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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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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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$$\textsf{ATM}$$ : A Logic for Quantitative Security Properties on Attack Trees 21st

$$\textsf{ATM}$$ : A Logic for Quantitative Security Proper...

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21st International Conference on Software Engineering and formal methods, SEFM 2023

作者： Nicoletti, Stefano M. Lopuhaä-Zwakenberg, Milan Hahn, Ernst Moritz Stoelinga, Mariëlle Formal Methods and Tools University of Twente Enschede Netherlands Department of Software Science Radboud University Nijmegen Netherlands

ISBN: (纸本)9783031471148

Critical infrastructure systems—for which high reliability and availability are paramount—must operate securely. Attack trees (ATs) are hierarchical diagrams that offer a flexible modelling language used to assess how systems can be attacked. ATs are widely employed both in industry and academia but—in spite of their popularity—little work has been done to give practitioners instruments to formulate queries on ATs in an understandable yet powerful way. In this paper we fill this gap by presenting $$\textsf{ATM}$$, a logic to express quantitative security properties on ATs. $$\textsf{ATM}$$ allows for the specification of properties involved with security metrics that include "cost", "probability" and "skill" and permits the formulation of insightful what-if scenarios. To showcase its potential, we apply $$\textsf{ATM}$$ to the case study of a CubeSAT, presenting three different ways in which an attacker can compromise its availability. We showcase property specification on the corresponding attack tree and we present theory and algorithms—based on binary decision diagrams—to check properties and compute metrics of $$\textsf{ATM}$$ -formulae. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Modeling languages

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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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WITH A LITTLE HELP FROM YOUR FRIENDS: SEMI-COOPERATIVE GAMES VIA JOKER MOVES

arXiv

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arXiv 2023年

作者： van Den Bos, Petra Stoelinga, Marielle Formal Methods & Tools University of Twente Netherlands Department of Software Science Radboud University Netherlands

This paper coins the notion of Joker games, a variant of concurrent games where the players are not strictly adversarial. Instead, Player 1 can get help from Player 2 by playing a Joker move. We formalize these games as cost games and develop strategies that minimize the use of Jokers – viewed as costs – to secure a win with the least possible help. Our investigation studies the theoretical underpinnings of these games and their associated Joker strategies. In particular, when comparing our cost-minimal strategies with admissible strategies, we find out that they differ. Moreover, while randomization can be beneficial in conventional concurrent games, it does not aid in winning Joker games, although it can help reduce the number of needed Jokers. We also enhance our framework by introducing a secondary objective, namely by minimizing the number of moves executed by a Joker strategy. Finally, we demonstrate the practical advantages of our approach by applying it to test generation in model-based testing. © 2023, CC BY.

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Deriving Modernity Signatures of Codebases with Static Analysis

SSRN

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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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Scenario Approach for Parametric Markov Models 21st

Scenario Approach for Parametric Markov Models

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21st International Symposium on Automated Technology for Verification and Analysis, ATVA 2023

作者： Liu, Ying Turrini, Andrea Hahn, Ernst Moritz Xue, Bai Zhang, Lijun State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Software Guangzhou Guangzhou China Formal Methods and Tools University of Twente Enschede Netherlands

ISBN: (纸本)9783031453281

In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly – without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state-of-the-art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomial approximation

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Covid severity prediction: Who cares about the data quality?

Covid severity prediction: Who cares about the data quality?

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Nae, Teodora Krabbe, Johannes Bukhsh, Faiza Allah Jayasinghe Arachchige, Jeewanie Ahmed, Faizan University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands Medische Spectrum Twente Enschede Netherlands University of Ruhuna Department of Computer Science Matara Sri Lanka University of Twente Formal Methods and Tools Group Enschede Netherlands

ISBN: (纸本)9798350345933

COVID-19 is an ongoing pandemic disrupting daily life and overwhelming the healthcare infrastructure. Since the outburst of the pandemic, researchers have used various techniques to predict many aspects of the disease, including mortality rate and severity. The reproducibility of this research is challenging due to varying methodologies used to collect data, data quality, vague description of methodological approach to training prediction models, over-relying on data imputation, and over-fitting. This paper focuses on these challenges and provides a short yet comprehensive review of research on COVID mortality and severity prediction. The emphasis is on the reproducibility of the results and data quality issues. To further elaborate on the issue, we report the development of severity prediction models using two data sets. CRISP-DM is used as a methodological approach. We analyze and criticize the quality of the used data sets and how they affect the performance and limitations of the trained models. We conclude this paper with comments on data quality issues, the importance of reproducibility, and suggestions to improve reproducibility. © 2022 IEEE.

关键词： COVID-19

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