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检索条件"任意字段=IEEE Working Conference on Source Code Analysis and Manipulation"

共 958 条记录，以下是331-340 订阅

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Fuzzy Software Analyzer (FSA): A New Approach for Interpreting source code Versioning Repositories

Fuzzy Software Analyzer (FSA): A New Approach for Interpreti...

引用

ieee CIS International conference on Fuzzy Systems (FUZZ-ieee)

作者： Oliveira, Joao C. B. Rios, Ricardo A. de Almeida, Eduardo S. Sant'Anna, Claudio N. Rios, Tatiane Nogueira Univ Fed Bahia Dept Comp Sci Salvador BA Brazil

ISBN: (纸本)9781665444071

source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code;ii) from the release interpretation, specialists can improve the quality of the source code;and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.

关键词： Measurement Machine learning algorithms Linux conferences Software quality Monitoring Fuzzy systems

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EvoGraph: A lightweight approach to evolutionary and structural analysis of large software systems

EvoGraph: A lightweight approach to evolutionary and structu...

引用

13th working conference on Reverse Engineering

作者： Fischer, Michael Gall, Harald Univ Zurich Dept Informat Seal Zurich Switzerland

ISBN: (纸本)0769527191

Structural analyses frequently fall short in an adequate representation of historical changes for retrospective analysis. By compounding the two underlying information spaces in a single approach, the comprehension about the interaction between evolving requirements and system development can be improved significantly. We therefore propose a lightweight approach based on release history data and source code changes, which first selects entities with evolutionary outstanding characteristics and then indicates their structural dependencies via commonly used source code entities. The resulting data sets and visualizations aim at a holistic view to point out and assess structural stability, recurring modifications, or changes in the dependencies of the file-sets under inspection. In this paper we describe our approach and its results in terms of the Mozilla case study. Our approach completes typical release history mining and source code analysis approaches, therefore past restructuring events, new, shifted, and removed dependencies can be spotted easily.

关键词： Evolutionary algorithms

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CLEAN++: code Smells Extraction for C++ 20

CLEAN++: Code Smells Extraction for C++

引用

ieee/ACM 20th International conference on Mining Software Repositories (MSR)

作者： Mashiach, Tom Sotto-Mayor, Bruno Kaminka, Gal Kalech, Meir Ben Gurion Univ Negev Beer Sheva Israel Bar Ilan Univ Ramat Gan Israel

ISBN: (纸本)9798350311846

The extraction of features is an essential step in the process of mining software repositories. An important feature that has been actively studied in the field of mining software repositories is bad code smells. Bad code smells are patterns in the source code that indicate an underlying issue in the design and implementation of the software. Several tools have been proposed to extract code smells. However, currently, there are no tools that extract a significant number of code smells from software written in C++. Therefore, we propose CLEAN(++) (code smeLls ExtrActioN for C++) [1]. It is an extension of a robust static code analysis tool that implements 35 code smells. To evaluate CLEAN(++), we ran it over 44 open-source projects and wrote test cases to validate each code smell. Also, we converted the test cases to Java and used two Java tools to validate the effectiveness of our tool. In the end, we confirmed that the CLEAN(++) is successful at detecting code smells. The tool is available at https://***/Tomma94/CLEAN-Plus-Plus.

关键词： C++ code Smells Mining Software Repositories Open source Static analysis Tool

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Automated Concept Location Using Independent Component analysis

Automated Concept Location Using Independent Component Analy...

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15th working conference on Reverse Engineering

作者： Grant, Scott Cordy, James R. Skillicorn, David Queens Univ Sch Comp Kingston ON Canada

ISBN: (纸本)9780769534299

Concept location techniques are designed to help isolate sections of source code that relate to specific concepts. Blind Signal Separation techniques like Singular Value Decomposition and Latent Semantic Indexing can be used as a way to identify related sections of source code. This paper explores a related technique called Independent Component analysis that has the added benefit of identifying statistically independent signals in text, as opposed to ones that are just decorrelated. We describe a tool that we have developed to explore how ICA performs when analysing source code, and show how the technique can be used to perform unsupervised concept location.

关键词： Independent component analysis

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Write me this code: An analysis of ChatGPT Quality for Producing source code 21

Write me this Code: An Analysis of ChatGPT Quality for Produ...

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ieee/ACM 21st International conference on Mining Software Repositories (MSR)

作者： Moratis, Konstantinos Diamantopoulos, Themistoklis Nastos, Dimitrios-Nikitas Symeonidis, Andreas Aristotle Univ Thessaloniki Elect & Comp Engn Dept Thessaloniki Greece

ISBN: (纸本)9798350363982;9798400705878

Developers nowadays are increasingly turning to large language models (LLMs) like ChatGPT to assist them with coding tasks, inspired by the promise of efficiency and the advanced capabilities they offer. However, this raises important questions about the ease of integration and the safety of incorporating these tools into the development process. To investigate these questions, this paper examines a set of ChatGPT conversations. Upon annotating the conversations according to the intent of the developer, we focus on two critical aspects: firstly, the ease with which developers can produce suitable source code using ChatGPT, and, secondly, the quality aspects of the generated source code, determined by the compliance to standards and best practices. We research both the quality of the generated code itself and its impact on the project of the developer. Our results indicate that ChatGPT can be a useful tool for software development when used with discretion.

关键词： code Generation code Quality ChatGPT Large Language Models

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Enriching reverse engineering with semantic clustering

Enriching reverse engineering with semantic clustering

引用

12th working conference on Reverse Engineering (WCRE)

作者： Kuhn, A Ducasse, S Gîrba, T Univ Bern Software Composit Grp CH-3012 Bern Switzerland

ISBN: (纸本)0769524745

Understanding a software system by just analyzing the structure of the system reveals only half of the picture, since the structure tells us only how the code is working but not what the code is about. What the code is about can be found in the semantics of the source code: names of identifiers, comments etc. In this paper, we analyze how these terms are spread over the source artifacts using Latent Semantic Indexing, an information retrieval technique. We use the assumption that parts of the system that use similar terms are related. We cluster artifacts that use similar terms, and we reveal the most relevant terms for the computed clusters. Our approach works at the level of the source code which makes it language independent. Nevertheless, we correlated the semantics with structural information and we applied it at different levels of abstraction (e.g. classes, methods). We applied our approach on three large case studies and we report the results we obtained.

关键词： semantic analysis clustering concept location reverse engineering

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Embedding Java Classes with code2vec: Improvements from Variable Obfuscation 17

Embedding Java Classes with code2vec: Improvements from Vari...

引用

ieee/ACM 17th International conference on Mining Software Repositories (MSR)

作者： Compton, Rhys Frank, Eibe Patros, Panos Koay, Abigail Univ Waikato Hamilton New Zealand

ISBN: (纸本)9781450375177

Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared(1) for further ML research on source code.

关键词： machine learning neural networks code2vec source code code obfuscation

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How I Learned to StopWorrying and Love ChatGPT 21

How I Learned to StopWorrying and Love ChatGPT

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ieee/ACM 21st International conference on Mining Software Repositories (MSR)

作者： Przymus, Piotr Fejzer, Mikolaj Narebski, Jakub Stencel, Krzysztof Nicolaus Copernicus Univ Torun Poland Univ Warsaw Warsaw Poland

ISBN: (纸本)9798350363982;9798400705878

In the dynamic landscape of software engineering, the emergence of ChatGPT-generated code signifies a distinctive and evolving paradigm in development practices. We delve into the impact of interactions with ChatGPT on the software development process, specifically analysing its influence on source code changes. Our emphasis lies in aligning code with ChatGPT conversations, separately analysing the user-provided context of the code and the extent to which the resulting code has been influenced by ChatGPT. Additionally, employing survival analysis techniques, we examine the longevity of ChatGPT-generated code segments in comparison to lines written traditionally. The goal is to provide valuable insights into the transformative role of ChatGPT in software development, illuminating its implications for code evolution and sustainability within the ecosystem.

关键词： ChatGPT DevGPT MSR code Survival analysis

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A Visual Trace analysis Tool for Understanding Feature Scattering

A Visual Trace Analysis Tool for Understanding Feature Scatt...

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15th working conference on Reverse Engineering

作者： Sobreira, Victor Maia, Marcelo de Almeida Univ Fed Uberlandia Dept Comp Sci Uberlandia MG Brazil

ISBN: (纸本)9780769534299

This work proposes a tool for understanding feature, scattering through the graphical visualization of the intersection between features and source code. The tool collects trace events of multi-threaded programs for selected features and shows matrices that help analyzing where those features are implemented. Our claim is that the tool reduces the effort to identify;Where features are implemented and which source code is specific to a feature

关键词： Trace analysis

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Visualizing source code as Comics Using Generative AI

Visualizing Source Code as Comics Using Generative AI

引用

ieee working conference on Software Visualization (VISSOFT)

作者： Heidrich, David Schreiber, Andreas German Aerosp Ctr DLR Inst Software Technol Wessling Germany German Aerosp Ctr DLR Inst Software Technol Cologne Germany

ISBN: (纸本)9798350308297

The architecture and inner structure of software is often only implicitly available in the form of its source code and thus not tangible and intuitively easy to understand for non-programmers and laymen. Our goal is to create visualizations as automatically as possible, with which such people can nevertheless understand the software or parts of the software and get a feel for the structure of the software and how its methods work. Especially for newcomers to software projects, for management or even for students and pupils, it can be helpful to get a nontechnical insight into the software. We use the concept of visualizing information as comics to present aspects of the software as strikingly as possible, as comics are an effective way to present complex systems and interrelationships for certain target groups. For this purpose, we present a method to generate comics from source code. Our semi-automated process is based on generating a prompt for an LLM from source code, which in turn generates a prompt for a comic image generation using the text-to-image model Stable Diffusion. We show that generative AI methods can be used to rapidly generate human-compatible artistic representations from source code. However, further research is needed to validate the understandability of the results.

关键词： visualization software visualization comics generative ai stable diffusion

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