Git Repository Research Articles

Predicting academic success through students’ interaction with Version Control Systems

Abstract Version Control Systems are commonly used by Information and communication technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of this work is to evaluate if the academic success of students may be predicted by monitoring their interaction with a Version Control System. In order to do so, we have built a Machine Learning model which predicts student results in a specific practical assignment of the Operating Systems Extension subject, from the second course of the degree in Computer Science of the University of León, through their interaction with a Git repository. To build the model, several classifiers and predictors have been evaluated. In order to do so, we have developed Model Evaluator (MoEv), a tool to evaluate Machine Learning models in order to get the most suitable for a specific problem. Prior to the model development, a feature selection from input data is done. The resulting model has been trained using results from 2016–2017 course and later validated using results from 2017–2018 course. Results conclude that the model predicts students’ success with a success high percentage.

Optimization-based Feedback Manipulation Through an Array of Ultrasonic Transducers

In this paper we document a novel laboratory experimental platform for noncontact planar manipulation (positioning) of millimeter-scale objects using acoustic pressure. The manipulated objects are either floating on a water surface or rolling on a solid surface. The pressure field is shaped in real time through an 8-by-8 array (matrix) of ultrasonic transducers. The transducers are driven with square voltages whose phase-shifts are updated periodically every 20 milliseconds based on the difference between the desired and true (estimated from video) position. Numerical optimization is used within every period of a discrete-time feedback loop to determine the phase shifts for the voltages. The platform can be used as an affordable testbed for algorithms for non-contact manipulation through arrays of actuators as all the design and implementation details for the presented platform are shared with the public through a dedicated git repository. The platform can certainly be extended towards higher numbers of simultaneously yet independently manipulated objects and larger manipulation areas by expanding the transducer array.

A Git-based Conditions Database backend for LHCb

LHCb has been using the CERN/IT developed Conditions Database library COOL for several years, during LHC Run 1 and Run 2. With the opportunity window of the second long shutdown of LHC, in preparation for Run 3 and the upgraded LHCb detector, we decided to investigate alternatives to COOL as Conditions Database backend. In particular, given our conditions and detector description data model, we investigated the possibility of reusing the internal Git repository database as a conditions storage, and we adopted it since 2017 data taking. The adoption of Git gave us improved performance, smaller storage size and simplified maintenance and deployment. In this paper we describe the implementation of our Git Conditions Database and the way it simplified our detector description and conditions development workflow.

Managing a Cray supercomputer as a git branch

SummaryModern software engineering/DevOps techniques applied to Cray XC Series systems management enable higher fidelity translation from test to production environments, reduce administration costs by avoiding duplicate efforts, and increase reliability. This can be done by using a git repository to track and manage all system configurations on the SMW(s) (System Management Workstation) and then adapt a gitflow‐like development methodology. Using git improves change management with peer review of changes, identification of who made a change, knowing why a change was made, facilitating easier reversion of bad changes, and enables a workflow using the ideas from gitflow with individual branches for preparing, maintaining, and recording releases. Using branches enables multiple contributors to work on different types of changes without getting unexpected changes from another feature area. We will describe a process similar to that used at NERSC (National Energy Research Scientific Computing Center) to extract/abstract configuration data, manage it using git, and then re‐apply changes to the SMW(s).

Change profile analysis of open-source software systems to understand their evolutionary behavior

Source code management systems (such as git) record changes to code repositories of Open-Source Software (OSS) projects. The metadata about a change includes a change message to record the intention of the change. Classification of changes, based on change messages, into different change types has been explored in the past to understand the evolution of software systems from the perspective of change size and change density only. However, software evolution analysis based on change classification with a focus on change evolution patterns is still an open research problem. This study examines change messages of 106 OSS projects, as recorded in the git repository, to explore their evolutionary patterns with respect to the types of changes performed over time. An automated keyword-based classifier technique is applied to the change messages to categorize the changes into various types (corrective, adaptive, perfective, preventive, and enhancement). Cluster analysis helps to uncover distinct change patterns that each change type follows. We identify three categories of 106 projects for each change type: high activity, moderate activity, and low activity. Evolutionary behavior is different for projects of different categories. The projects with high and moderate activity receive maximum changes during 76–81 months of the project lifetime. The project attributes such as the number of committers, number of files changed, and total number of commits seem to contribute the most to the change activity of the projects. The statistical findings show that the change activity of a project is related to the number of contributors, amount of work done, and total commits of the projects irrespective of the change type. Further, we explored languages and domains of projects to correlate change types with domains and languages of the projects. The statistical analysis indicates that there is no significant and strong relation of change types with domains and languages of the 106 projects.

An empirical study on how project context impacts on code cloning

AbstractCode cloning can seriously affect software quality. Code clones are various fragments of syntactically or semantically equivalent code. Some authors argue that code clones have a negative impact on maintainability and understandability, since clones propagate defects and make it mandatory to pay attention to several copies. However, other authors believe clones are not necessarily bad, since self‐admitted clones favor system stability and allow developers to move projects forward. Although some root causes and effects of cloning have been widely studied, there is not much relevant work analyzing how certain projects context factors impact on code cloning. This work presents an empirical validation of six open source projects by considering certain factors from Git repositories measured throughout a total of 70 releases for the 6 systems. The factors analyzed were the number of commits and committers per release, the average size of the commits and the size of the system in each release. The main conclusion obtained from the study is that, while the number of commits and committers and the system size do not significantly affect cloning, larger commits lead to a higher cloning ratio. These insights contribute to predicting and preventing code cloning, thus enabling a software quality improvement.

Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering.

MotivationMathematical models have become standard tools for the investigation of cellular processes and the unraveling of signal processing mechanisms. The parameters of these models are usually derived from the available data using optimization and sampling methods. However, the efficiency of these methods is limited by the properties of the mathematical model, e.g. non-identifiabilities, and the resulting posterior distribution. In particular, multi-modal distributions with long valleys or pronounced tails are difficult to optimize and sample. Thus, the developement or improvement of optimization and sampling methods is subject to ongoing research.ResultsWe suggest a region-based adaptive parallel tempering algorithm which adapts to the problem-specific posterior distributions, i.e. modes and valleys. The algorithm combines several established algorithms to overcome their individual shortcomings and to improve sampling efficiency. We assessed its properties for established benchmark problems and two ordinary differential equation models of biochemical reaction networks. The proposed algorithm outperformed state-of-the-art methods in terms of calculation efficiency and mixing. Since the algorithm does not rely on a specific problem structure, but adapts to the posterior distribution, it is suitable for a variety of model classes.Availability and implementationThe code is available both as Supplementary Material and in a Git repository written in MATLAB.Supplementary information Supplementary data are available at Bioinformatics online.

An Analysis of Merge Conflicts and Resolutions in Git-Based Open Source Projects

Version control systems such as Git support parallel collaborative work and became very widespread in the open-source community. Whilst Git offers some very interesting features, resolving conflicts that arise during synchronisation of parallel changes is a time-consuming task. In this paper we present an analysis of concurrency and conflicts in official Git repository of four projects: Rails, IkiWiki, Samba and Linux Kernel. We analyse the collaboration process of these projects at specific periods revealing how change integration and conflict rates vary during project development life-cycle. We also analyse how often users decide to rollback to previous document version when the integration process generates conflicts. Finally, we discuss the mechanism adopted by Git to consider changes made on two continuous lines as conflicting.

Development and evaluation of a deep learning model for protein-ligand binding affinity prediction.

MotivationStructure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to ‘learn’ to extract features that are relevant for the task at hand.ResultsWe have developed a novel deep neural network estimating the binding affinity of ligand–receptor complexes. The complex is represented with a 3D grid, and the model utilizes a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner. Our network was tested on the CASF-2013 ‘scoring power’ benchmark and Astex Diverse Set and outperformed classical scoring functions.Availability and implementationThe model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy.Supplementary information Supplementary data are available at Bioinformatics online.

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions.

In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes. Synonym expansion approaches apply thesauri, ontologies, and query logs. All need laborious curation and maintenance. Furthermore, access to query logs is in general restricted. Approaches that infer related queries by their query profile like research field, geographic location, co-authorship, affiliation etc. require user’s registration and its public accessibility that contradict privacy concerns. To overcome these drawbacks, we implemented LAILAPS-QSM, a machine learning approach that reconstruct possible linguistic contexts of a given keyword query. The context is referred from the text records that are stored in the databases that are going to be queried or extracted for a general purpose query suggestion from PubMed abstracts and UniProt data. The supplied tool suite enables the pre-processing of these text records and the further computation of customized distributed word vectors. The latter are used to suggest alternative keyword queries. An evaluated of the query suggestion quality was done for plant science use cases. Locally present experts enable a cost-efficient quality assessment in the categories trait, biological entity, taxonomy, affiliation, and metabolic function which has been performed using ontology term similarities. LAILAPS-QSM mean information content similarity for 15 representative queries is 0.70, whereas 34% have a score above 0.80. In comparison, the information content similarity for human expert made query suggestions is 0.90. The software is either available as tool set to build and train dedicated query suggestion services or as already trained general purpose RESTful web service. The service uses open interfaces to be seamless embeddable into database frontends. The JAVA implementation uses highly optimized data structures and streamlined code to provide fast and scalable response for web service calls. The source code of LAILAPS-QSM is available under GNU General Public License version 2 in Bitbucket GIT repository: https://bitbucket.org/ipk_bit_team/bioescorte-suggestion

Empirical analysis of change metrics for software fault prediction

A quality assurance activity, known as software fault prediction, can reduce development costs and improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are performed on different versions of Eclipse projects and change metrics are extracted from the GIT repositories. In addition to the existing change metrics, several new change metrics are defined and collected from the Eclipse project repository. Machine learning algorithms are applied in conjunction with the change and source code metrics to build fault prediction models. The classification model with new change metrics performs better than the models using existing change metrics. In this work, the experimental results demonstrate that change metrics have a positive impact on the performance of fault prediction models, and high-performance models can be built with several change metrics.

Scripts for: Inter-chromosomal coupling between vision and pigmentation genes during genomic divergence

This git repository contains all the scripts needed to reproduce our results from the study Inter-chromosomal coupling between vision and pigmentation genes during genomic divergence from raw data to figures. It contains all scripts referenced within the materials & methods section. Related datasets are deposited at ENA (raw sequencing data, acession nr. PRJEB27858) and dryad (genome annotation & genotypes, doi:10.5061/dryad.pg8q56g).

Are suggestions from coupled file changes useful for perfective maintenance tasks?

Background Software maintenance is an important activity in the development process where maintenance team members leave and new members join over time. The identification of files which are changed together frequently has been proposed several times. Yet, existing studies about coupled file changes ignore the feedback from developers as well as the impact of these changes on the performance of maintenance and rather these studies rely on the analysis findings and expert evaluation. Methods We investigate the usefulness of coupled file changes during perfective maintenance tasks when developers are inexperienced in programming or when they were new on the project. Using data mining on software repositories we identify files that are changed most frequently together in the past. We extract coupled file changes from the Git repository of a Java software system and join them with corresponding attributes from the versioning and issue tracking system and the project documentation. We present a controlled experiment involving 36 student participants in which we investigate if coupled file change suggestions influence the correctness of the task solutions and the required time to complete them. Results The results show that the use of coupled file change suggestions significantly increases the correctness of the solutions. However, there is only a minor effect on the time required to complete the perfective maintenance tasks. We also derived a set of the most useful attributes based on the developers’ feedback. Discussion Coupled file changes and a limited number of the proposed attributes are useful for inexperienced developers working on perfective maintenance tasks where although the developers using these suggestions solved more tasks, they still need time to understand and organize this information.

DESTINY: A Comprehensive Tool with 3D and Multi-Level Cell Memory Modeling Capability

To enable the design of large capacity memory structures, novel memory technologies such as non-volatile memory (NVM) and novel fabrication approaches, e.g., 3D stacking and multi-level cell (MLC) design have been explored. The existing modeling tools, however, cover only a few memory technologies, technology nodes and fabrication approaches. We present DESTINY, a tool for modeling 2D/3D memories designed using SRAM, resistive RAM (ReRAM), spin transfer torque RAM (STT-RAM), phase change RAM (PCM) and embedded DRAM (eDRAM) and 2D memories designed using spin orbit torque RAM (SOT-RAM), domain wall memory (DWM) and Flash memory. In addition to single-level cell (SLC) designs for all of these memories, DESTINY also supports modeling MLC designs for NVMs. We have extensively validated DESTINY against commercial and research prototypes of these memories. DESTINY is very useful for performing design-space exploration across several dimensions, such as optimizing for a target (e.g., latency, area or energy-delay product) for a given memory technology, choosing the suitable memory technology or fabrication method (i.e., 2D v/s 3D) for a given optimization target, etc. We believe that DESTINY will boost studies of next-generation memory architectures used in systems ranging from mobile devices to extreme-scale supercomputers. The latest source-code of DESTINY is available from the following git repository: https://bitbucket.org/sparshmittal/destinyv2.

The digital ‘connected’ earth: open technology for providing location-based services on degraded communication environments

ABSTRACTIn the current world, it is easy to listen that everybody and everything is connected. Over this connected world, the concept of location-based services has grown in order to provide digital services in everyplace and at every time. Nevertheless, this is not 100% true because the connection is not guaranteed for many people and in many places. These are the Degraded Communications Environments (DCE), environments where the availability of high-speed communications is not guaranteed in at least the 75% of the time. This paper works over the experience of a previous work in developing light protocols that do not need broadband for communication. This work provides an extension of these protocols for the inclusion of mobile devices as elements of the communication process and a set of libraries to allow the development of applications in DCE. The work done has involved the development of two frameworks: an Android framework that makes the incorporation of Android devices easier and a server-based framework that provides the server side for the development of the referred applications. A use case that uses these two frameworks has been developed. Finally, all technology developed is available throw a public Git repository.

Filter-Bank Artifact Rejection: High performance real-time single-channel artifact detection for EEG

Abstract Recent developments in electroencephalography (EEG) have led to a variety of consumer-grade EEG devices for brain–computer interfacing (BCI) and neurofeedback (NF) equipped with only one or a few EEG sensors. With minimal electrode coverage, most methods of detecting artifacts in the signal which arise from non-brain sources are not applicable. Furthermore, methods which can be used on single-channel EEG are typically not sufficiently accurate or fast for BCI and NF applications. In this paper a new highly accurate artifact rejection method is introduced, called Filter-Bank Artifact Rejection (FBAR), which is designed for real-time EEG applications using just a few or even a single EEG channel. FBAR is compared to a current state-of-the-art method, Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER). FBAR outperformed FASTER on all test data, due mainly to its ability to detect small artifacts in the presence of high amplitude EEG. This makes FBAR particularly useful for BCI and NF applications, which are especially dependent on achieving the highest possible signal-to-noise ratio in a real-time setting. A MATLAB toolbox allowing for use and experimentation with FBAR including several customizable options is available as a Git repository at https://bitbucket.org/kiretd/FBAR .

Making Sense Of Git In A Legal Context

The Git revision control system does not enforce correctness of data but instead is reliant on correct inputs for correct outcomes. Git records potential authorship rather than copyright ownership and t his means that an additional process layer is needed to ensure fidelity and accuracy of data. The core implication is that the “git blame” tool does not show potential authorship with enough granular ity to make clear decisions and a dditional review is required to determine potential authors of code contained in any Git repository.

A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution

With an ever-increasing volume and complexity of data collected from a variety of sources, the efficient management of geospatial information becomes a key topic in disaster risk management. For example, the representation of assets exposed to natural disasters is subjected to changes throughout the different phases of risk management reaching from pre-disaster mitigation to the response after an event and the long-term recovery of affected assets. Spatio-temporal changes need to be integrated into a sound conceptual and technological framework able to deal with data coming from different sources, at varying scales, and changing in space and time. Especially managing the information life-cycle, the integration of heterogeneous information and the distributed versioning and release of geospatial information are important topics that need to become essential parts of modern exposure modelling solutions. The main purpose of this study is to provide a conceptual and technological framework to tackle the requirements implied by disaster risk management for describing exposed assets in space and time. An information life-cycle management solution is proposed, based on a relational spatio-temporal database model coupled with Git and GeoGig repositories for distributed versioning. Two application scenarios focusing on the modelling of residential building stocks are presented to show the capabilities of the implemented solution. A prototype database model is shared on GitHub along with the necessary scenario data.

An Open Catalog for Supernova Data

Abstract We present the Open Supernova Catalog, an online collection of observations and metadata for presently 36,000+ supernovae and related candidates. The catalog is freely available on the web (https://sne.space), with its main interface having been designed to be a user-friendly, rapidly searchable table accessible on desktop and mobile devices. In addition to the primary catalog table containing supernova metadata, an individual page is generated for each supernova, which displays its available metadata, light curves, and spectra spanning X-ray to radio frequencies. The data presented in the catalog is automatically rebuilt on a daily basis and is constructed by parsing several dozen sources, including the data presented in the supernova literature and from secondary sources such as other web-based catalogs. Individual supernova data is stored in the hierarchical, human- and machine-readable JSON format, with the entirety of each supernova’s data being contained within a single JSON file bearing its name. The setup we present here, which is based on open-source software maintained via git repositories hosted on github, enables anyone to download the entirety of the supernova data set to their home computer in minutes, and to make contributions of their own data back to the catalog via git. As the supernova data set continues to grow, especially in the upcoming era of all-sky synoptic telescopes, which will increase the total number of events by orders of magnitude, we hope that the catalog we have designed will be a valuable tool for the community to analyze both historical and contemporary supernovae.

Visualizing and exploring software version control repositories using interactive tag clouds over formal concept lattices

Context: version control repositories contain a wealth of implicit information that can be used to answer many questions about a project’s development process. However, this information is not directly accessible in the repositories and must be extracted and visualized.Objective: the main objective of this work is to develop a flexible and generic interactive visualization engine called ConceptCloud that supports exploratory search in version control repositories.Method: ConceptCloud is a flexible, interactive browser for SVN and Git repositories. Its main novelty is the combination of an intuitive tag cloud visualization with an underlying concept lattice that provides a formal structure for navigation. ConceptCloud supports concurrent navigation in multiple linked but individually customizable tag clouds, which allows for multi-faceted repository browsing, and scriptable construction of unique visualizations.Results: we describe the mathematical foundations and implementation of our approach and use ConceptCloud to quickly gain insight into the team structure and development process of three projects. We perform a user study to determine the usability of ConceptCloud. We show that untrained participants are able to answer historical questions about a software project better using ConceptCloud than using a linear list of commits.Conclusion: ConceptCloud can be used to answer many difficult questions such as “What has happened in this project while I was away?” and “Which developers collaborate?”. Tag clouds generated from our approach provide a visualization in which version control data can be aggregated and explored interactively.