---
_id: '53958'
abstract:
- lang: eng
  text: "To detect security vulnerabilities, static analysis tools need to be configured
    with security-relevant methods. Current approaches can automatically identify
    such methods using binary relevance machine learning approaches. However, they
    ignore dependencies among security-relevant methods, over-generalize and perform
    poorly in practice. Additionally, users have to nevertheless manually configure
    static analysis tools using the detected methods. Based on feedback from users
    and our observations, the excessive manual steps can often be tedious, error-prone
    and counter-intuitive.\r\n In this paper, we present Dev-Assist, an IntelliJ IDEA
    plugin that detects security-relevant methods using a multi-label machine learning
    approach that considers dependencies among labels. The plugin can automatically
    generate configurations for static analysis tools, run the static analysis, and
    show the results in IntelliJ IDEA. Our experiments reveal that Dev-Assist's machine
    learning approach has a higher F1-Measure than related approaches. Moreover, the
    plugin reduces and simplifies the manual effort required when configuring and
    using static analysis tools."
author:
- first_name: Oshando
  full_name: Johnson, Oshando
  id: '66583'
  last_name: Johnson
- first_name: Goran
  full_name: Piskachev, Goran
  id: '41936'
  last_name: Piskachev
  orcid: 0000-0003-4424-5838
- first_name: Ranjith
  full_name: Krishnamurthy, Ranjith
  id: '78060'
  last_name: Krishnamurthy
  orcid: 0000-0002-0906-5463
- first_name: Eric
  full_name: Bodden, Eric
  id: '59256'
  last_name: Bodden
  orcid: 0000-0003-3470-3647
citation:
  ama: 'Johnson O, Piskachev G, Krishnamurthy R, Bodden E. Detecting Security-Relevant
    Methods using Multi-label Machine Learning. In: <i>Proceedings of the 46th International
    Conference on Software Engineering, IDE Workshop</i>. ; 2024. doi:<a href="https://doi.org/10.48550/ARXIV.2403.07501">10.48550/ARXIV.2403.07501</a>'
  apa: Johnson, O., Piskachev, G., Krishnamurthy, R., &#38; Bodden, E. (2024). Detecting
    Security-Relevant Methods using Multi-label Machine Learning. <i>Proceedings of
    the 46th International Conference on Software Engineering, IDE Workshop</i>. <a
    href="https://doi.org/10.48550/ARXIV.2403.07501">https://doi.org/10.48550/ARXIV.2403.07501</a>
  bibtex: '@inproceedings{Johnson_Piskachev_Krishnamurthy_Bodden_2024, title={Detecting
    Security-Relevant Methods using Multi-label Machine Learning}, DOI={<a href="https://doi.org/10.48550/ARXIV.2403.07501">10.48550/ARXIV.2403.07501</a>},
    booktitle={Proceedings of the 46th International Conference on Software Engineering,
    IDE Workshop}, author={Johnson, Oshando and Piskachev, Goran and Krishnamurthy,
    Ranjith and Bodden, Eric}, year={2024} }'
  chicago: Johnson, Oshando, Goran Piskachev, Ranjith Krishnamurthy, and Eric Bodden.
    “Detecting Security-Relevant Methods Using Multi-Label Machine Learning.” In <i>Proceedings
    of the 46th International Conference on Software Engineering, IDE Workshop</i>,
    2024. <a href="https://doi.org/10.48550/ARXIV.2403.07501">https://doi.org/10.48550/ARXIV.2403.07501</a>.
  ieee: 'O. Johnson, G. Piskachev, R. Krishnamurthy, and E. Bodden, “Detecting Security-Relevant
    Methods using Multi-label Machine Learning,” 2024, doi: <a href="https://doi.org/10.48550/ARXIV.2403.07501">10.48550/ARXIV.2403.07501</a>.'
  mla: Johnson, Oshando, et al. “Detecting Security-Relevant Methods Using Multi-Label
    Machine Learning.” <i>Proceedings of the 46th International Conference on Software
    Engineering, IDE Workshop</i>, 2024, doi:<a href="https://doi.org/10.48550/ARXIV.2403.07501">10.48550/ARXIV.2403.07501</a>.
  short: 'O. Johnson, G. Piskachev, R. Krishnamurthy, E. Bodden, in: Proceedings of
    the 46th International Conference on Software Engineering, IDE Workshop, 2024.'
date_created: 2024-05-06T11:43:19Z
date_updated: 2024-05-06T11:47:14Z
department:
- _id: '76'
- _id: '662'
doi: 10.48550/ARXIV.2403.07501
language:
- iso: eng
publication: Proceedings of the 46th International Conference on Software Engineering,
  IDE Workshop
status: public
title: Detecting Security-Relevant Methods using Multi-label Machine Learning
type: conference
user_id: '15249'
year: '2024'
...
---
_id: '41812'
author:
- first_name: Linghui
  full_name: Luo, Linghui
  last_name: Luo
- first_name: Goran
  full_name: Piskachev, Goran
  id: '41936'
  last_name: Piskachev
  orcid: 0000-0003-4424-5838
- first_name: Ranjith
  full_name: Krishnamurthy, Ranjith
  id: '78060'
  last_name: Krishnamurthy
  orcid: 0000-0002-0906-5463
- first_name: Julian
  full_name: Dolby, Julian
  last_name: Dolby
- first_name: Martin
  full_name: Schäf, Martin
  last_name: Schäf
- first_name: Eric
  full_name: Bodden, Eric
  id: '59256'
  last_name: Bodden
  orcid: 0000-0003-3470-3647
citation:
  ama: 'Luo L, Piskachev G, Krishnamurthy R, Dolby J, Schäf M, Bodden E. Model Generation
    For Java Frameworks. In: <i>IEEE International Conference on Software Testing,
    Verification and Validation (ICST)</i>. ; 2023.'
  apa: Luo, L., Piskachev, G., Krishnamurthy, R., Dolby, J., Schäf, M., &#38; Bodden,
    E. (2023). Model Generation For Java Frameworks. <i>IEEE International Conference
    on Software Testing, Verification and Validation (ICST)</i>.
  bibtex: '@inproceedings{Luo_Piskachev_Krishnamurthy_Dolby_Schäf_Bodden_2023, title={Model
    Generation For Java Frameworks}, booktitle={IEEE International Conference on Software
    Testing, Verification and Validation (ICST)}, author={Luo, Linghui and Piskachev,
    Goran and Krishnamurthy, Ranjith and Dolby, Julian and Schäf, Martin and Bodden,
    Eric}, year={2023} }'
  chicago: Luo, Linghui, Goran Piskachev, Ranjith Krishnamurthy, Julian Dolby, Martin
    Schäf, and Eric Bodden. “Model Generation For Java Frameworks.” In <i>IEEE International
    Conference on Software Testing, Verification and Validation (ICST)</i>, 2023.
  ieee: L. Luo, G. Piskachev, R. Krishnamurthy, J. Dolby, M. Schäf, and E. Bodden,
    “Model Generation For Java Frameworks,” 2023.
  mla: Luo, Linghui, et al. “Model Generation For Java Frameworks.” <i>IEEE International
    Conference on Software Testing, Verification and Validation (ICST)</i>, 2023.
  short: 'L. Luo, G. Piskachev, R. Krishnamurthy, J. Dolby, M. Schäf, E. Bodden, in:
    IEEE International Conference on Software Testing, Verification and Validation
    (ICST), 2023.'
date_created: 2023-02-06T10:37:23Z
date_updated: 2025-04-07T10:15:08Z
department:
- _id: '76'
- _id: '662'
language:
- iso: eng
publication: IEEE International Conference on Software Testing, Verification and Validation
  (ICST)
status: public
title: Model Generation For Java Frameworks
type: conference
user_id: '15249'
year: '2023'
...
---
_id: '33838'
author:
- first_name: Ranjith
  full_name: Krishnamurthy, Ranjith
  id: '78060'
  last_name: Krishnamurthy
  orcid: 0000-0002-0906-5463
- first_name: Goran
  full_name: Piskachev, Goran
  id: '41936'
  last_name: Piskachev
  orcid: 0000-0003-4424-5838
- first_name: Eric
  full_name: Bodden, Eric
  id: '59256'
  last_name: Bodden
  orcid: 0000-0003-3470-3647
citation:
  ama: Krishnamurthy R, Piskachev G, Bodden E. To what extent can we analyze Kotlin
    programs using existing Java taint analysis tools? Published online 2022.
  apa: Krishnamurthy, R., Piskachev, G., &#38; Bodden, E. (2022). <i>To what extent
    can we analyze Kotlin programs using existing Java taint analysis tools?</i>
  bibtex: '@article{Krishnamurthy_Piskachev_Bodden_2022, series={IEEE International
    Working Conference on Source Code Analysis and Manipulation (SCAM)}, title={To
    what extent can we analyze Kotlin programs using existing Java taint analysis
    tools?}, author={Krishnamurthy, Ranjith and Piskachev, Goran and Bodden, Eric},
    year={2022}, collection={IEEE International Working Conference on Source Code
    Analysis and Manipulation (SCAM)} }'
  chicago: Krishnamurthy, Ranjith, Goran Piskachev, and Eric Bodden. “To What Extent
    Can We Analyze Kotlin Programs Using Existing Java Taint Analysis Tools?” IEEE
    International Working Conference on Source Code Analysis and Manipulation (SCAM),
    2022.
  ieee: R. Krishnamurthy, G. Piskachev, and E. Bodden, “To what extent can we analyze
    Kotlin programs using existing Java taint analysis tools?” 2022.
  mla: Krishnamurthy, Ranjith, et al. <i>To What Extent Can We Analyze Kotlin Programs
    Using Existing Java Taint Analysis Tools?</i> 2022.
  short: R. Krishnamurthy, G. Piskachev, E. Bodden, (2022).
date_created: 2022-10-20T12:38:09Z
date_updated: 2022-10-20T12:38:32Z
department:
- _id: '76'
- _id: '662'
language:
- iso: eng
series_title: IEEE International Working Conference on Source Code Analysis and Manipulation
  (SCAM)
status: public
title: To what extent can we analyze Kotlin programs using existing Java taint analysis
  tools?
type: conference
user_id: '15249'
year: '2022'
...