Program debloating via stochastic optimization Article Swipe
Xin Qi
,
Myeongsoo Kim
,
Qirun Zhang
,
Alessandro Orso
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1145/3377816.3381739
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1145/3377816.3381739
Programs typically provide a broad range of features. Because different typologies of users tend to use only a subset of these features, and unnecessary features can harm performance and security, program debloating techniques, which can reduce the size of a program by eliminating (possibly) unneeded features, are becoming increasingly popular. Most existing debloating techniques tend to focus on program-size reduction alone and, although effective, ignore other important aspects of debloating. We believe that program debloating is a multifaceted problem that must be addressed in a more general way. In this spirit, we propose a general approach that allows for formulating program debloating as a multi-objective optimization problem. Given a program to be debloated, our approach lets users specify (1) a usage profile for the program (i.e., a set of inputs with associated usage probabilities), (2) the factors of interest for debloating, and (3) the relative importance of these factors. Based on this information, the approach defines a suitable objective function for associating a score to every possible reduced program and aims to generate an optimal solution that maximizes the objective function. We also present and evaluate Debop, a specific instance of our approach that considers three objectives: size reduction, attack-surface reduction, and generality (i.e., the extent to which the reduced program handles inputs in the usage profile provided). Our results, albeit still preliminary, are promising and show that our approach can be effective at generating debloated programs that achieve a good trade-off between the different de-bloating objectives considered. Our results also provide insights on the performance of our general approach when compared to a specialized single-goal technique.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3377816.3381739
- https://dl.acm.org/doi/pdf/10.1145/3377816.3381739
- OA Status
- gold
- Cited By
- 10
- References
- 21
- Related Works
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- OpenAlex ID
- https://openalex.org/W3091419034
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3091419034Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3377816.3381739Digital Object Identifier
- Title
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Program debloating via stochastic optimizationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
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2020-06-27Full publication date if available
- Authors
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Xin Qi, Myeongsoo Kim, Qirun Zhang, Alessandro OrsoList of authors in order
- Landing page
-
https://doi.org/10.1145/3377816.3381739Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3377816.3381739Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3377816.3381739Direct OA link when available
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Computer science, Generality, Set (abstract data type), Reduction (mathematics), Range (aeronautics), Function (biology), Mathematics, Programming language, Evolutionary biology, Geometry, Psychotherapist, Biology, Psychology, Composite material, Materials scienceTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 2, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.existing | 51 |
| abstract_inverted_index.factors. | 148 |
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| abstract_inverted_index.function | 159 |
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| abstract_inverted_index.interest | 138 |
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| abstract_inverted_index.debloated | 235 |
| abstract_inverted_index.different | 9, 244 |
| abstract_inverted_index.effective | 232 |
| abstract_inverted_index.features, | 21, 45 |
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| abstract_inverted_index.reduction | 59 |
| abstract_inverted_index.security, | 29 |
| abstract_inverted_index.trade-off | 241 |
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| abstract_inverted_index.debloated, | 112 |
| abstract_inverted_index.debloating | 31, 52, 74, 101 |
| abstract_inverted_index.effective, | 63 |
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| abstract_inverted_index.generating | 234 |
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| abstract_inverted_index.reduction, | 198, 200 |
| abstract_inverted_index.technique. | 266 |
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| abstract_inverted_index.considered. | 247 |
| abstract_inverted_index.de-bloating | 245 |
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| abstract_inverted_index.formulating | 99 |
| abstract_inverted_index.objectives: | 196 |
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| abstract_inverted_index.specialized | 264 |
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| abstract_inverted_index.preliminary, | 222 |
| abstract_inverted_index.program-size | 58 |
| abstract_inverted_index.attack-surface | 199 |
| abstract_inverted_index.multi-objective | 104 |
| abstract_inverted_index.probabilities), | 133 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.82953596 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |