Source Code Examples Research Articles

SpatialOne: end-to-end analysis of visium data at scale.

Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose. Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale. SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image.

Empowering Internet Management: A User-Friendly Website Blocking Tool with Cross-Platform Compatibility

Abstract: In this paper, we’ll introduce a superb Python software tool that uses Tkinter’s graphical user interface (GUI) to impose restrictions on website accessibility. By modifying the fundamental”hosts file” on your computer, you can now easily get around restrictions after installing this revolutionary application. An improved user interface ensures greater usability, even for those with little pro- gramming experience. This application’s reliance on the tkinter library for GUI construction, which ensures compatibility across various platforms, is what makes it unique. This study delves into the finer points of implementation, offers examples of source code, and makes suggestions for improving website blocking administration.

CoCoFuzzing: Testing Neural Code Models With Coverage-Guided Fuzzing

Deep learning-based code processing models have shown good performance for tasks such as predicting method names, summarizing programs, and comment generation. However, despite the tremendous progress, deep learning models are often prone to adversarial attacks, which can significantly threaten the robustness and generalizability of these models by leading them to misclassification with unexpected inputs. To address the above issue, many deep learning testing approaches have been proposed, however, these approaches mainly focus on testing deep learning applications in the domains of image, audio, and text analysis, etc., which cannot be directly applied to neural models for code due to the unique properties of programs. In this paper, we propose a coverage-based fuzzing framework, CoCoFuzzing, for testing deep learning-based code processing models. In particular, we first propose ten mutation operators to automatically generate valid and semantically preserving source code examples as tests; then we propose a neuron coverage-based approach to guide the generation of tests. We investigate the performance of CoCoFuzzing on three state-of-the-art neural code models, i.e., NeuralCodeSum, CODE2SEQ, and CODE2VEC. Our experiment results demonstrate that CoCoFuzzing can generate valid and semantically preserving source code examples for testing the robustness and generalizability of these models and improve the neuron coverage. Moreover, these tests can be used to improve the performance of the target neural code models through adversarial retraining.

Towards Robustness of Deep Program Processing Models—Detection, Estimation, and Enhancement

Deep learning (DL) has recently been widely applied to diverse source code processing tasks in the software engineering (SE) community, which achieves competitive performance (e.g., accuracy). However, the robustness, which requires the model to produce consistent decisions given minorly perturbed code inputs, still lacks systematic investigation as an important quality indicator. This article initiates an early step and proposes a framework CARROT for robustness detection, measurement, and enhancement of DL models for source code processing. We first propose an optimization-based attack technique CARROT A to generate valid adversarial source code examples effectively and efficiently. Based on this, we define the robustness metrics and propose robustness measurement toolkit CARROT M , which employs the worst-case performance approximation under the allowable perturbations. We further propose to improve the robustness of the DL models by adversarial training (CARROT T ) with our proposed attack techniques. Our in-depth evaluations on three source code processing tasks (i.e., functionality classification, code clone detection, defect prediction) containing more than 3 million lines of code and the classic or SOTA DL models, including GRU, LSTM, ASTNN, LSCNN, TBCNN, CodeBERT, and CDLH, demonstrate the usefulness of our techniques for ❶ effective and efficient adversarial example detection, ❷ tight robustness estimation, and ❸ effective robustness enhancement.

NCMW: A Python Package to Analyze Metabolic Interactions in the Nasal Microbiome.

The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), including Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae, Moraxella catarrhalis, and Staphylococcus aureus, which occasionally turn into pathogens causing infectious diseases, while the contribution of many nasal microorganisms to human health remains undiscovered. To better understand the composition of the nasal microbiome community, we create a workflow of the community model, which mimics the human nasal environment. To address this challenge, constraint-based reconstruction of biochemically accurate genome-scale metabolic models (GEMs) networks of microorganisms is mandatory. Our workflow applies constraint-based modeling (CBM), simulates the metabolism between species in a given microbiome, and facilitates generating novel hypotheses on microbial interactions. Utilizing this workflow, we hope to gain a better understanding of interactions from the metabolic modeling perspective. This article presents nasal community modeling workflow (NCMW)—a python package based on GEMs of species as a starting point for understanding the composition of the nasal microbiome community. The package is constructed as a step-by-step mathematical framework for metabolic modeling and analysis of the nasal microbial community. Using constraint-based models reduces the need for culturing species in vitro, a process that is not convenient in the environment of human noses. Availability: NCMW is freely available on the Python Package Index (PIP) via pip install NCMW. The source code, documentation, and usage examples (Jupyter Notebook and example files) are available at https://github.com/manuelgloeckler/ncmw.

ARTEMIS: Ab initio restructuring tool enabling the modelling of interface structures

A program, ARTEMIS, has been developed for the study of interface structures. This software allows for the generation of interfaces by identifying lattice matches between two parent crystal structures. To allow for further exploration of the energetic space of the interface, multiple surface terminations parallel to the Miller plane, interface alignments and intermixings are used to generate sets of potential interfaces for each lattice match. These interface structures can then be used in atomic simulations to determine the most energetically favourable interface. The software reported here can help to both drastically reduce the work of generating and exploring interfaces, and aid in understanding of how the interface structure influences the subsequent properties. Using several test cases, we demonstrate how ARTEMIS can both identify the location of an interface in existing structures, and also predict an optimum interface separation based upon the parents’ atomic structures, which aims to accelerate and inform the study of interface science. Program summaryProgram Title: ARTEMISCPC Library link to program files:http://dx.doi.org/10.17632/5bcrh67xty.1Developer’s repository link:http://www.artemis-materials.co.uk/Licensing provisions: CC BY NC 3.0Programming language: Fortran 2003Nature of problem: Construction and identification of the interface between any two crystals. Complicating factors include the choice of Miller planes, alignment of the two crystals and potential intermixing of them.Solution method: This problem is tackled by generating sets of interface structures that allow the user to explore the energy space using atomic simulations in order to identify the most favourable interface to form between two such crystals.Additional comments: The source code and working examples can be found in the compressed file obtainable from http://www.artemis-materials.co.uk/. The code has been tested and developed using the GNU 7.2.0 and the Intel 17.0.4 Fortran compilers on Unix/Linux operating systems.

Generating Adversarial Examples for Holding Robustness of Source Code Processing Models

Automated processing, analysis, and generation of source code are among the key activities in software and system lifecycle. To this end, while deep learning (DL) exhibits a certain level of capability in handling these tasks, the current state-of-the-art DL models still suffer from non-robust issues and can be easily fooled by adversarial attacks.Different from adversarial attacks for image, audio, and natural languages, the structured nature of programming languages brings new challenges. In this paper, we propose a Metropolis-Hastings sampling-based identifier renaming technique, named \fullmethod (\method), which generates adversarial examples for DL models specialized for source code processing. Our in-depth evaluation on a functionality classification benchmark demonstrates the effectiveness of \method in generating adversarial examples of source code. The higher robustness and performance enhanced through our adversarial training with \method further confirms the usefulness of DL models-based method for future fully automated source code processing.

µ -ANT: semantic microaggregation-based anonymization tool.

Detailed patient data are crucial for medical research. Yet, these healthcare data can only be released for secondary use if they have undergone anonymization. We present and describe µ-ANT, a practical and easily configurable anonymization tool for (healthcare) data. It implements several state-of-the-art methods to offer robust privacy guarantees and preserve the utility of the anonymized data as much as possible. µ-ANT also supports the heterogenous attribute types commonly found in electronic healthcare records and targets both practitioners and software developers interested in data anonymization. (source code, documentation, executable, sample datasets and use case examples) https://github.com/CrisesUrv/microaggregation-based_anonymization_tool.

APIUaaS: a reference architecture for facilitating API usage from a data analytics perspective

Source code examples are key resources for software developers to learn application programming interfaces (APIs) and to understand corresponding usage patterns. Developers usually have to utilise, evaluate and understand code examples from multiple sources, which involve heavy manually processing efforts. To reduce such efforts, there has been growing interest in developing source code mining and recommendation systems. This study proposes API usage as a service (APIUaaS), a reference architecture for facilitating API usage, which allows infrastructures to be built for recommending proper API code examples based on semi-automatic data analytics. This reference architecture contains five logical layers and six global-level architectural concerns. API queries are accepted from programmers, and corresponding code example candidates are extracted from the data sources layer. The detailed structural links between API elements and source codes are captured and stored in the data model & code assets layer. During the recommendation phase, API usages mining, clustering and ranking algorithms are enabled in the knowledge discover & intelligent model layer. Services such as code assist and bug detection are assembled in the API usage services layer. Finally, the authors evaluate APIUaaS from three perspectives: rationality, feasibility, and usability.

SignalMaster Update 2019

SignalMaster is a portable open speech and signal processing system that was developed specifically for researchers interested in developing their own real-time signal processing strategies. The system can be used for a wide range of research applications including development of hearing aid amplification and processing strategies and recording of evoked responses. SignalMaster uses a TMS320C6748 Digital Signal Processor (DSP) and provides two input and two output channels with 32 bit Analog-to-Digital (AD) and Digital-to-Analog (DA) convertors. Lithium-ion batteries provide up to 10 hours of freestanding operation. The system is available in two hardware platforms, the standard SignalMaster and the new SignalMasterLite, more compact and light weight for portable applications. The system may also be used while connected to a personal computer (PC) or Android Table via Bluetooth for additional control of experiments, off-line processing and data storage. A DLL provides functions for user developed applications to communicate with the hardware and be upload C language programs. This allows for different applications to be uploaded in order to reconfigure the processing algorithms being executed for any experiment at any time. DSP, PC-based and Android Table software source code examples provide users with the ability to develop their own user specific applications.

OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers.

MotivationIdentification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes.ResultsWe have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method can identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. Our results indicate that OncodriveCLUSTL can be applied to the analysis of non-coding genomic elements and non-human mutations data.Availability and implementationOncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public License.Supplementary information Supplementary data are available at Bioinformatics online.

Stochastic Automatic Differentiation - AAD for Monte-Carlo Simulations (Presentation at the 13th Fixed Income Conference) (Presentation Slides)

The presentation illustrates the theory and application of a Stochastic Automatic Differentiation (Stochastic Algorithmic Differentiation). The Stochastic Automatic Differentiation allows to improve performance and reduce the memory requirements of an AD algorithm by exploiting the stochastic nature of the random variables. Considering an Expected Stochastic Automatic Differentiation we can give a modified AD algorithm which allows efficient adjoint (backward) automatic differentiation of evaluations containing non-pathwise operators like the conditional expectation operator. The algorithm can be applied to calculate forward sensitivities, i.e.~differentiation with respect to stochastic intermediate (future) values. This can be used for the efficient calculation of MVAs in finance. The implementation can be performed using only few additional lines of code. We provide source code and numerical examples.

The Role of Extrafoveal Vision in Source Code Comprehension.

Understanding software engineers' behaviour plays a vital role in the software development industry. It also provides helpful guidelines for teaching and learning. In this article, we conduct a study of the extrafoveal vision and its role in information processing. This is a new perspective on source code comprehension. Despite its major importance, the extrafoveal vision has been largely ignored by previous studies. The available research has been focused entirely on the foveal information processing and the gaze fixation position. In this work, we share the results of a gaze-contingent study of source code comprehension by expert ( N = 12) and novice ( N = 12) programmers in conditions of the restricted extrafoveal vision. The window-moving paradigm was employed to restrict the extrafoveal area of vision as participants comprehend two source code examples. The results indicate that the semantic preview allowed by the extrafoveal vision provides tangible benefits to expert programmers. When the experts could not use the semantic information from the extrafoveal area, their fixation duration increased to duration similar to novices. The experts' performance dropped in the restricted-view mode, and they required more time to solve the tasks.

Spectral identification of topological domains.

Topological domains have been proposed as the backbone of interphase chromosome structure. They are regions of high local contact frequency separated by sharp boundaries. Genes within a domain often have correlated transcription. In this paper, we present a computational efficient spectral algorithm to identify topological domains from chromosome conformation data (Hi-C data). We consider the genome as a weighted graph with vertices defined by loci on a chromosome and the edge weights given by interaction frequency between two loci. Laplacian-based graph segmentation is then applied iteratively to obtain the domains at the given compactness level. Comparison with algorithms in the literature shows the advantage of the proposed strategy. An efficient algorithm is presented to identify topological domains from the Hi-C matrix. The Matlab source code and illustrative examples are available at http://bionetworks.ccmb.med.umich.edu/ : indikar@med.umich.edu Supplementary data are available at Bioinformatics online.

Learning to rank code examples for code search engines

Source code examples are used by developers to implement unfamiliar tasks by learning from existing solutions. To better support developers in finding existing solutions, code search engines are designed to locate and rank code examples relevant to user's queries. Essentially, a code search engine provides a ranking schema, which combines a set of ranking features to calculate the relevance between a query and candidate code examples. Consequently, the ranking schema places relevant code examples at the top of the result list. However, it is difficult to determine the configurations of the ranking schemas subjectively. In this paper, we propose a code example search approach that applies a machine learning technique to automatically train a ranking schema. We use the trained ranking schema to rank candidate code examples for new queries at run-time. We evaluate the ranking performance of our approach using a corpus of over 360,000 code snippets crawled from 586 open-source Android projects. The performance evaluation study shows that the learning-to-rank approach can effectively rank code examples, and outperform the existing ranking schemas by about 35.65 % and 48.42 % in terms of normalized discounted cumulative gain (NDCG) and expected reciprocal rank (ERR) measures respectively.

Mining usage patterns for the Android API

5 API methods are not used alone, but in groups and following some patterns. However, despite being a key information for API users, most usage patterns are not explicitly described in official API documents. In this article, we report a study to evaluate the feasibility of automatically enriching API documents with information on usage patterns. For this purpose, we extract and analyze 1,952 usage patterns, from a set of 396 Android applications. As part of our findings, we report that the Android API has many undocumented and non-trivial usage patterns, which can be inferred using association rule mining algorithms. We also describe a field study where a version of the original Android documentation was instrumented with the extracted usage patterns and source code examples. During 17 months, this documentation received 77,863 visits from real Android developers. . 6

Studying Students' Attitudes on Using Examples of Game Source Code for Learning Programming

Games for learning are currently used in several disciplines for motivating students and enhancing their learning experience. This new approach of technology-enhanced learning has attracted researchers' and instructors' attention in the area of programming that is one of the most cognitively demanding fields in Computer Science. Several educational, or else serious, games for learning programming have been developed and the first results of their evaluation as a means of learning are quite positive. In this paper, we propose using arcade games as a means for learning programming. Based on this approach students first play a simple game, such as Snake or Tetris, study its code and then extend it. In a pilot study carried out in the context of an undergraduate programming course, students studied the source code of the well-known game Snake and ex- tended it with new functionalities. The analysis of students' replies in a questionnaire showed that using arcade games as a means of learning programming concepts enhances students' motivation for learning programming, supports them in comprehending complex concepts and engages them in carrying out programming activities.

NGS++: a library for rapid prototyping of epigenomics software tools

The development of computational tools to enable testing and analysis of high-throughput-sequencing data is essential to modern genomics research. However, although multiple frameworks have been developed to facilitate access to these tools, comparatively little effort has been made at implementing low-level programming libraries to increase the speed and ease of their development. We propose NGS++, a programming library in C++11 specialized in manipulating both next-generation sequencing (NGS) datasets and genomic information files. This library allows easy integration of new formats and rapid prototyping of new functionalities with a focus on the analysis of genomic regions and features. It offers a powerful, yet versatile and easily extensible interface to read, write and manipulate multiple genomic file formats. By standardizing the internal data structures and presenting a common interface to the data parser, NGS++ offers an effective framework for epigenomics tool development. NGS++ was written in C++ using the C++11 standard. It requires minimal efforts to build and is well-documented via a complete docXygen guide, online documentation and tutorials. Source code, tests, code examples and documentation are available via the website at http://www.ngsplusplus.ca and the github repository at https://github.com/NGS-lib/NGSplusplus. nicolas.gevry@usherbrooke.ca or arnaud.droit@crchuq.ulaval.ca.

CAGO: A Software Tool for Dynamic Visual Comparison and Correlation Measurement of Genome Organization

CAGO (Comparative Analysis of Genome Organization) is developed to address two critical shortcomings of conventional genome atlas plotters: lack of dynamic exploratory functions and absence of signal analysis for genomic properties. With dynamic exploratory functions, users can directly manipulate chromosome tracks of a genome atlas and intuitively identify distinct genomic signals by visual comparison. Signal analysis of genomic properties can further detect inconspicuous patterns from noisy genomic properties and calculate correlations between genomic properties across various genomes. To implement dynamic exploratory functions, CAGO presents each genome atlas in Scalable Vector Graphics (SVG) format and allows users to interact with it using a SVG viewer through JavaScript. Signal analysis functions are implemented using R statistical software and a discrete wavelet transformation package waveslim. CAGO is not only a plotter for generating complex genome atlases, but also a platform for exploring genome atlases with dynamic exploratory functions for visual comparison and with signal analysis for comparing genomic properties across multiple organisms. The web-based application of CAGO, its source code, user guides, video demos, and live examples are publicly available and can be accessed at http://cbs.ym.edu.tw/cago.

JsPhyloSVG: A Javascript Library for Visualizing Interactive and Vector-Based Phylogenetic Trees on the Web

BackgroundMany software packages have been developed to address the need for generating phylogenetic trees intended for print. With an increased use of the web to disseminate scientific literature, there is a need for phylogenetic trees to be viewable across many types of devices and feature some of the interactive elements that are integral to the browsing experience. We propose a novel approach for publishing interactive phylogenetic trees.Methods/Principal FindingsWe present a javascript library, jsPhyloSVG, which facilitates constructing interactive phylogenetic trees from raw Newick or phyloXML formats directly within the browser in Scalable Vector Graphics (SVG) format. It is designed to work across all major browsers and renders an alternative format for those browsers that do not support SVG. The library provides tools for building rectangular and circular phylograms with integrated charting. Interactive features may be integrated and made to respond to events such as clicks on any element of the tree, including labels.Conclusions/SignificancejsPhyloSVG is an open-source solution for rendering dynamic phylogenetic trees. It is capable of generating complex and interactive phylogenetic trees across all major browsers without the need for plugins. It is novel in supporting the ability to interpret the tree inference formats directly, exposing the underlying markup to data-mining services. The library source code, extensive documentation and live examples are freely accessible at www.jsphylosvg.com.