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Showing 1 to 11 of 11 for “"Static Code Analysis"”.
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Identifying and documenting false positive patterns generated by static code analysis tools
Static code analysis tools are known to flag a large number of false positives. A false positive is a warning message generated by a static code analysis tool for a location in the source code that does not have any known problems. This thesis presents our approach and results in identifying and …
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Testing a web application for security using static code analysis and dynamic analysis
… and extensible tool which uses a combination of static and dynamic analysis to identify security vulnerabilities. The framework also supports correlation of results from multiple static analysers. Any static analyser which can output the intra-procedural dataflow of the system being tested and …
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Augmenting large language models with static code analysis for accelerated software development and quality improvements
… of the software development lifecycle using static analysis tools. NGQA integrates detection, grounding, revision, validation, and coordination into a unified workflow through a verification-aware artificial intelligence (AI) agent architecture. The pipeline consists of six steps that combine …
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Testing and Security Related Considerations in Embedded Software
… system is presented in this paper. The use of static code analysis can be used to verify the functionality of embedded software code. Ideally, a software code should be tested in a way that guarantees correct behavior across all possible execution paths. While in practices this is typically …
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A critical analysis of two refactoring tools
This study provides a critical analysis of refactoring by surveying the refactoring tools in IDEA and Eclipse. Ways are discussed to locate targets for refactorings, via detection of code smells from static code analysis in IDEA and during the compilation process in Eclipse. New code smells are …
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Comparative research on code vulnerability detection: Open-source vs. proprietary large language models and LSTM neural network
… Development Lifecycle through a focus on Static Code Analysis as a proactive approach for the discovery and mitigation of security flaws in the development cycle. This research focuses on enhancing vulnerability detection in source code through advanced machine learning techniques, …
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Extending Traditional Static Analysis Techniques to Support Development, Testing and Maintenance of Component-Based Solutions
<p>Traditional static code analysis encompasses a mature set of techniques for helping understand and optimize programs, such as dead code elimination, program slicing, and partial evaluation (code specialization). It is well understood that compared to other program analysis techniques (e.g., …
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Tool Support for the Automatic Analysis of Natural Language User Statements
… for concepts. With the result visualization, the analysis results can be reviewed and the performance can be evaluated via the F1-score on a ground truth. Feed.UVL uses a micro-service architecture, which means it can be extended easily with new methods or functions. The integrated methods are …
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Measurement and Development for Automated Secure Coding Solutions
… efforts, there has also been a rise in source code vulnerabilities. Advanced security tools have been created to identify these vulnerabilities throughout the lifetime of the developer's ecosystem and afterward, before the vulnerabilities are exposed. One such popular method is Static Code …
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The Development of an Economic Feasibility Decision Support System for a Building Automation and Control System
… validation, comprising requirement traceability, static code analysis, boundary testing, and an applied case study supported by expert evaluation. Verification results confirmed that the DSS performs in accordance with its functional and design requirements, with robust computational reliability …
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Pattern recognition for computer security
… is a research area that revolves around the analysis of data and the discovery of patterns that describe aspects of the data. Discriminative learning methods extract prediction models from data that are optimized to predict a target attribute as accurately as possible. Machine-learning …