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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Gerhold, Marcus Solovyeva, Lola Zaytsev, Vadim University of Twente Formal Methods & Tools Enschede Netherlands University of Twente Computer Science Enschede Netherlands

ISBN: (纸本)9798350330663

The evolution of Python requires accurate version identification to facilitate compatibility and ongoing support. We extend previous work on deep learning models for Python version identification, where LSTM and CodeBERT achieved a 92% accuracy on short code snippets. We further expand these results to larger realistic files, utilising code segmentation techniques for varying input granularities. These techniques ranged from per-line analysis to larger code segments. Our findings show that while LSTM with CodeBERT embeddings maintained high accuracy on short snippets, performance significantly drops on longer segments, particularly in balancing information retention and misclassification risks. Notably, import-statement analysis, despite being the most intuitive indicator of version requirements, reached only a 30% accuracy. This exposes the limitations of our approach when encountering rare or user-defined modules. The findings expose the limitations of deep learning for language version identification, and suggest that alternative approaches may be necessary for high accuracy on larger datasets. © 2024 IEEE.

关键词： Python

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The Calculus of Temporal Influence 30

The Calculus of Temporal Influence

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30th International Symposium on Temporal Representation and Reasoning, TIME 2023

作者： Bruse, Florian Kastaun, Marit Lange, Martin Möller, Sören Theoretical Computer Science / Formal Methods University of Kassel Germany Didactics of Biology University of Kassel Germany

ISBN: (纸本)9783959772983

We present the Calculus of Temporal Influence, a simple logical calculus that allows reasoning about the behaviour of real-valued functions over time by making assertions that bound their values or the values of their derivatives. The motivation for the design of such a proof system comes from the need to provide the background computational machinery for tools that support learning in experimental subjects in secondary-education classrooms. The end goal is a tool that allows school pupils to formalise hypotheses about phenomena in natural sciences, such that their validity with respect to some formal experiment model can be checked automatically. The Calculus of Temporal Influence provides a language for formal statements and the mechanisms for reasoning about valid logical consequences. It extends (and deviates in parts from) previous work introducing the Calculus of (Non-Temporal) Influence by integrating the ability to model temporal effects in such experiments. We show that reasoning in the calculus is sound with respect to a natural formal semantics, that logical consequence is at least semi-decidable, and that one obtains polynomial-time decidability for a natural stratification of the problem. 2012 ACM Subject Classification Theory of computation → Theory and algorithms for application domains;Theory of computation → Automated reasoning;Applied computing → Interactive learning environments © Florian Bruse, Marit Kastaun, Martin Lange, and Sören Möller

关键词： Computability and decidability

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Leveraging deep learning for Python version identification 22

Leveraging deep learning for Python version identification

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22nd Belgium-Netherlands Software Evolution Workshop, BENEVOL 2023

作者： Gerhold, Marcus Solovyeva, Lola Zaytsev, Vadim Technical Computer Science University of Twente Enschede Netherlands Formal Methods and Tools University of Twente Enschede Netherlands

Python, recognized for its dynamic and adaptable nature, has found widespread application in a myriad of projects. As the language evolves, determining the Python version employed in a project becomes pivotal to ensure compatibility and facilitate maintenance. Deep learning (DL) has emerged as a promising tool to automate this process. In this research, we assess various DL techniques in determining the minimum Python version for a code snippet. We explore the complexities of handling Python data and the DL techniques to achieve high classification accuracy. Our experimental results show, that LSTM with CodeBERT embedding achives an accuracy of 92%. This success can be attributed to the LSTM's proficiency in capturing structural details of the hierarchical nature of a source code, complemented by CodeBERT's ability to discern contextual differences between keywords and variable names. This research provides insights into the challenges associated with utilizing programming languages for deep learning models and suggests potential solutions for addressing these issues. The envisioned applications extend to predicting the minimum required version for individual files or an entire code base. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Python

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Perfecting Nothingness by Refactoring Whitespace 15

Perfecting Nothingness by Refactoring Whitespace

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15th Seminar on Advanced Techniques and Tools for Software Evolution, SATToSE 2023

作者： Witmans, Rutger Zaytsev, Vadim Technical Computer Science University of Twente Enschede Netherlands Formal Methods & Tools University of Twente Enschede Netherlands

In this paper we explore the possibilities of refactoring code in Whitespace, a programming language which only recognises whitespace characters as code, and tolerates textual comments that describe the program. The paper presents a list of possible refactorings applicable to Whitespace, and describes a tool that automates some of these refactorings. The functionality of the tool is demonstrated with concrete examples. Refactoring is a important systematic process of improving code without creating new functionality, improving long-term properties of the code such as readability, maintainability, changeability, testability, extendability and safety. We argue that, despite the lack of real-life applications for Whitespace specifically, it is beneficial to apply refactoring methodology to it, since lessons learnt from esoteric languages can be ported elsewhere — in this case, to assemblers and similarly restrictive software languages. © 2023 Copyright for this paper by its authors.

关键词： Application programs

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Code Comprehension in a Multi-Paradigm Environment: Background and Experimental Setup 15

Code Comprehension in a Multi-Paradigm Environment: Backgrou...

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15th Seminar on Advanced Techniques and Tools for Software Evolution, SATToSE 2023

作者： Floor, Daniël van Hees, Rinse Zaytsev, Vadim Computer Science University of Twente Enschede Netherlands Info Support Veenendaal Netherlands Formal Methods & Tools University of Twente Enschede Netherlands

Code comprehension, a fundamental asset in software development and its maintenance, is influenced by the programming paradigms employed. Comprehending code takes up a major part of the maintenance process. This study forms a basis in discovering the relation between code comprehension and multi-paradigm usage. The different paradigms covered here are Object-oriented programming and functional programming. To measure the possible impact an experimental setup is designed that will help capturing quantitative and qualitative data. The decision of using interviews as study experiment allows for the capturing of the qualitative data necessary for an in-depth exploration of comprehension strategies and participants’ cognitive reasoning. The interviews will use Kotlin code snippets, this choice harmonises with participants’ familiarity with Java, which serves as a foundation, and the design of interview questions, which prioritise the comprehension of code and the unravelling of its underlying purpose. This paper provides the background and experimental setup that allows to investigate the relationship between code comprehension and multi-paradigm usage. © 2023 Copyright for this paper by its authors.

关键词： Functional programming

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The Limits of the Identifiable: Challenges in Python Version Identification with Deep Learning

The Limits of the Identifiable: Challenges in Python Version...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Marcus Gerhold Lola Solovyeva Vadim Zaytsev Formal Methods & Tools University of Twente Enschede the Netherlands Computer Science University of Twente Enschede the Netherlands

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

关键词： Deep learning Accuracy Codes Natural languages Pipelines Training data Predictive models

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"It’s like an explosion": Cyberwarfare harms for civilian population in Ukraine during the Russian invasion

"It’s like an explosion": Cyberwarfare harms for civilian p...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Kulyk, Oksana Kickbusch, Jari Mayer, Peter IT University of Copenhagen Copenhagen Denmark Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798400713958

Studies on civilian costs of cyberwarfare operations are crucial in understanding how to protect the population in large-scale cyberattacks conducted by state actors. In this preliminary study, we conduct interviews (N=5) focusing on the harms experienced by Ukrainian civilians due to Russian cyberwarfare operations since the start of the full-scale invasion in 2022 as well as mitigation measures for protection of the civil society. The findings include harms such as lack of information, lack of access to essential services, physical harms, anxiety and lack of trust in the government. The study shows that cyberwarfare operations have the potential of causing severe harm to civilians and that there is a need for future studies to be conducted and adopted by institutions in order to develop measures to mitigate, not only in Ukraine but in any countries in risk of such attacks. © 2025 Copyright held by the owner/author(s).

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TRANSFORMER ENCODER SATISFIABILITY: COMPLEXITY AND IMPACT ON formal REASONING

arXiv

引用

arXiv 2024年

作者： Sälzer, Marco Alsmann, Eric Lange, Martin University of Kaiserslautern-Landau RPTU Kaiserslautern Germany Theoretical Computer Science Formal Methods University of Kassel Germany

We analyse the complexity of the satisfiability problem, or similarly feasibility problem, (trSAT) for transformer encoders (TE), which naturally occurs in formal verification or interpretation, collectively referred to as formal reasoning. We find that trSAT is undecidable when considering TE as they are commonly studied in the expressiveness community. Furthermore, we identify practical scenarios where trSAT is decidable and establish corresponding complexity bounds. Beyond trivial cases, we find that quantized TE, those restricted by fixed-width arithmetic, lead to the decidability of trSAT due to their limited attention capabilities. However, the problem remains difficult, as we establish scenarios where trSAT is NEXPTIME-hard and others where it is solvable in NEXPTIME for quantized TE. To complement our complexity results, we place our findings and their implications in the broader context of formal reasoning. © 2024, CC BY.

关键词： formal verification

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A Logic for Reasoning about Aggregate-Combine Graph Neural Networks

arXiv

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arXiv 2024年

作者： Nunn, Pierre Sälzer, Marco Schwarzentruber, François Troquard, Nicolas University of Rennes IRISA CNRS France Theoretical Computer Science / Formal Methods University of Kassel Germany Gran Sasso Science Institute L’Aquila Italy

We propose a modal logic in which counting modalities appear in linear inequalities. We show that each formula can be transformed into an equivalent graph neural network (GNN). We also show that a broad class of GNNs can be transformed efficiently into a formula, thus significantly improving upon the literature about the logical expressiveness of GNNs. We also show that the satisfiability problem is PSPACE-complete. These results bring together the promise of using standard logical methods for reasoning about GNNs and their properties, particularly in applications such as GNN querying, equivalence checking, etc. We prove that such natural problems can be solved in polynomial space. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Deriving Modernity Signatures of Codebases with Static Analysis

SSRN

引用

SSRN 2023年

作者： Admiraal, Chris van den Brink, Wouter Gerhold, Marcus Zaytsev, Vadim Zubcu, Cristian Technical Computer Science University of Twente Enschede Netherlands Formal Methods & Tools University of Twente Enschede Netherlands

This paper addresses the problem of determining the modernity of software systems by analysing the use of new language features and their adoption over time. We propose the concept of modernity signatures to estimate the age of a codebase, naturally adjusted for maintenance practices, such that the modernity of a regularly updated system would be above that of a more recently created one which neglects current features and best practices. This can provide insights into coding practices, codebase health and the evolution of software languages. We present case studies on PHP and Python code, demonstrating the effectiveness of modernity signatures in determining the age of a codebase without executing the code or performing extensive human inspection. The paper describes the technical implementation details of generating the modernity signature for both of these languages, including the use of existing tools like the PHP parser and Vermin. The findings suggest that modernity signatures can aid developers in many ways from choosing whether to use a system or how to approach its maintenance, to assessing usefulness of a language feature, thus providing a valuable tool for source code analysis and manipulation. © 2023, The Authors. All rights reserved.

关键词： Static analysis

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