9 Search Results for "Ryder, Barbara G."

Document
DOI: 10.4230/LIPIcs.ECOOP.2025.28
Reusing Caches and Invariants for Efficient and Sound Incremental Static Analysis

Authors: Mamy Razafintsialonina, David Bühler, Antoine Miné, Valentin Perrelle, and Julien Signoles

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Static analysis by means of abstract interpretation is a tool of choice for proving absence of some classes of errors, typically undefined behaviors in C code, in a sound way. However, static analysis tools are hardly integrated in CI/CD processes. One of the main reasons is that they are still time- and memory-expensive to apply after every single patch when developing a program. For solving this issue, incremental static analysis helps developers quickly obtain analysis results after making changes to a program. However, existing approaches are often not guaranteed to be sound, limited to specific analyses, or tied to specific tools. This limits their generalizability and applicability in practice, especially for large and critical software. In this paper, we propose a generic, sound approach to incremental static analysis that is applicable to any abstract interpreter. Our approach leverages the similarity between two versions of a program to soundly reuse previously computed analysis results. We introduce novel methods for summarizing functions and reusing loop invariants. They significantly reduce the cost of reanalysis, while maintaining soundness and a high level of precision. We have formalized our approach, proved it sound, implemented it in Eva, the abstract interpreter of Frama-C, and evaluated it on a set of real-world commits of open-source programs.
Cite as

Mamy Razafintsialonina, David Bühler, Antoine Miné, Valentin Perrelle, and Julien Signoles. Reusing Caches and Invariants for Efficient and Sound Incremental Static Analysis. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 28:1-28:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{razafintsialonina_et_al:LIPIcs.ECOOP.2025.28,
  author =	{Razafintsialonina, Mamy and B\"{u}hler, David and Min\'{e}, Antoine and Perrelle, Valentin and Signoles, Julien},
  title =	{{Reusing Caches and Invariants for Efficient and Sound Incremental Static Analysis}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{28:1--28:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.28},
  URN =		{urn:nbn:de:0030-drops-233207},
  doi =		{10.4230/LIPIcs.ECOOP.2025.28},
  annote =	{Keywords: Abstract Interpretation, Static Analysis, Incremental Analysis}
}
Document
Experience Paper
DOI: 10.4230/LIPIcs.ECOOP.2025.38
Type-Safe and Portable Support for Packed Data (Experience Paper)

Authors: Arthur Jamet and Michael Vollmer

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
When components of a system exchange data, they need to serialise the data so that it can be sent over the network. Then, the recipient has to deserialise the data in order to be able to process it. These steps take time and have an impact on the overall system’s performance. A solution to this is to use packed data, which has a unified representation between the memory and the network, removing the need for any marshalling steps. Additionally, using this data representation can improve the program’s performance thanks to the data locality enabled by the compact representation of the data in memory. Unfortunately, no mainstream programming languages support packed data, whether it’s out-of-the-box or through a compiler extension. We present packed-data, a Haskell library that allows for type safe building and reading of packed data in a functional style. The library does not rely on compiler modifications, making it portable, and leverages meta-programming to allow programmers to pack their own data types effortlessly. We evaluate the usability and performance of the library, and conclude that it allows traversing packed data up to 60% faster than unpacked data. We also reflect on how to enhance the performance of library-based support for packed data. Our implementation approach is general and can easily be used with any programming languages that support higher-kinded types.
Cite as

Arthur Jamet and Michael Vollmer. Type-Safe and Portable Support for Packed Data (Experience Paper). In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 38:1-38:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{jamet_et_al:LIPIcs.ECOOP.2025.38,
  author =	{Jamet, Arthur and Vollmer, Michael},
  title =	{{Type-Safe and Portable Support for Packed Data}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{38:1--38:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.38},
  URN =		{urn:nbn:de:0030-drops-233301},
  doi =		{10.4230/LIPIcs.ECOOP.2025.38},
  annote =	{Keywords: program optimisation, data structures, data layout, packed data}
}
Document
Replication Paper
DOI: 10.4230/LIPIcs.ECOOP.2025.40
Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification (Replication Paper)

Authors: Francisco Handrick Tomaz da Costa, Ismael Medeiros, Leandro Oliveira, João Calássio, Rodrigo Bonifácio, Krishna Narasimhan, Mira Mezini, and Márcio Ribeiro

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
The widespread use of smartphones in daily life has raised concerns about privacy and security among researchers and practitioners. Privacy issues are generally highly prevalent in mobile applications, particularly targeting the Android platform - the most popular mobile operating system. For this reason, several techniques have been proposed to identify malicious behavior in Android applications, including the Mining Android Sandbox approach (MAS approach), which aims to identify malicious behavior in repackaged Android applications (apps). However, previous empirical studies evaluated the MAS approach using a small dataset consisting of only 102 pairs of original and repackaged apps. This limitation raises questions about the external validity of their findings and whether the MAS approach can be generalized to larger datasets. To address these concerns, this paper presents the results of a replication study focused on evaluating the performance of the MAS approach regarding its capabilities of correctly classifying malware from different families. Unlike previous studies, our research employs a dataset that is an order of magnitude larger, comprising 4,076 pairs of apps covering a more diverse range of Android malware families. Surprisingly, our findings indicate a poor performance of the MAS approach for identifying malware, with the F1-score decreasing from 0.90 for the small dataset used in the previous studies to 0.54 in our more extensive dataset. Upon closer examination, we discovered that certain malware families partially account for the low accuracy of the MAS approach, which fails to classify a repackaged version of an app as malware correctly. Our findings highlight the limitations of the MAS approach, particularly when scaled, and underscore the importance of complementing it with other techniques to detect a broader range of malware effectively. This opens avenues for further discussion on addressing the blind spots that affect the accuracy of the MAS approach.
Cite as

Francisco Handrick Tomaz da Costa, Ismael Medeiros, Leandro Oliveira, João Calássio, Rodrigo Bonifácio, Krishna Narasimhan, Mira Mezini, and Márcio Ribeiro. Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification (Replication Paper). In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 40:1-40:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{handricktomazdacosta_et_al:LIPIcs.ECOOP.2025.40,
  author =	{Handrick Tomaz da Costa, Francisco and Medeiros, Ismael and Oliveira, Leandro and Cal\'{a}ssio, Jo\~{a}o and Bonif\'{a}cio, Rodrigo and Narasimhan, Krishna and Mezini, Mira and Ribeiro, M\'{a}rcio},
  title =	{{Scaling Up: Revisiting Mining Android Sandboxes at Scale for Malware Classification}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{40:1--40:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.40},
  URN =		{urn:nbn:de:0030-drops-233320},
  doi =		{10.4230/LIPIcs.ECOOP.2025.40},
  annote =	{Keywords: Android Malware Detection, Dynamic Analysis, Mining Android Sandboxes}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.19
Profile-Guided Field Externalization in an Ahead-Of-Time Compiler

Authors: Sebastian Kloibhofer, Lukas Makor, Peter Hofer, David Leopoldseder, and Hanspeter Mössenböck

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
Field externalization is a technique to reduce the footprint of objects by removing fields that most frequently contain zero or null. While researchers have developed ways to bring this optimization into the Java world, these have been limited to research compilers or virtual machines for embedded systems. In this work, we present a novel field externalization technique that uses information from static analysis and profiling to determine externalizable fields. During compilation, we remove those fields and define companion classes. These are used in case of non-default-value writes to the externalized fields. Our approach also correctly handles synchronization to prevent issues in multithreaded environments. We integrated our approach into the modern Java ahead-of-time compiler GraalVM Native Image. We conducted an evaluation on a diverse set of benchmarks that includes standard and microservice-based benchmarks. For standard benchmarks, our approach reduces the total allocated bytes by 2.76% and the maximum resident set size (max-RSS) by 2.55%. For microservice benchmarks, we achieved a reduction of 6.88% for normalized allocated bytes and 2.45% for max-RSS. We computed these improvements via the geometric mean. The median reductions are are 1.46% (alloc. bytes) and 0.22% (max-RSS) in standard benchmarks, as well as 3.63% (alloc. bytes) and 0.20% (max-RSS) in microservice benchmarks.
Cite as

Sebastian Kloibhofer, Lukas Makor, Peter Hofer, David Leopoldseder, and Hanspeter Mössenböck. Profile-Guided Field Externalization in an Ahead-Of-Time Compiler. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 19:1-19:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{kloibhofer_et_al:LIPIcs.ECOOP.2025.19,
  author =	{Kloibhofer, Sebastian and Makor, Lukas and Hofer, Peter and Leopoldseder, David and M\"{o}ssenb\"{o}ck, Hanspeter},
  title =	{{Profile-Guided Field Externalization in an Ahead-Of-Time Compiler}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{19:1--19:32},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.19},
  URN =		{urn:nbn:de:0030-drops-233121},
  doi =		{10.4230/LIPIcs.ECOOP.2025.19},
  annote =	{Keywords: compilation, instrumentation, profiling, fields, externalization, memory footprint reduction, memory footprint optimization}
}
Document
Position
DOI: 10.4230/TGDK.1.1.5
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

Authors: Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.
Cite as

Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma. Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 5:1-5:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@Article{chen_et_al:TGDK.1.1.5,
  author =	{Chen, Jiaoyan and Dong, Hang and Hastings, Janna and Jim\'{e}nez-Ruiz, Ernesto and L\'{o}pez, Vanessa and Monnin, Pierre and Pesquita, Catia and \v{S}koda, Petr and Tamma, Valentina},
  title =	{{Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{5:1--5:33},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.5},
  URN =		{urn:nbn:de:0030-drops-194791},
  doi =		{10.4230/TGDK.1.1.5},
  annote =	{Keywords: Knowledge graphs, Life science, Knowledge discovery, Explainable AI}
}
Document
Position
DOI: 10.4230/TGDK.1.1.2
Large Language Models and Knowledge Graphs: Opportunities and Challenges

Authors: Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.
Cite as

Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux. Large Language Models and Knowledge Graphs: Opportunities and Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 2:1-2:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@Article{pan_et_al:TGDK.1.1.2,
  author =	{Pan, Jeff Z. and Razniewski, Simon and Kalo, Jan-Christoph and Singhania, Sneha and Chen, Jiaoyan and Dietze, Stefan and Jabeen, Hajira and Omeliyanenko, Janna and Zhang, Wen and Lissandrini, Matteo and Biswas, Russa and de Melo, Gerard and Bonifati, Angela and Vakaj, Edlira and Dragoni, Mauro and Graux, Damien},
  title =	{{Large Language Models and Knowledge Graphs: Opportunities and Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:38},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.2},
  URN =		{urn:nbn:de:0030-drops-194766},
  doi =		{10.4230/TGDK.1.1.2},
  annote =	{Keywords: Large Language Models, Pre-trained Language Models, Knowledge Graphs, Ontology, Retrieval Augmented Language Models}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2015.712
Adaptive Context-sensitive Analysis for JavaScript

Authors: Shiyi Wei and Barbara G. Ryder

Published in: LIPIcs, Volume 37, 29th European Conference on Object-Oriented Programming (ECOOP 2015)

Abstract
Context sensitivity is a technique to improve program analysis precision by distinguishing between function calls. A specific context-sensitive analysis is usually designed to accommodate the programming paradigm of a particular programming language. JavaScript features both the object-oriented and functional programming paradigms. Our empirical study suggests that there is no single context-sensitive analysis that always produces precise results for JavaScript applications. This observation motivated us to design an adaptive analysis, selecting a context-sensitive analysis from multiple choices for each function. Our two-staged adaptive context-sensitive analysis first extracts function characteristics from an inexpensive points-to analysis and then chooses a specialized context-sensitive analysis per function based on the heuristics. The experimental results show that our adaptive analysis achieved more precise results than any single context-sensitive analysis for several JavaScript programs in the benchmarks.
Cite as

Shiyi Wei and Barbara G. Ryder. Adaptive Context-sensitive Analysis for JavaScript. In 29th European Conference on Object-Oriented Programming (ECOOP 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 37, pp. 712-734, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

Copy BibTex To Clipboard

@InProceedings{wei_et_al:LIPIcs.ECOOP.2015.712,
  author =	{Wei, Shiyi and Ryder, Barbara G.},
  title =	{{Adaptive Context-sensitive Analysis for JavaScript}},
  booktitle =	{29th European Conference on Object-Oriented Programming (ECOOP 2015)},
  pages =	{712--734},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-86-6},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{37},
  editor =	{Boyland, John Tang},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2015.712},
  URN =		{urn:nbn:de:0030-drops-52446},
  doi =		{10.4230/LIPIcs.ECOOP.2015.712},
  annote =	{Keywords: Context Sensitivity, JavaScript, Static Program Analysis}
}
Document
DOI: 10.4230/LITES-v002-i001-a002
More General Optimal Offset Assignment

Authors: Sven Mallach

Published in: LITES, Volume 2, Issue 1 (2015). Leibniz Transactions on Embedded Systems, Volume 2, Issue 1

Abstract
This manuscript presents exact approaches to the general offset assignment problem arising in the address code generation phase of compilers for application-specific processors. First, integer programming models for architecture-dependent and theoretically motivated special cases of the problem are established. Then, these models are extended to provide the first widely applicable formulations for the most general problem setting, supporting processors with several address registers and complex addressing capabilities. Existing heuristics are similarly extended and practical applicability of the proposed methods is demonstrated by experimental evaluation using an established and large benchmark set. The experiments allow us to study the impact of exploiting more complex memory addressing capabilities on the address computation costs of real-world programs. We also show how to integrate operand reordering techniques for commutative instructions into existing solution approaches.
Cite as

Sven Mallach. More General Optimal Offset Assignment. In LITES, Volume 2, Issue 1 (2015). Leibniz Transactions on Embedded Systems, Volume 2, Issue 1, pp. 02:1-02:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

Copy BibTex To Clipboard

@Article{mallach:LITES-v002-i001-a002,
  author =	{Mallach, Sven},
  title =	{{More General Optimal Offset Assignment}},
  journal =	{Leibniz Transactions on Embedded Systems},
  pages =	{02:1--02:18},
  ISSN =	{2199-2002},
  year =	{2015},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES-v002-i001-a002},
  URN =		{urn:nbn:de:0030-drops-192520},
  doi =		{10.4230/LITES-v002-i001-a002},
  annote =	{Keywords: Compiler optimization, Application-specific processors, Address code generation, Offset assignment, Integer programming}
}
Document
DOI: 10.4230/DagSemRep.405
Understanding Program Dynamics (Dagstuhl Seminar 03491)

Authors: Jong-Deok Choi, Barbara G. Ryder, and Andreas Zeller

Published in: Dagstuhl Seminar Reports. Dagstuhl Seminar Reports, Volume 1 (2021)

Abstract
Cite as

Jong-Deok Choi, Barbara G. Ryder, and Andreas Zeller. Understanding Program Dynamics (Dagstuhl Seminar 03491). Dagstuhl Seminar Report 405, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2003)

Copy BibTex To Clipboard

@TechReport{choi_et_al:DagSemRep.405,
  author =	{Choi, Jong-Deok and Ryder, Barbara G. and Zeller, Andreas},
  title =	{{Understanding Program Dynamics (Dagstuhl Seminar 03491)}},
  pages =	{1--6},
  ISSN =	{1619-0203},
  year =	{2003},
  type = 	{Dagstuhl Seminar Report},
  number =	{405},
  institution =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemRep.405},
  URN =		{urn:nbn:de:0030-drops-152854},
  doi =		{10.4230/DagSemRep.405},
}
  • Refine by Type
  • 9 Document/PDF
  • 6 Document/HTML

  • Refine by Publication Year
  • 4 2025
  • 2 2023
  • 2 2015
  • 1 2003

  • Refine by Author
  • 2 Chen, Jiaoyan
  • 2 Ryder, Barbara G.
  • 1 Biswas, Russa
  • 1 Bonifati, Angela
  • 1 Bonifácio, Rodrigo
  • Show More...

  • Refine by Series/Journal
  • 5 LIPIcs
  • 1 LITES
  • 2 TGDK
  • 1 DagSemRep

  • Refine by Classification
  • 2 Computing methodologies → Knowledge representation and reasoning
  • 1 Applied computing → Life and medical sciences
  • 1 Computing methodologies → Natural language processing
  • 1 General and reference → Surveys and overviews
  • 1 Information systems → Data layout
  • Show More...

  • Refine by Keyword
  • 1 Abstract Interpretation
  • 1 Address code generation
  • 1 Android Malware Detection
  • 1 Application-specific processors
  • 1 Compiler optimization
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact