated: 2022-07-26T11:42:23Z
department:
- _id: '76'
doi: 10.1145/3533767
keyword:
- Automated static analysis
- Software usability
language:
- iso: eng
page: 532 - 543
publication: Proceedings of the 31st ACM SIGSOFT International Symposium on Software
Testing and Analysis
publication_identifier:
isbn:
- '9781450393799'
publication_status: published
publisher: ACM
quality_controlled: '1'
related_material:
link:
- relation: confirmation
url: https://dl.acm.org/doi/10.1145/3533767.3534374
status: public
title: A Large-Scale Study of Usability Criteria Addressed by Static Analysis Tools
type: conference
user_id: '32312'
year: '2022'
...
---
_id: '31133'
abstract:
- lang: eng
text: Application Programming Interfaces (APIs) are the primary mechanism that developers
use to obtain access to third-party algorithms and services. Unfortunately, APIs
can be misused, which can have catastrophic consequences, especially if the APIs
provide security-critical functionalities like cryptography. Understanding what
API misuses are, and for what reasons they are caused, is important to prevent
them, e.g., with API misuse detectors. However, definitions and nominations for
API misuses and related terms in literature vary and are diverse. This paper addresses
the problem of scattered knowledge and definitions of API misuses by presenting
a systematic literature review on the subject and introducing FUM, a novel Framework
for API Usage constraint and Misuse classification. The literature review revealed
that API misuses are violations of API usage constraints. To capture this, we
provide unified definitions and use them to derive FUM. To assess the extent to
which FUM aids in determining and guiding the improvement of an API misuses detectors'
capabilities, we performed a case study on CogniCrypt, a state-of-the-art misuse
detector for cryptographic APIs. The study showed that FUM can be used to properly
assess CogniCrypt's capabilities, identify weaknesses and assist in deriving mitigations
and improvements. And it appears that also more generally FUM can aid the development
and improvement of misuse detection tools.
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Steffen
full_name: Sassalla, Steffen
last_name: Sassalla
- first_name: Krishna
full_name: Narasimhan, Krishna
last_name: Narasimhan
- first_name: Eric
full_name: Bodden, Eric
id: '59256'
last_name: Bodden
orcid: 0000-0003-3470-3647
citation:
ama: 'Schlichtig M, Sassalla S, Narasimhan K, Bodden E. FUM - A Framework for API
Usage constraint and Misuse Classification. In: 2022 IEEE International Conference
on Software Analysis, Evolution and Reengineering (SANER). ; 2022:673-684.
doi:https://doi.org/10.1109/SANER53432.2022.00085'
apa: Schlichtig, M., Sassalla, S., Narasimhan, K., & Bodden, E. (2022). FUM
- A Framework for API Usage constraint and Misuse Classification. 2022 IEEE
International Conference on Software Analysis, Evolution and Reengineering (SANER),
673–684. https://doi.org/10.1109/SANER53432.2022.00085
bibtex: '@inproceedings{Schlichtig_Sassalla_Narasimhan_Bodden_2022, title={FUM -
A Framework for API Usage constraint and Misuse Classification}, DOI={https://doi.org/10.1109/SANER53432.2022.00085},
booktitle={2022 IEEE International Conference on Software Analysis, Evolution
and Reengineering (SANER)}, author={Schlichtig, Michael and Sassalla, Steffen
and Narasimhan, Krishna and Bodden, Eric}, year={2022}, pages={673–684} }'
chicago: Schlichtig, Michael, Steffen Sassalla, Krishna Narasimhan, and Eric Bodden.
“FUM - A Framework for API Usage Constraint and Misuse Classification.” In 2022
IEEE International Conference on Software Analysis, Evolution and Reengineering
(SANER), 673–84, 2022. https://doi.org/10.1109/SANER53432.2022.00085.
ieee: 'M. Schlichtig, S. Sassalla, K. Narasimhan, and E. Bodden, “FUM - A Framework
for API Usage constraint and Misuse Classification,” in 2022 IEEE International
Conference on Software Analysis, Evolution and Reengineering (SANER), 2022,
pp. 673–684, doi: https://doi.org/10.1109/SANER53432.2022.00085.'
mla: Schlichtig, Michael, et al. “FUM - A Framework for API Usage Constraint and
Misuse Classification.” 2022 IEEE International Conference on Software Analysis,
Evolution and Reengineering (SANER), 2022, pp. 673–84, doi:https://doi.org/10.1109/SANER53432.2022.00085.
short: 'M. Schlichtig, S. Sassalla, K. Narasimhan, E. Bodden, in: 2022 IEEE International
Conference on Software Analysis, Evolution and Reengineering (SANER), 2022, pp.
673–684.'
date_created: 2022-05-09T13:04:10Z
date_updated: 2022-07-26T11:42:30Z
department:
- _id: '76'
doi: https://doi.org/10.1109/SANER53432.2022.00085
keyword:
- API misuses
- API usage constraints
- classification framework
- API misuse detection
- static analysis
language:
- iso: eng
page: 673 - 684
publication: 2022 IEEE International Conference on Software Analysis, Evolution and
Reengineering (SANER)
quality_controlled: '1'
related_material:
link:
- relation: confirmation
url: https://ieeexplore.ieee.org/document/9825763
status: public
title: FUM - A Framework for API Usage constraint and Misuse Classification
type: conference
user_id: '32312'
year: '2022'
...
---
_id: '33959'
abstract:
- lang: eng
text: Recent studies have revealed that 87 % to 96 % of the Android apps using cryptographic
APIs have a misuse which may cause security vulnerabilities. As previous studies
did not conduct a qualitative examination of the validity and severity of the
findings, our objective was to understand the findings in more depth. We analyzed
a set of 936 open-source Java applications for cryptographic misuses. Our study
reveals that 88.10 % of the analyzed applications fail to use cryptographic APIs
securely. Through our manual analysis of a random sample, we gained new insights
into effective false positives. For example, every fourth misuse of the frequently
misused JCA class MessageDigest is an effective false positive due to its occurrence
in a non-security context. As we wanted to gain deeper insights into the security
implications of these misuses, we created an extensive vulnerability model for
cryptographic API misuses. Our model includes previously undiscussed attacks in
the context of cryptographic APIs such as DoS attacks. This model reveals that
nearly half of the misuses are of high severity, e.g., hard-coded credentials
and potential Man-in-the-Middle attacks.
author:
- first_name: Anna-Katharina
full_name: Wickert, Anna-Katharina
last_name: Wickert
- first_name: Lars
full_name: Baumgärtner, Lars
last_name: Baumgärtner
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Mira
full_name: Mezini, Mira
last_name: Mezini
citation:
ama: 'Wickert A-K, Baumgärtner L, Schlichtig M, Mezini M. To Fix or Not to Fix:
A Critical Study of Crypto-Misuses in the Wild.; 2022. doi:10.48550/ARXIV.2209.11103'
apa: 'Wickert, A.-K., Baumgärtner, L., Schlichtig, M., & Mezini, M. (2022).
To Fix or Not to Fix: A Critical Study of Crypto-misuses in the Wild. https://doi.org/10.48550/ARXIV.2209.11103'
bibtex: '@book{Wickert_Baumgärtner_Schlichtig_Mezini_2022, title={To Fix or Not
to Fix: A Critical Study of Crypto-misuses in the Wild}, DOI={10.48550/ARXIV.2209.11103},
author={Wickert, Anna-Katharina and Baumgärtner, Lars and Schlichtig, Michael
and Mezini, Mira}, year={2022} }'
chicago: 'Wickert, Anna-Katharina, Lars Baumgärtner, Michael Schlichtig, and Mira
Mezini. To Fix or Not to Fix: A Critical Study of Crypto-Misuses in the Wild,
2022. https://doi.org/10.48550/ARXIV.2209.11103.'
ieee: 'A.-K. Wickert, L. Baumgärtner, M. Schlichtig, and M. Mezini, To Fix or
Not to Fix: A Critical Study of Crypto-misuses in the Wild. 2022.'
mla: 'Wickert, Anna-Katharina, et al. To Fix or Not to Fix: A Critical Study
of Crypto-Misuses in the Wild. 2022, doi:10.48550/ARXIV.2209.11103.'
short: 'A.-K. Wickert, L. Baumgärtner, M. Schlichtig, M. Mezini, To Fix or Not to
Fix: A Critical Study of Crypto-Misuses in the Wild, 2022.'
date_created: 2022-10-28T13:21:05Z
date_updated: 2022-10-28T13:26:39Z
department:
- _id: '76'
doi: 10.48550/ARXIV.2209.11103
language:
- iso: eng
related_material:
link:
- relation: confirmation
url: https://arxiv.org/abs/2209.11103
status: public
title: 'To Fix or Not to Fix: A Critical Study of Crypto-misuses in the Wild'
type: misc
user_id: '32312'
year: '2022'
...
---
_id: '29298'
abstract:
- lang: ger
text: "Die Themen „Big Data“, „Künstliche Intelligenz und „Data Science“ werden
seit einiger Zeit nicht nur in der breiten Öffentlichkeit kontrovers diskutiert,
sondern stellen für die Ausbildung in den IT- und IT-nahen Berufen schon heute
neue Herausforderungen dar, die in Zukunft durch die gesellschaftliche und technologische
Weiterentwicklung hin zu einer Datengesellschaft noch größer werden.\r\nAn dieser
Stelle stellt sich die Frage, welche Aspekte dieses großen Themenkomplexes für
Schule und Ausbildung von Wichtigkeit sind und wie diese Themen sinnstiftend und
gewinnbringend in die informatische Ausbildung in verschiedenen Bildungsgängen
integriert werden können. Im Rahmen des von uns im Jahr 2017 organisierten Symposiums
zum Thema „Data Science“ wurden für die Bildung relevante Aspekte erörtert, wodurch
als Kernelemente für den Unterricht Algorithmen der Künstlichen Intelligenz und
ihre Anwendung in Industrie und Gesellschaft, Explorationen von Big Data sowie
der Umgang mit eigenen Daten in sozialen Netzwerken herausgearbeitet wurden. Ziel
ist, aus diesen Themenbereichen sowohl ein umfassendes Curriculum als auch Module
für verschiedene Unterrichtsszenarien zu entwickeln und zu erproben. Durch diese
Materialien soll es Lehrkräften aus der Informatik, Mathematik oder Technik ermöglicht
werden, diese Themen auf Basis des Curriculums und der erprobten Unterrichtskonzepte
selbst zu unterrichten.\r\nHierfür wurde im Rahmen des Projekts ProDaBi (Projekt
Data Science und Big Data in der Schule, https://www.prodabi.de), initiiert von
der Telekom Stiftung, ein experimenteller Projektkurs entwickelt, den wir mit
Schüler:innen der Sekundarstufe II an der Universität Paderborn im Schuljahr 2018/19
durchführten. Dieser Kurs enthält neben einem Modul zur Exploration von Big Data
und einem weiteren Modul zum Maschinellen Lernen als Teil der Künstlichen Intelligenz
auch eine Projektphase, die es in Zusammenarbeit mit lokalen Unternehmen den Schüler:innen\r\nermöglicht,
das Erlernte in ein reales Data Science-Projekt einzubringen. Aus den Erfahrungen
dieses Projektkurses sowie den parallel durchgeführten Erprobungen einzelner Bausteine
auch mit beruflichen Schulen werden ab dem Schuljahr 2019/20 die hierfür verwendeten
Materialien weiterentwickelt und weiteren Kooperationspartnern zur Erprobung zur
Verfügung gestellt. Damit wurden zum Ende des Projekts nicht nur vollständige
Unterrichtsmaterialien, sondern auch ein umfassendes Curriculum entwickelt."
- lang: eng
text: "The topics ”Big Data”, “Artificial Intelligence” and “Data Science” are controversially
discussed among the general public, but they present new challenges for training
in IT and IT-related professions. These challenges will become more important
in the future as a result of further social and technological development towards
a data society.\r\nAt this point, the question arises as to which aspects of this
large complex of topics are important for school and education, and how these
topics can be integrated in a meaningful and profitable way into informatics education
in vocational education. In 2017, we organized a symposium towards the topic “Data
Science” and discussed relevant aspects for general and vocational education.
Algorithms of artificial intelligence and their application in industry and society,
explorations of Big Data as well as the handling of one's own data in social networks
were worked out as core elements for teaching. For this reason, our aim is to
develop a comprehensive curriculum on this topic from these subject areas and
to develop and test modules for various teaching scenarios in order to enable
teachers from computer science, mathematics or technology to teach these topics
themselves.\r\nFor this purpose, an experimental project course was developed
within the framework of the ProDaBi project (Project Data Science and Big Data
at School, https://www.prodabi.de), which we conducted with students from upper
secondary classes at the University of Paderborn in the school year 2018/19. In
this course we try to address all these aspects. This course consists of several
modules: One module has been designed to teach the exploration of Big Data. Another
module encompasses aspects of machine learning as part of artificial intelligence.
The course concludes in a project phase which, in cooperation with local companies,
will enable the students to apply what they have learned into a real Data Science
project. Based on the experiences of this project course and the parallel testing
of individual modules with vocational schools, we will further develop the material
and make it available to other cooperation partners for testing, so that not only
complete teaching materials but also a comprehensive curriculum will have been
developed until the end of the project."
author:
- first_name: Simone Anna
full_name: Opel, Simone Anna
id: '72932'
last_name: Opel
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
citation:
ama: 'Opel SA, Schlichtig M. Data Science und Big Data in der beruflichen Bildung
– Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II. In: Vollmer
T, Karges T, Richter T, Schlömer B, Schütt-Sayed S, eds. Sammelband der 27.
Fachtagung der BAG Berufliche Bildung. Vol 55. Berufsbildung, Arbeit und Innovation.
wbv Media GmbH & Co. KG; 2020:176-194. doi:https://doi.org/10.3278/6004722w'
apa: Opel, S. A., & Schlichtig, M. (2020). Data Science und Big Data in der
beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe
II. In T. Vollmer, T. Karges, T. Richter, B. Schlömer, & S. Schütt-Sayed (Eds.),
Sammelband der 27. Fachtagung der BAG Berufliche Bildung (Vol. 55, pp.
176–194). wbv Media GmbH & Co. KG. https://doi.org/10.3278/6004722w
bibtex: '@inproceedings{Opel_Schlichtig_2020, place={Bielefeld}, series={Berufsbildung,
Arbeit und Innovation}, title={Data Science und Big Data in der beruflichen Bildung
– Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II}, volume={55},
DOI={https://doi.org/10.3278/6004722w},
booktitle={Sammelband der 27. Fachtagung der BAG Berufliche Bildung}, publisher={wbv
Media GmbH & Co. KG}, author={Opel, Simone Anna and Schlichtig, Michael},
editor={Vollmer, Thomas and Karges, Torben and Richter, Tim and Schlömer, Britta
and Schütt-Sayed, Sören}, year={2020}, pages={176–194}, collection={Berufsbildung,
Arbeit und Innovation} }'
chicago: 'Opel, Simone Anna, and Michael Schlichtig. “Data Science und Big Data
in der beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für
die Sekundarstufe II.” In Sammelband der 27. Fachtagung der BAG Berufliche
Bildung, edited by Thomas Vollmer, Torben Karges, Tim Richter, Britta Schlömer,
and Sören Schütt-Sayed, 55:176–94. Berufsbildung, Arbeit und Innovation. Bielefeld:
wbv Media GmbH & Co. KG, 2020. https://doi.org/10.3278/6004722w.'
ieee: 'S. A. Opel and M. Schlichtig, “Data Science und Big Data in der beruflichen
Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II,”
in Sammelband der 27. Fachtagung der BAG Berufliche Bildung, Siegen, 2020,
vol. 55, pp. 176–194, doi: https://doi.org/10.3278/6004722w.'
mla: Opel, Simone Anna, and Michael Schlichtig. “Data Science und Big Data in der
beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe
II.” Sammelband der 27. Fachtagung der BAG Berufliche Bildung, edited by
Thomas Vollmer et al., vol. 55, wbv Media GmbH & Co. KG, 2020, pp. 176–94,
doi:https://doi.org/10.3278/6004722w.
short: 'S.A. Opel, M. Schlichtig, in: T. Vollmer, T. Karges, T. Richter, B. Schlömer,
S. Schütt-Sayed (Eds.), Sammelband der 27. Fachtagung der BAG Berufliche Bildung,
wbv Media GmbH & Co. KG, Bielefeld, 2020, pp. 176–194.'
conference:
end_date: 2019-03-13
location: Siegen
name: 20. Hochschultage Berufliche Bildung (HTBB) "Digitale Welt - Bildung und Arbeit
in Transformationsgesellschaften".
start_date: 2019-03-11
date_created: 2022-01-12T16:43:38Z
date_updated: 2022-01-12T17:04:10Z
department:
- _id: '67'
doi: https://doi.org/10.3278/6004722w
editor:
- first_name: Thomas
full_name: Vollmer, Thomas
last_name: Vollmer
- first_name: Torben
full_name: Karges, Torben
last_name: Karges
- first_name: Tim
full_name: Richter, Tim
last_name: Richter
- first_name: Britta
full_name: Schlömer, Britta
last_name: Schlömer
- first_name: Sören
full_name: Schütt-Sayed, Sören
last_name: Schütt-Sayed
intvolume: ' 55'
keyword:
- Berufsbildung
- vocational education
- Ausbildung
- training
- berufliche Weiterbildung
- advanced vocational education
- Digitalisierung
- digitalization
- Unterricht
- teaching
- Lehrmethode
- teaching method
- Interdisziplinarität
- interdisciplinarity
- Fachdidaktik
- subject didactics
- Curriculum
- curriculum
- gewerblich-technischer Beruf
- vocational/technical occupation
- Fachkraft
- specialist
- Qualifikationsanforderungen
- qualification requirements
- Kompetenz
- competence
- Lehrerbildung
- teacher training
- Bundesrepublik Deutschland
- Federal Republic of Germany
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://library.oapen.org/handle/20.500.12657/43933
oa: '1'
page: 176-194
place: Bielefeld
publication: Sammelband der 27. Fachtagung der BAG Berufliche Bildung
publication_status: published
publisher: wbv Media GmbH & Co. KG
series_title: Berufsbildung, Arbeit und Innovation
status: public
title: Data Science und Big Data in der beruflichen Bildung – Konzeption und Erprobung
eines Projektkurses für die Sekundarstufe II
type: conference
user_id: '32312'
volume: 55
year: '2020'
...
---
_id: '15332'
abstract:
- lang: eng
text: "Artificial intelligence (AI) has the potential for far-reaching – in our
opinion – irreversible changes.\r\nThey range from effects on the individual and
society to new societal and social issues. The question arises\r\nas to how students
can learn the basic functioning of AI systems, what areas of life and society
are affected\r\nby these and – most important – how their own lives are affected
by these changes. Therefore, we are developing and evaluating school materials
for the German ”Science Year AI”. It can be used for students of all\r\nschool
types from the seventh grade upwards and will be distributed to about 2000 schools
in autumn with\r\nthe support of the Federal Ministry of Education and Research.
The material deals with the following aspects\r\nof AI: Discussing everyday experiences
with AI, how does machine learning work, historical development\r\nof AI concepts,
difference between man and machine, future distribution of roles between man and
machine,\r\nin which AI world do we want to live and how much AI would we like
to have in our lives. Through an\r\naccompanying evaluation, high quality of the
technical content and didactic preparation is achieved in order\r\nto guarantee
the long-term applicability in the teaching context in the different age groups
and school types.\r\nIn this paper, we describe the current state of the material
development, the challenges arising, and the results\r\nof tests with different
classes to date. We also present first ideas for evaluating the results."
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Simone Anna
full_name: Opel, Simone Anna
id: '72932'
last_name: Opel
- first_name: Lea
full_name: Budde, Lea
id: '32443'
last_name: Budde
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
citation:
ama: 'Schlichtig M, Opel SA, Budde L, Schulte C. Understanding Artificial Intelligence
– A Project for the Development of Comprehensive Teaching Material. In: Jasutė
E, Pozdniakov S, eds. ISSEP 2019 - 12th International Conference on Informatics
in Schools: Situation, Evaluation and Perspectives, Local Proceedings. Vol
12. ; 2019:65-73.'
apa: 'Schlichtig, M., Opel, S. A., Budde, L., & Schulte, C. (2019). Understanding
Artificial Intelligence – A Project for the Development of Comprehensive Teaching
Material. In E. Jasutė & S. Pozdniakov (Eds.), ISSEP 2019 - 12th International
conference on informatics in schools: Situation, evaluation and perspectives,
Local Proceedings (Vol. 12, pp. 65–73).'
bibtex: '@inproceedings{Schlichtig_Opel_Budde_Schulte_2019, title={Understanding
Artificial Intelligence – A Project for the Development of Comprehensive Teaching
Material}, volume={12}, booktitle={ISSEP 2019 - 12th International conference
on informatics in schools: Situation, evaluation and perspectives, Local Proceedings},
author={Schlichtig, Michael and Opel, Simone Anna and Budde, Lea and Schulte,
Carsten}, editor={Jasutė, Eglė and Pozdniakov, Sergei}, year={2019}, pages={65–73}
}'
chicago: 'Schlichtig, Michael, Simone Anna Opel, Lea Budde, and Carsten Schulte.
“Understanding Artificial Intelligence – A Project for the Development of Comprehensive
Teaching Material.” In ISSEP 2019 - 12th International Conference on Informatics
in Schools: Situation, Evaluation and Perspectives, Local Proceedings, edited
by Eglė Jasutė and Sergei Pozdniakov, 12:65–73, 2019.'
ieee: 'M. Schlichtig, S. A. Opel, L. Budde, and C. Schulte, “Understanding Artificial
Intelligence – A Project for the Development of Comprehensive Teaching Material,”
in ISSEP 2019 - 12th International conference on informatics in schools: Situation,
evaluation and perspectives, Local Proceedings, Lanarca, 2019, vol. 12, pp.
65–73.'
mla: 'Schlichtig, Michael, et al. “Understanding Artificial Intelligence – A Project
for the Development of Comprehensive Teaching Material.” ISSEP 2019 - 12th
International Conference on Informatics in Schools: Situation, Evaluation and
Perspectives, Local Proceedings, edited by Eglė Jasutė and Sergei Pozdniakov,
vol. 12, 2019, pp. 65–73.'
short: 'M. Schlichtig, S.A. Opel, L. Budde, C. Schulte, in: E. Jasutė, S. Pozdniakov
(Eds.), ISSEP 2019 - 12th International Conference on Informatics in Schools:
Situation, Evaluation and Perspectives, Local Proceedings, 2019, pp. 65–73.'
conference:
end_date: 2019-11-20
location: Lanarca
name: 'ISSEP 2019 - 12th International conference on informatics in schools: Situation,
evaluation and perspectives'
start_date: 2019-11-18
date_created: 2019-12-16T17:50:08Z
date_updated: 2022-07-26T11:41:41Z
department:
- _id: '67'
editor:
- first_name: Eglė
full_name: Jasutė, Eglė
last_name: Jasutė
- first_name: Sergei
full_name: Pozdniakov, Sergei
last_name: Pozdniakov
intvolume: ' 12'
keyword:
- Artificial Intelligence
- Machine Learning
- Teaching Material
- Societal Aspects
- Ethics. Social Aspects
- Science Year
- Simulation Game
language:
- iso: eng
main_file_link:
- url: http://cyprusconferences.org/issep2019/wp-content/uploads/2019/10/LocalISSEP-v5.pdf
page: 65 - 73
publication: 'ISSEP 2019 - 12th International conference on informatics in schools:
Situation, evaluation and perspectives, Local Proceedings'
publication_identifier:
isbn:
- 978-9925-553-27-3
publication_status: published
quality_controlled: '1'
status: public
title: Understanding Artificial Intelligence – A Project for the Development of Comprehensive
Teaching Material
type: conference
user_id: '32312'
volume: 12
year: '2019'
...
---
_id: '15640'
author:
- first_name: Simone Anna
full_name: Opel, Simone Anna
id: '72932'
last_name: Opel
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
- first_name: Rolf
full_name: Biehler, Rolf
last_name: Biehler
- first_name: Daniel
full_name: Frischemeier, Daniel
last_name: Frischemeier
- first_name: Susanne
full_name: Podworny, Susanne
last_name: Podworny
- first_name: Thomas
full_name: Wassong, Thomas
id: '21241'
last_name: Wassong
citation:
ama: 'Opel SA, Schlichtig M, Schulte C, et al. Entwicklung und Reflexion einer Unterrichtssequenz
zum Maschinellen Lernen als Aspekt von Data Science in der Sekundarstufe II. In:
INFOS. Vol P-288. LNI. Gesellschaft für Informatik; 2019:285-294.'
apa: Opel, S. A., Schlichtig, M., Schulte, C., Biehler, R., Frischemeier, D., Podworny,
S., & Wassong, T. (2019). Entwicklung und Reflexion einer Unterrichtssequenz
zum Maschinellen Lernen als Aspekt von Data Science in der Sekundarstufe II. INFOS,
P-288, 285–294.
bibtex: '@inproceedings{Opel_Schlichtig_Schulte_Biehler_Frischemeier_Podworny_Wassong_2019,
series={LNI}, title={Entwicklung und Reflexion einer Unterrichtssequenz zum Maschinellen
Lernen als Aspekt von Data Science in der Sekundarstufe II}, volume={P-288}, booktitle={INFOS},
publisher={Gesellschaft für Informatik}, author={Opel, Simone Anna and Schlichtig,
Michael and Schulte, Carsten and Biehler, Rolf and Frischemeier, Daniel and Podworny,
Susanne and Wassong, Thomas}, year={2019}, pages={285–294}, collection={LNI} }'
chicago: Opel, Simone Anna, Michael Schlichtig, Carsten Schulte, Rolf Biehler, Daniel
Frischemeier, Susanne Podworny, and Thomas Wassong. “Entwicklung und Reflexion
einer Unterrichtssequenz zum Maschinellen Lernen als Aspekt von Data Science in
der Sekundarstufe II.” In INFOS, P-288:285–94. LNI. Gesellschaft für Informatik,
2019.
ieee: S. A. Opel et al., “Entwicklung und Reflexion einer Unterrichtssequenz
zum Maschinellen Lernen als Aspekt von Data Science in der Sekundarstufe II,”
in INFOS, 2019, vol. P-288, pp. 285–294.
mla: Opel, Simone Anna, et al. “Entwicklung und Reflexion einer Unterrichtssequenz
zum Maschinellen Lernen als Aspekt von Data Science in der Sekundarstufe II.”
INFOS, vol. P-288, Gesellschaft für Informatik, 2019, pp. 285–94.
short: 'S.A. Opel, M. Schlichtig, C. Schulte, R. Biehler, D. Frischemeier, S. Podworny,
T. Wassong, in: INFOS, Gesellschaft für Informatik, 2019, pp. 285–294.'
date_created: 2020-01-28T10:28:34Z
date_updated: 2022-07-26T11:42:05Z
department:
- _id: '67'
language:
- iso: ger
page: 285-294
publication: INFOS
publisher: Gesellschaft für Informatik
quality_controlled: '1'
series_title: LNI
status: public
title: Entwicklung und Reflexion einer Unterrichtssequenz zum Maschinellen Lernen
als Aspekt von Data Science in der Sekundarstufe II
type: conference
user_id: '32312'
volume: P-288
year: '2019'
...
---
_id: '15641'
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Simone Anna
full_name: Opel, Simone Anna
id: '72932'
last_name: Opel
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
- first_name: Rolf
full_name: Biehler, Rolf
last_name: Biehler
- first_name: Daniel
full_name: Frischemeier, Daniel
last_name: Frischemeier
- first_name: Susanne
full_name: Podworny, Susanne
last_name: Podworny
- first_name: Thomas
full_name: Wassong, Thomas
id: '21241'
last_name: Wassong
citation:
ama: 'Schlichtig M, Opel SA, Schulte C, et al. Maschinelles Lernen im Unterricht
mit Jupyter Notebook. In: INFOS. Vol P-288. LNI. Gesellschaft für Informatik;
2019:385.'
apa: Schlichtig, M., Opel, S. A., Schulte, C., Biehler, R., Frischemeier, D., Podworny,
S., & Wassong, T. (2019). Maschinelles Lernen im Unterricht mit Jupyter Notebook.
INFOS, P-288, 385.
bibtex: '@inproceedings{Schlichtig_Opel_Schulte_Biehler_Frischemeier_Podworny_Wassong_2019,
series={LNI}, title={Maschinelles Lernen im Unterricht mit Jupyter Notebook},
volume={P-288}, booktitle={INFOS}, publisher={Gesellschaft für Informatik}, author={Schlichtig,
Michael and Opel, Simone Anna and Schulte, Carsten and Biehler, Rolf and Frischemeier,
Daniel and Podworny, Susanne and Wassong, Thomas}, year={2019}, pages={385}, collection={LNI}
}'
chicago: Schlichtig, Michael, Simone Anna Opel, Carsten Schulte, Rolf Biehler, Daniel
Frischemeier, Susanne Podworny, and Thomas Wassong. “Maschinelles Lernen im Unterricht
mit Jupyter Notebook.” In INFOS, P-288:385. LNI. Gesellschaft für Informatik,
2019.
ieee: M. Schlichtig et al., “Maschinelles Lernen im Unterricht mit Jupyter
Notebook,” in INFOS, 2019, vol. P-288, p. 385.
mla: Schlichtig, Michael, et al. “Maschinelles Lernen im Unterricht mit Jupyter
Notebook.” INFOS, vol. P-288, Gesellschaft für Informatik, 2019, p. 385.
short: 'M. Schlichtig, S.A. Opel, C. Schulte, R. Biehler, D. Frischemeier, S. Podworny,
T. Wassong, in: INFOS, Gesellschaft für Informatik, 2019, p. 385.'
date_created: 2020-01-28T10:28:35Z
date_updated: 2022-07-26T11:41:58Z
department:
- _id: '67'
language:
- iso: ger
page: '385'
publication: INFOS
publisher: Gesellschaft für Informatik
quality_controlled: '1'
series_title: LNI
status: public
title: Maschinelles Lernen im Unterricht mit Jupyter Notebook
type: conference
user_id: '32312'
volume: P-288
year: '2019'
...
---
_id: '15643'
author:
- first_name: Simone Anna
full_name: Opel, Simone Anna
id: '72932'
last_name: Opel
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
citation:
ama: 'Opel SA, Schlichtig M, Schulte C. Developing Teaching Materials on Artificial
Intelligence by Using a Simulation Game (Work in Progress). In: WiPSCE.
ACM; 2019:11:1-11:2.'
apa: Opel, S. A., Schlichtig, M., & Schulte, C. (2019). Developing Teaching
Materials on Artificial Intelligence by Using a Simulation Game (Work in Progress).
WiPSCE, 11:1-11:2.
bibtex: '@inproceedings{Opel_Schlichtig_Schulte_2019, title={Developing Teaching
Materials on Artificial Intelligence by Using a Simulation Game (Work in Progress)},
booktitle={WiPSCE}, publisher={ACM}, author={Opel, Simone Anna and Schlichtig,
Michael and Schulte, Carsten}, year={2019}, pages={11:1-11:2} }'
chicago: Opel, Simone Anna, Michael Schlichtig, and Carsten Schulte. “Developing
Teaching Materials on Artificial Intelligence by Using a Simulation Game (Work
in Progress).” In WiPSCE, 11:1-11:2. ACM, 2019.
ieee: S. A. Opel, M. Schlichtig, and C. Schulte, “Developing Teaching Materials
on Artificial Intelligence by Using a Simulation Game (Work in Progress),” in
WiPSCE, 2019, p. 11:1-11:2.
mla: Opel, Simone Anna, et al. “Developing Teaching Materials on Artificial Intelligence
by Using a Simulation Game (Work in Progress).” WiPSCE, ACM, 2019, p. 11:1-11:2.
short: 'S.A. Opel, M. Schlichtig, C. Schulte, in: WiPSCE, ACM, 2019, p. 11:1-11:2.'
date_created: 2020-01-28T10:28:37Z
date_updated: 2022-07-26T11:41:51Z
department:
- _id: '67'
language:
- iso: eng
page: 11:1-11:2
publication: WiPSCE
publisher: ACM
quality_controlled: '1'
status: public
title: Developing Teaching Materials on Artificial Intelligence by Using a Simulation
Game (Work in Progress)
type: conference
user_id: '32312'
year: '2019'
...
---
_id: '14848'
abstract:
- lang: ger
text: Data Science und Big Data durchdringt in ihren diversen Facetten unser tägliches
Leben– kaum ein Tag, an dem nicht verschiedene Meldungen über technische Innovationen,
Einsatzmöglichkeiten von Künstlicher Intelligenz (KI) und Maschinelles Lernen
(ML) und ihre ethischen sowie gesellschaftlichen Implikationen in den unterschiedlichen
Medien diskutiert werden. Aus diesem Grund erscheint es uns immens wichtig, diese
Fragestellungen und Technologien auch in den Unterricht der Sekundarstufe II zu
integrieren. Um diesem Anspruch gerecht zu werden, entwickelten wir im Rahmen
eines Forschungsprojekts ein Curriculum, welches wir als konkretes Unterrichtskonzept
innerhalb eines Projektkurses erprobt, evaluiert weiterentwickelt wird. Bei der
Implementierung entschieden wir uns, zur aktiven Umsetzung von Konzepten von ML
als Plattform Jupyter Notebook mit Python zu verwenden, da diese Umgebung durch
die Verbindung von Code und Hypertext zur Dokumentation und Erklärung Medienbrüche
im Lernprozess verringern kann. Zudem ist Python zur Implementierung der Methoden
von ML sehr gut geeignet. Im Themenfeld des ML als Teilgebiet der KI legen wir
den Fokus auf zwei unterschiedliche Lernverfahren um verschieden Aspekte von ML,
u.A. wie Nachvollziehbarkeit unter gesellschaftlichen Gesichtspunkten zu vermitteln.
Diese sind Künstliche Neuronale Netze (bei denen die Berechnung und Bedeutung
der Kantengewichte zwischen den Neuronen für den Menschen insbesondere bei komplexeren
Netzen kaum nachvollziehbar erschienen) und Entscheidungsbäume (strukturierte
und gerichtete Bäume zur Darstellung von Entscheidungsregeln, welche auch für
Schülerinnen und Schüler meist gut nachvollziehbares und verständliches KI-Modell
darstellen). In diesem Workshop stellen wir konkrete Umsetzungsbeispiele inklusive
der Programmierung für beide Verfahren mit Jupyter Notebook und Python als Teil
einer Unterrichtssequenz vor und diskutieren diese.
author:
- first_name: Michael
full_name: Schlichtig, Michael
id: '32312'
last_name: Schlichtig
orcid: 0000-0001-6600-6171
- first_name: Simone
full_name: Opel, Simone
last_name: Opel
- first_name: Carsten
full_name: Schulte, Carsten
last_name: Schulte
- first_name: Rolf
full_name: Biehler, Rolf
last_name: Biehler
- first_name: Daniel
full_name: Frischemeier, Daniel
last_name: Frischemeier
- first_name: Susanne
full_name: Podworny, Susanne
last_name: Podworny
- first_name: Thomas
full_name: Wassong, Thomas
id: '21241'
last_name: Wassong
citation:
ama: 'Schlichtig M, Opel S, Schulte C, et al. Maschinelles Lernen im Unterricht
mit Jupyter Notebook. In: Pasternak A, ed. Informatik für alle. Gesellschaft
für Informatik; 2019:385.'
apa: Schlichtig, M., Opel, S., Schulte, C., Biehler, R., Frischemeier, D., Podworny,
S., & Wassong, T. (2019). Maschinelles Lernen im Unterricht mit Jupyter Notebook.
In A. Pasternak (Ed.), Informatik für alle (p. 385). Gesellschaft für Informatik.
bibtex: '@inproceedings{Schlichtig_Opel_Schulte_Biehler_Frischemeier_Podworny_Wassong_2019,
place={Bonn}, title={Maschinelles Lernen im Unterricht mit Jupyter Notebook},
booktitle={Informatik für alle}, publisher={Gesellschaft für Informatik}, author={Schlichtig,
Michael and Opel, Simone and Schulte, Carsten and Biehler, Rolf and Frischemeier,
Daniel and Podworny, Susanne and Wassong, Thomas}, editor={Pasternak, Arno}, year={2019},
pages={385} }'
chicago: 'Schlichtig, Michael, Simone Opel, Carsten Schulte, Rolf Biehler, Daniel
Frischemeier, Susanne Podworny, and Thomas Wassong. “Maschinelles Lernen im Unterricht
mit Jupyter Notebook.” In Informatik für alle, edited by Arno Pasternak,
385. Bonn: Gesellschaft für Informatik, 2019.'
ieee: M. Schlichtig et al., “Maschinelles Lernen im Unterricht mit Jupyter
Notebook,” in Informatik für alle, Dortmund, Germany, 2019, p. 385.
mla: Schlichtig, Michael, et al. “Maschinelles Lernen im Unterricht mit Jupyter
Notebook.” Informatik für alle, edited by Arno Pasternak, Gesellschaft
für Informatik, 2019, p. 385.
short: 'M. Schlichtig, S. Opel, C. Schulte, R. Biehler, D. Frischemeier, S. Podworny,
T. Wassong, in: A. Pasternak (Ed.), Informatik für alle, Gesellschaft für Informatik,
Bonn, 2019, p. 385.'
conference:
end_date: 2019-09-18
location: Dortmund, Germany
name: INFOS 2019
start_date: 2019-09-16
date_created: 2019-11-07T14:08:13Z
date_updated: 2025-05-25T20:01:30Z
department:
- _id: '67'
- _id: '97'
editor:
- first_name: Arno
full_name: Pasternak, Arno
last_name: Pasternak
language:
- iso: ger
main_file_link:
- url: https://dl.gi.de/handle/20.500.12116/28964
page: ' 385 '
place: Bonn
publication: Informatik für alle
publication_identifier:
isbn:
- 978-3-88579-682-4
publication_status: published
publisher: Gesellschaft für Informatik
status: public
title: Maschinelles Lernen im Unterricht mit Jupyter Notebook
type: conference
user_id: '21241'
year: '2019'
...