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About 10,000 papers for “source code analysis”

Bioimage ANalysis Desktop Source Code

Sun, Yi, Tischer, Christian, Hériché, Jean-Karim · Zenodo (CERN European Organization for Nuclear Research) · 2025

The BAND platform offers cloud based desktops accessible with a web browser so that you can work on your data from anywhere with an internet connection. This project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 46/1 – 501864659

Cited by 0Open access

Vulnerable Source Code Detection using SonarCloud Code Analysis

Alifia Puspaningrum, Muhammad Anis Al Hilmi, Darsih et al. · arXiv (Cornell University) · 2023

In Software Development Life Cycle (SDLC), security vulnerabilities are one of the points introduced during the construction stage. Failure to detect software defects earlier after releasing the product to the market causes higher repair costs for the company. So, it decreases the company's reputation, violates user privacy, and causes an unrepairable issue for the application. The introduction of vulnerability detection enables reducing the number of false alerts to focus the limited testing efforts on potentially vulnerable files. UMKM Masa Kini (UMI) is a Point of Sales application to sell any Micro, Small, and Medium Enterprises Product (UMKM). Therefore, in the current work, we analyze the suitability of these metrics to create Machine Learning based software vulnerability detectors for UMI applications. Code is generated using a commercial tool, SonarCloud. Experimental result shows that there are 3,285 vulnerable rules detected.

Cited by 0Open access

Heuristic analysis of source code security

МГТУ им. Н.Э. Баумана, Alexey Markov, В.А. Матвеев et al. · Herald of the Bauman Moscow State Technical University Series Instrument Engineering · 2016

Рассмотрены структурный статический анализ безопасности программного кода и решение задачи обеспечения полноты проводимых проверок.Для реализации полноты проверок выявления уязвимостей программного кода обосновано использование эвристического (сигнатурного) анализа безопасности программ, учитывающего полный спектр классов дефектов программ.Разработана семантическая метамодель описания эвристических алгоритмов выявления уязвимостей и дефектов безопасности программ на различных уровнях представления программного кода.Отмечено, что наиболее актуальными моделями представления программ с позиции безопасности являются абстрактное синтаксическое дерево и абстрактный синтаксический граф.Необходимый уровень быстродействия, формальная простота и наглядность реализации эвристического анализа могут быть достигнуты применением системы продукционных правил

Cited by 2

ODNA Data and Analysis Source Code

Roman Martin, Minh Kien Nguyen, Nick Lowack et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023

This upload contains the analysis source code and processed data for developing the software ODNA, including the machine learning pipeline. ODNA is software for identifying organellar DNA sequences from genome assemblies using genome annotation derived from the Modular Open-Source Genome Annotator (MOSGA).

Cited by 0Open access

ODNA Data and Analysis Source Code

Roman Martin, Minh Kien Nguyen, Nick Lowack et al. · Zenodo (CERN European Organization for Nuclear Research) · 2023

This upload contains the analysis source code and processed data for developing the software ODNA, including the machine learning pipeline. ODNA is software for identifying organellar DNA sequences from genome assemblies using genome annotation derived from the Modular Open-Source Genome Annotator (MOSGA).

Cited by 0Open access

Quality analysis of source code comments

Daniela Steidl, Benjamin Hummel, Elmar Juergens · 2013

A significant amount of source code in software systems consists of comments, i. e., parts of the code which are ignored by the compiler. Comments in code represent a main source for system documentation and are hence key for source code understanding with respect to development and maintenance. Although many software developers consider comments to be crucial for program understanding, existing approaches for software quality analysis ignore system commenting or make only quantitative claims. Hence, current quality analyzes do not take a significant part of the software into account. In this work, we present a first detailed approach for quality analysis and assessment of code comments. The approach provides a model for comment quality which is based on different comment categories. To categorize comments, we use machine learning on Java and C/C++ programs. The model comprises different quality aspects: by providing metrics tailored to suit specific categories, we show how quality aspects of the model can be assessed. The validity of the metrics is evaluated with a survey among 16 experienced software developers, a case study demonstrates the relevance of the metrics in practice.

Cited by 195

B.4 Source-Code Analysis Tool

The Previous · 2007

helps to determine whether the mandatory access control rules are implemented correctly. (4) It helps determine the source code locations where audit code, delays, and randomization code should be placed for handling covert channels. (5) The method is a fully automated search for potential covert storage channels. [He and Gligor90] In addition to the advantages stated above, a code level examination for covert storage channels seems to be stronger than the specification level searches provided by FDM. GVE, and HDM, since a formal method has not been developed for showing the correspondence of the specification to the code. The code-level search for covert storage channels is theoretically similar to specificationlevel approaches and is conducted as follows: (1) Select the set kernel primitives (TCB interface calls) to be analyzed. (2) Determine the visibility/alterability of kernel variables when primitives are invoked. (4) Construction of information flow analogues for each routine

Cited by 0

Compiler Optimizations and Source Code Analysis

Lóki Gábor · SZTE Repository of Dissertations (University of Szeged) · 2024

Software optimization is a key element in improving the overall performance and efficiency of computer programs.As computer architectures evolve and the demand for faster and more robust software applications increases, the role of code compilers is increasingly important.A code compiler is a fundamental tool for translating human-readable source code into machine-readable binary code, facilitating software execution on the target computer system.The optimized binary code, achieved through advanced compiler technology, plays a key role in meeting the challenges of today's computer landscape.The importance of optimizing binary code cannot be overstated.Optimized binary code allows software to run faster and consume fewer storage resources, thus increasing the overall performance of computer programs.In today's computing environment, software efficiency can be a decisive factor in improving competitiveness.Optimization techniques use a wide range of strategies during the compilation process, including, but not limited to, loop transformations, register allocations, instruction scheduling, inter-and intra-procedural analysis, and code transformations.Together, these technologies are designed to reduce execution time, memory consumption, energy consumption, and executable code size, which have real benefits in various areas, such as real-time systems, embedded devices, scientific simulations, and high-performance computing.

Cited by 0Open access

Source Code Analysis - A Beginner's Guide

Ruef, Marc · Zenodo (CERN European Organization for Nuclear Research) · 2014

A very powerful method of a security check is the Source Code Analysis (SCA). In such an analysis, the code of specific software is examined to determine vulnerabilities. This article will show you the basics of such a review.

Cited by 0Open access

iView-iSCAT image analysis source code

Park, Jin-Sung, Lee, Il-Buem, Moon, Hyeon-Min et al. · Figshare · 2023

Matlab source code for generating SBR- and TD-iSCAT images from raw iSCAT images

Cited by 0Open access

DeepStrataAge: Source Code and Analysis Scripts

Aaron Lin · Zenodo (CERN European Organization for Nuclear Research) · 2025

Complete code implementation for DeepStrataAge, a deep learning epigenetic clock that predicts chronological age from DNA methylation data. Model Overview The model uses an ensemble of three deep neural networks trained via leave-one-cohort-out (LOCO) cross-validation on TruDiagnostic data plus three external validation cohorts (Mass General Brigham Biobank, Biomarkers of Aging, and CIBMTR). Each model features 2 hidden layers with 1024 units, ReLU activation, and 0.25 dropout, with hyperparameters optimized via Optuna. Repository Contents DeepStrataAge Package: Model architecture, prediction interface, configuration files, example usage comparing DeepStrataAge with established clocks (Horvath, Hannum, PhenoAge), and comprehensive documentation feature_selection.R: Sex-stratified, age-binned CpG selection via Spearman correlations across four age strata (10-40, 40-50, 50-70, 70-90 years), selecting top 1% most correlated CpGs per stratum predict_age_and_SHAP.py: Generates age predictions on held-out test sets and computes SHAP values to quantify individual CpG contributions to model predictions Requirements Python: torch, pandas, numpy, biolearn, shap, scikit-learnR: dplyr, parallel Licensing Source code (MIT License): All .py, .R, and other source files included in this record (including those contained within DeepStratv3.zip) are released under the MIT License and may be used, modified, and redistributed subject to the MIT terms. Trained model artifacts (CC BY-NC 4.0): The trained model weight files and derived model artifacts included in this record are released under CC BY-NC 4.0 (attribution required; commercial use is not permitted without separate permission from the rights holder). For avoidance of doubt: the CC BY-NC 4.0 restriction applies only to the trained weights/artifacts, not to the source code.

Cited by 0Open access

Gist Generation Analysis Source Code/Scripts

Koh, Zhao Hui, Chuyin Zhang · OSF Preprints (OSF Preprints) · 2019

Source code/scripts (mostly in R/Python) created for analysis in the gist generation project.

Cited by 0Open access

Static Source Code Analysis using OCL

Mirko Seifert, Roland Samlaus · Technische Universität Berlin – Universitätsbibliothek · 2008

The majority of artifacts created during software development are representations of programs in textual syntax. Although graphical descriptions are becoming more widespread, source code is still indispensable. To obtain programs that behave correctly and adhere to given coding conventions, source code must be analyzed - preferably using automated tools. Building source code analyzers has a long tradition and various mature tools exist to check code written in conventional languages, such as Java or C. As new languages emerge (e.g., Domain Specific Languages) these tools can not be applied and building a tool for each language does not seem feasible either. This paper investigates how meta models for textual languages and the Object Constraint Language can enable generic static source code analysis for arbitrary languages. The presented approach is evaluated using three languages (Java, SQL and a DSL for state machines).

Cited by 14Open access

Include-Analysis for C++ Source Code

Bernehed, Aiko · elib (German Aerospace Center) · 2022

The C++ programming language is highly versatile and leaves many aspects of code organization to programmers, but provides functionality for code separation into source and header files. As projects have grown larger in size and C++ has found its way into critical infrastructure, the community has started developing a host of best-practice guidelines for code organization. Since these are not programmatically enforced during development, static include analysis is an excellent tool for checking programmers adherence to these self-imposed guidelines. In this work, the Axivion Suite by the Axivion GmbH is used to develop a set of rules that enforce a number of guidelines as they may be encountered in real-world applications. The Axivion Suite uses a proprietary compiler to gather analysis data on existing C++ source code of a given project. The rules presented here are enforced by running specially developed Python scripts on the analysis data generated by the Axivion compiler. Finally, the entire set of rules is used to analyze an existing application, "Notepad++", and the results are compared to Axivion's proprietary rules, gathered under the name of Generic stylechecks. "Notepad++" consists of a core program and the Scintilla and Boost libraries, all three of which exhibit a host of different issues. Overall, the rules developed here uncover 3,673 and Axivion's rules 3,890 violations. The minutiae of these results are discussed and final conclusions about this work and Axivion's implementation drawn.

Cited by 0

DeepStrataAge: Source Code and Analysis Scripts

Aaron Lin · Zenodo (CERN European Organization for Nuclear Research) · 2025

Complete code implementation for DeepStrataAge, a deep learning epigenetic clock that predicts chronological age from DNA methylation data. Model Overview The model uses an ensemble of three deep neural networks trained via leave-one-cohort-out (LOCO) cross-validation on TruDiagnostic data plus three external validation cohorts (Mass General Brigham Biobank, Biomarkers of Aging, and CIBMTR). Each model features 2 hidden layers with 1024 units, ReLU activation, and 0.25 dropout, with hyperparameters optimized via Optuna. Repository Contents DeepStrataAge Package: Model architecture, prediction interface, configuration files, example usage comparing DeepStrataAge with established clocks (Horvath, Hannum, PhenoAge), and comprehensive documentation feature_selection.R: Sex-stratified, age-binned CpG selection via Spearman correlations across four age strata (10-40, 40-50, 50-70, 70-90 years), selecting top 1% most correlated CpGs per stratum predict_age_and_SHAP.py: Generates age predictions on held-out test sets and computes SHAP values to quantify individual CpG contributions to model predictions Requirements Python: torch, pandas, numpy, biolearn, shap, scikit-learnR: dplyr, parallel Licensing Source code (MIT License): All .py, .R, and other source files included in this record (including those contained within DeepStratv3.zip) are released under the MIT License and may be used, modified, and redistributed subject to the MIT terms. Trained model artifacts (CC BY-NC 4.0): The trained model weight files and derived model artifacts included in this record are released under CC BY-NC 4.0 (attribution required; commercial use is not permitted without separate permission from the rights holder). For avoidance of doubt: the CC BY-NC 4.0 restriction applies only to the trained weights/artifacts, not to the source code.

Cited by 0Open access